Blog NivelEpsilon

What it really takes to run AI workloads on AWS

A surprising number of AI platforms begin life with a question that sounds reasonable in a standup and catastrophic in a postmortem, something along the lines of “Can we just stick a GPU behind an API?” You can. You probably shouldn’t. AI workloads are not ordinary web services wearing a thicker coat. They behave differently, fail differently, scale differently, and cost differently, and an architecture that ignores those differences will eventually let you know, usually on a Sunday.

This article is not about how to train a model. It is about building an AWS architecture that can host AI workloads safely, scale them reliably, and keep the monthly bill within shouting distance of the original estimate.

Why AI workloads change the architecture conversation

Treating an AI workload as “the same thing, but with bigger instances” is a classic and very expensive mistake. Inference latency matters in milliseconds. Accelerator choice (GPU, Trainium, Inferentia) affects both performance and invoice. Traffic spikes are unpredictable because humans, not schedulers, trigger them. Model lifecycle and data lineage become first-class design concerns. Governance stops being a compliance checkbox and becomes the seatbelt that keeps sensitive information from ending up inside a prompt log.

Put differently, AI adds several new axes of failure to the usual cloud architecture, and pretending otherwise is how teams rediscover the limits of their CloudWatch alerting at 3 am.

Start with the use case, not the model

Before anyone opens the Bedrock console, the first design decision should be the business problem. A chatbot for internal knowledge, a document summarization pipeline, a fraud detection scorer, and an image generation service have almost nothing in common architecturally, even if they all happen to involve transformer models.

From the use case, derive the architectural drivers (latency budget, throughput, data sensitivity, availability target, model accuracy requirements, cost ceiling). These drivers decide almost everything else. The opposite workflow, picking the infrastructure first and then seeing what it can do, is how you end up with a beautifully optimized cluster solving a problem nobody asked about.

Choosing your AI path on AWS

AWS offers several paths, and they are not interchangeable. A rough guide.

Amazon Bedrock is the right choice when you want managed foundation models, guardrails, agents, and knowledge bases without running the model infrastructure yourself. Good for teams that want to ship features, not operate GPUs.

Amazon SageMaker AI is the right choice when you need more control over training, deployment, pipelines, and MLOps. Good for teams with ML engineers who enjoy that sort of thing. Yes, they exist.

AWS accelerator-based infrastructure (Trainium, Inferentia2, SageMaker HyperPod) is the right choice when cost efficiency or raw performance at scale becomes the dominant constraint, typically for custom training or large-scale inference.

The common mistake here is picking the most powerful option by default. Bedrock with a sensible model is usually cheaper to operate than a custom SageMaker endpoint you forgot to scale down over Christmas.

The data foundation comes first

AI systems are a thin layer of cleverness on top of data. If the data layer is broken, the AI will be confidently wrong, which is worse than being uselessly wrong because people tend to believe it.

Answer the unglamorous questions first. Where does the data live? Who owns it? How fresh does it need to be? Who can see which parts of it? For generative AI workloads that use retrieval, add more questions. How are documents chunked? What embedding model is used? Which vector store? What metadata accompanies each chunk? How is the index refreshed when the source changes?

A poor data foundation produces a poor AI experience, even when the underlying model is state of the art. Think of the model as a very articulate intern; it will faithfully report whatever you put in front of it, including the typo in the policy document from 2019.

Designing compute for reality, not for demos

Training and inference are not the same workload and should rarely share the same architecture. Training is bursty, expensive, and tolerant of scheduling. Inference is steady, latency-sensitive, and intolerant of downtime. A single “AI cluster” that tries to do both tends to be bad at each.

For inference, focus on right-sizing, dynamic scaling, and high availability across AZs. For training, focus on ephemeral capacity, checkpointing, and data pipeline throughput. For serving large models, consider whether Bedrock’s managed endpoints remove enough operational burden to justify their pricing compared to self-hosted inference on EC2 or EKS with Inferentia2.

And please, autoscale. A fixed-size fleet of GPU instances running at 3% utilization is a monument to optimism.

Treating inference as a production workload

Many AI articles spend chapters on models and a paragraph on serving them, which is roughly the opposite of how the effort is distributed in real projects. Inference is where the workload meets reality, and reality brings concurrency, timeouts, thundering herds, and users who click the retry button like they are trying to start a stubborn lawnmower.

Plan for all of it. Set timeouts. Configure throttling and quotas. Add rate limiting at the edge. Use exponential backoff. Put circuit breakers between your application tier and your AI tier so a slow model does not take the whole product down. AWS explicitly recommends rate limiting and throttling as part of protecting generative AI systems from overload, and they recommend it because they have seen what happens without it.

Protecting inference is not mainly about safety. It is about surviving the traffic spike after your launch gets a mention somewhere popular.

Separating application, AI, and data responsibilities

A quietly important architectural point is that the AI tier should not share an account, an IAM boundary, or a blast radius with the application that calls it. AWS security guidance increasingly points toward separating the application account from the generative AI account. The reasoning is simple: the consequences of a mistake in prompt construction, data retrieval, or model output are different from the consequences of a mistake in, say, a shopping cart service, and they deserve different controls.

Think of it as the organizational version of not keeping your passport in the same drawer as your house keys. If one goes missing, the other is still where it should be.

Security and guardrails from day one

AI-specific controls sit on top of the usual cloud security hygiene (IAM least privilege, encryption at rest and in transit, VPC endpoints, logging, data classification). On top of that, you need approved model catalogues so teams cannot quietly wire up any foundation model they saw on Hacker News, prompt governance with templates and input validation and logging policies that do not accidentally store sensitive data forever, output filtering for harmful content and PII leakage and jailbreak attempts, and clear data classification policies that decide which data is allowed to reach which model.

For Bedrock-based systems, Amazon Bedrock Guardrails offer configurable safeguards for harmful content and sensitive information. They are not magic, but they save a surprising amount of custom work, and custom work in this area tends to age badly.

Governance is not bureaucracy. Governance is what lets your AI feature get through a security review without being rewritten twice.

Protecting the retrieval layer when you use RAG

Retrieval-augmented generation is often described as “LLM plus documents”, which is technically true and practically misleading. A production RAG system involves ingestion pipelines, embedding generation, a vector store, metadata design, and ongoing synchronization with source systems. Each of those is a place where things can quietly go wrong.

One specific point is worth emphasizing. User identity must propagate to the retrieval layer. If Alice asks a question, the knowledge base should only return chunks Alice is allowed to see. AWS guidance recommends enforcing authorization through metadata filtering so users only get results they have access to. Without this, your RAG system will happily summarize the CFO’s compensation memo for the summer intern, which is the sort of thing that gets architectures shut down by email.

Observability goes beyond CPU and memory

Traditional observability (CPU, memory, latency, error rates) is necessary but insufficient for AI workloads. For these systems, you also want to track model quality and drift over time, retrieval quality (are the right chunks being returned?), prompt behavior and common failure modes, token usage per request and per tenant and per feature, latency per model and not just per service, and user feedback signals, with thumbs-up and thumbs-down being the cheapest useful telemetry ever invented.

Amazon Bedrock provides evaluation capabilities, and SageMaker Model Monitor covers drift and model quality in production. Use them. If you run your own inference, budget time for custom metrics, because the default dashboards will tell you the endpoint is healthy right up until users stop trusting its answers.

AI operations is not a different discipline. It is mature operations thinking applied to a stack where “the service works” and “the service is useful” are two different statements.

Cost optimization belongs in the first draft

Cost should be a design constraint, not a debugging session six weeks after launch. The biggest levers, roughly in order of impact.

Model choice. Smaller models are cheaper and often good enough. Not every feature needs the largest frontier model in the catalogue.

Inference mode. Real-time endpoints, batch inference, serverless inference, and on-demand Bedrock invocations have wildly different cost profiles. Match the mode to the traffic pattern, not the other way around.

Autoscaling policy. Scale to zero where possible. Keep the minimum capacity honest.

Hardware choice. Inferentia2 and Trainium are positioned specifically for cost-effective ML deployment, and they often deliver on that positioning.

Batching. Batching inference requests can dramatically improve throughput per dollar for workloads that tolerate small latency increases.

A common failure mode is the impressive prototype with the terrifying monthly bill. Put cost targets in the design document next to the latency targets, and revisit both before go-live.

Close with an operating model, not just a diagram

An architecture diagram is the opening paragraph of the story, not the whole book. What makes an AI platform sustainable is the operating model around it (versioning, CI/CD or MLOps/LLMOps pipelines, evaluation suites, rollback strategy, incident response, and clear ownership between platform, data, security, and application teams).

AWS guidance for enterprise-ready generative AI consistently stresses repeatable patterns and standardized approaches, because that is what turns successful experiments into durable platforms rather than fragile demos held together by one engineer’s tribal knowledge.

What separates a platform from a demo

Preparing a cloud architecture for AI on AWS is not mainly about buying GPU capacity. It is about designing a platform where data, models, security, inference, observability, and cost controls work together from the start. The teams that do well with AI are not the ones with the biggest clusters; they are the ones who took the boring parts seriously before the interesting parts broke.

If your AI architecture is running quietly, scaling predictably, and costing roughly what you expected, congratulations, you have done something genuinely difficult, and nobody will notice. That is always how it goes.

April 22, 2026 by Cloud stuff

Anatomy of an overworked Kubernetes operator called CoreDNS

Your newly spun-up frontend pod wakes up in the cluster with total amnesia. It has a job to do, but it has no idea where it is, who its neighbors are, or how to find the database it desperately needs to query. IP addresses in a Kubernetes cluster change as casually as socks in a gym locker room. Keeping track of them requires a level of bureaucratic masochism that no sane developer wants to deal with.

Someone has to manage this phonebook. That someone lives in a windowless sub-basement namespace called kube-system. It sits there, answering the same questions thousands of times a second, routing traffic, and receiving absolutely zero credit for any of it until something breaks.

That entity is CoreDNS. And this is an exploration of the thankless, absurd, and occasionally heroic life it leads inside your infrastructure.

The temperamental filing cabinet that came before

Before CoreDNS was given the keys to the kingdom in Kubernetes 1.13, the job belonged to kube-dns. It technically worked, much like a rusty fax machine transmits documents. But nobody was happy to see it. kube-dns was not a single, elegant program. It was a chaotic trench coat containing three different containers stacked on top of each other, all whispering to each other to resolve a single address.

CoreDNS replaced it because it is written in Go as a single, compiled binary. It is lighter, faster, and built around a modular plugin architecture. You can bolt on new behaviors to it, much like adding attachments to a vacuum cleaner. It is efficient, utterly devoid of joy, and built to survive the hostile environment of a modern microservices architecture.

Inside the passport control of resolv.conf

When a pod is born, the container runtime shoves a folded piece of paper into its pocket. This piece of paper is the /etc/resolv.conf file. It is the internal passport and instruction manual for how the pod should talk to the outside world.

If you were to exec into a standard web application pod and look at that slip of paper, you would see something resembling this:

search default.svc.cluster.local svc.cluster.local cluster.local
nameserver 10.96.0.10
options ndots:5

At first glance, it looks harmless. The nameserver line simply tells the pod exactly where the CoreDNS operator is sitting. But the rest of this file is a recipe for a spectacular amount of wasted effort.

Look at the search directive. This is a list of default neighborhoods. In human terms, if you tell a courier to deliver a package to “Smith”, the courier does not just give up. The courier checks “Smith in the default namespace”, then “Smith in the general services area”, then “Smith in the local cluster”. It is a highly structured, incredibly repetitive guessing game. Every time your application tries to look up a short name like “inventory-api”, CoreDNS has to physically flip through these directories until it finds a match.

But the true villain of this document, the source of immense invisible suffering, is located at the very bottom.

The loud waiter and the tragedy of ndots

Let us talk about options ndots:5. This single line of configuration is responsible for more wasted network traffic than a teenager downloading 4K video over cellular data.

The ndots value tells your pod how many dots a domain name must have before it is considered an absolute, fully qualified domain name. If a domain has fewer than five dots, the pod assumes it is a local nickname and starts appending the search domains to it.

Imagine a waiter in a crowded, high-end restaurant. You ask this waiter to bring a message to a guest named “google.com”.

Because “google.com” only has one dot, the waiter refuses to believe this is the person’s full legal name. Instead of looking at the master reservation book, the waiter walks into the center of the dining room and screams at the top of his lungs, “IS THERE A GOOGLE.COM IN THE DEFAULT.SVC.CLUSTER.LOCAL NAMESPACE?”

The room goes dead silent. Nobody answers.

Undeterred, the waiter moves to the next search domain and screams, “IS THERE A GOOGLE.COM IN THE SVC.CLUSTER.LOCAL NAMESPACE?”

Again, nothing. The waiter does this a third time for cluster.local. Finally, sweating and out of breath, having annoyed every single patron in the establishment, the waiter says, “Fine. Let me check the outside world for just plain google.com.”

This happens for every single external API call your application makes. Three useless, doomed-to-fail DNS queries hit CoreDNS before your pod even attempts the correct external address. CoreDNS processes these garbage queries with the dead-eyed stare of a DMV clerk stamping “DENIED” on improperly filled forms. If you ever wonder why your cluster DNS latency is slightly terrible, the screaming waiter is usually to blame.

The Corefile and other documents of self-inflicted pain

The rules governing CoreDNS are dictated by a configuration file known as the Corefile. It is essentially the Human Resources policy manual for the DNS server. It defines which plugins are active, who is allowed to ask questions, and where to forward queries it does not understand.

A simplified corporate policy might look like this:

.:53 {
    errors
    health
    kubernetes cluster.local in-addr.arpa ip6.arpa {
       pods insecure
       fallthrough in-addr.arpa ip6.arpa
    }
    prometheus :9153
    forward . /etc/resolv.conf
    cache 30
    loop
    reload
}

Most of this is standard bureaucratic routing. The Kubernetes plugin tells CoreDNS how to talk to the Kubernetes API to find out where pods actually live. The cache plugin allows CoreDNS to memorize answers for 30 seconds, so it does not have to bother the API constantly.

But the most fascinating part of this document is the loop plugin.

In complex networks, it is very easy to accidentally configure DNS servers to point at each other. Server A asks Server B, and Server B asks Server A. In a normal corporate environment, two middle managers delegating the same task back and forth will do so indefinitely, drawing salaries for years until retirement.

Software does not have a retirement plan. Left unchecked, a DNS forwarding loop will exponentially consume memory and CPU until the entire node catches fire and dies.

The loop plugin exists solely to detect this exact scenario. It sends a uniquely tagged query out into the world. If that same query comes back to it, CoreDNS realizes it is trapped in a futile, infinite cycle of middle-management delegation.

And what does it do? It refuses to participate. It halts. CoreDNS will intentionally shut itself down rather than perpetuate a stupid system. There is a profound life lesson hiding in that logic. It shows a level of self-awareness and boundary-setting that most human workers never achieve.

Headless services or giving out direct phone numbers

Most of the time, when you ask CoreDNS for a service, it gives you the IP address of a load balancer. You call the front desk, and the receptionist routes your call to an available agent. You do not know who you are talking to, and you do not care.

But some applications are needy. Databases in a cluster, like a Cassandra ring or a MongoDB replica set, cannot just talk to a random receptionist. They need to replicate data. They need to know exactly who they are talking to. They need direct home phone numbers.

This is where CoreDNS provides a feature known as a “headless service”.

When you create a service in Kubernetes, it usually looks like a standard networking request. But when you explicitly add one specific line to the YAML definition, you are effectively firing the receptionist:

apiVersion: v1
kind: Service
metadata:
  name: inventory-db
spec:
  clusterIP: None # <-- The receptionist's termination letter
  selector:
    app: database

By setting “clusterIP: None”, you are telling CoreDNS that this department has no front desk.

Now, when a pod asks for “inventory-db”, CoreDNS does not hand out a single routing IP. Instead, it dumps a raw list of the individual IP addresses of every single pod backing that service. Furthermore, it creates a custom, highly specific DNS entry for every individual pod in the StatefulSet.

It assigns them names like “pod-0.inventory-db.production.svc.cluster.local”.

Suddenly, your database node has a personal identity. It can be addressed directly. It is a minor administrative miracle, allowing complex, stateful applications to map out their own internal corporate structure without relying on the cluster’s default routing mechanics.

The unsung hero of the sub-basement

CoreDNS is rarely the subject of glowing keynotes at tech conferences. It does not have the flashy appeal of a service mesh, nor does it generate the architectural excitement of an advanced deployment strategy. It is plumbing. It is bureaucracy. It is the guy in the basement checking names off a clipboard.

But the next time you type a simple, human-readable name into an application configuration file and it flawlessly connects to a database across the cluster, think of CoreDNS. Think of the thousands of fake ndots queries it cheerfully absorbed. Think of its rigid adherence to the Corefile.

And most importantly, respect a piece of software that is smart enough to know when it is stuck in a loop, and brave enough to quit on the spot.

April 12, 2026 by DevOps stuff Kubernetes SRE stuff

Stop killing your pods just to give them more RAM

If your pants feel a little tight after a large Thanksgiving meal, the logical solution is to discreetly unbutton them. You do not typically hire a hitman to assassinate yourself, clone your DNA in a vat, and grow a slightly wider version of yourself just to digest dessert. Yet, for nearly a decade, this is exactly how Kubernetes handled resource management.

When an application slowly consumed all its allocated memory, the standard orchestration response was absolute, violent destruction. We did not give the application more memory. We shot it in the head, deleted its entire existence, and span up a brand new replica with a slightly larger plate. Connections dropped. Warm caches evaporated. State was lost in the digital wind.

Thankfully, the era of the Kubernetes firing squad is drawing to a close. In-place pod resizing has officially graduated to stable General Availability in Kubernetes v1.35, and it changes the fundamental physics of how we manage workloads. We can finally stop burning down the house just to buy a bigger sofa.

Let us explore how this works, why it is practically miraculous, and how to use it without accidentally angering the Linux kernel.

The historical absurdity of pod resource management

Before in-place resizing, if you wanted to change the CPU or memory allocated to a running pod, the workflow was brutally simplistic. You updated your Deployment specification with the new resource requests and limits. The Kubernetes control plane saw the discrepancy between the desired state and the current state. The control plane then instructed the Kubelet to terminate the old pod and create a new one.

Think of taking your car to the mechanic because you need thicker tires. Instead of swapping the tires, the mechanic puts your car into an industrial crusher, hands you an identical car with thicker tires, and tells you to reprogram your radio stations. Sure, the rolling update strategy ensured you had a backup car to drive while the primary one was being crushed, but the specific pod doing the heavy lifting was gone.

For stateless microservices written in Go, this was barely an inconvenience. For a massive Java Virtual Machine holding gigabytes of cached data, or a machine learning inference service handling a traffic spike, a restart was a traumatic event. It meant minutes of downtime, CPU spikes during startup, and a slew of unhappy alerts.

Enter the era of in-place resizing

The magic of Kubernetes v1.35 is that the “resources.requests” and “resources.limits” fields within a pod specification are no longer immutable. You can edit them on the fly.

Under the hood, Kubernetes is finally taking full advantage of Linux control groups (cgroups). A container is basically just a regular Linux process trapped in a highly restrictive administrative box. The kernel has always had the ability to move the walls of this box without killing the process inside. If a container needs more memory, the kernel simply adjusts the cgroup memory limit. It is like a landlord quietly sliding a partition wall outward while you are still sleeping in your bed.

Here is a sanitized example of how you might update a running pod. Notice how we are just applying a patch to an existing, actively running resource.

# We patch the running pod to increase memory limits
# No restarts, no dropped connections, just instant gratification
kubectl patch pod bloated-legacy-api-7b89f5c -p '{"spec":{"containers":[{"name":"main-app","resources":{"limits":{"memory":"4Gi"}}}]}}'

It feels almost illegal the first time you do it. The pod keeps running, the uptime counter keeps ticking, but suddenly the application has breathing room.

The bureaucratic lifecycle of asking for more RAM

Of course, you cannot just demand more resources and expect the universe to instantly comply. The physical node hosting your pod must actually have the spare CPU or memory available. To manage this negotiation, Kubernetes introduces a new field in the pod status called resize.

This field tracks the bureaucratic process of your resource request. It is very much like dealing with the Department of Motor Vehicles, but measured in milliseconds. The statuses are surprisingly descriptive.

First, there is Proposed. This means your request for more resources has been acknowledged. The paperwork is on the desk, but nobody has stamped it yet.

Second, we have “InProgress”. The Kubelet has accepted the request and is currently asking the container runtime (like containerd or CRI-O) to adjust the cgroup limits. The walls are physically moving.

Third, you might see Deferred. This is the orchestrator politely telling you that the node is currently full. Your pod is on a waitlist. As soon as another pod terminates or frees up space, your resize request will be processed. You do not get an error, but you also do not get your RAM. You just wait.

Finally, there is Infeasible. This is Kubernetes looking at you with deep, profound disappointment. You probably asked for 64 Gigabytes of memory on a tiny virtual machine that only has 8 Gigabytes total. The API server essentially stamps your form with a big red “DENIED” and moves on with its life.

Negotiating with stubborn runtimes using resize policies

Not all applications are smart enough to realize they have been gifted more resources. Node.js or Go applications will happily consume newly available CPU cycles without being told. Java applications, on the other hand, are like stubborn mules. If you start a JVM with a maximum heap size of 2 Gigabytes, giving the container 4 Gigabytes of memory will accomplish absolutely nothing. The JVM will stubbornly refuse to look at the new memory until you reboot it.

To handle these varying levels of application intelligence, Kubernetes gives us the resizePolicy array. This allows you to define exactly how the Kubelet should handle changes for each specific resource type.

apiVersion: v1
kind: Pod
metadata:
  name: stubborn-java-beast
spec:
  containers:
  - name: heavy-calculator
    image: corporate-repo/calculator:v4
    resizePolicy:
    - resourceName: cpu
      restartPolicy: RestartNotRequired
    - resourceName: memory
      restartPolicy: RestartContainer
    resources:
      requests:
        memory: "2Gi"
        cpu: "1"
      limits:
        memory: "4Gi"
        cpu: "2"

In this configuration, we are telling Kubernetes a very specific set of rules. If we patch the CPU limits, the Kubelet will adjust the cgroups and leave the container alone (RestartNotRequired). The application will just run faster. However, if we patch the memory limits, the Kubelet knows it must restart the container (RestartContainer) so the application can read the new environment variables and adjust its internal memory management.

The vertical pod autoscaler is your marital therapist

Manually patching pods in the middle of the night is still a terrible way to manage infrastructure. The true power of in-place resizing is unlocked when you pair it with the Vertical Pod Autoscaler.

Historically, the VPA was a bit of a brute. It would watch your pods, realize they needed more memory, and then mercilessly murder them to apply the new sizes. It was effective, but highly destructive.

Now, the VPA features a magical mode called InPlaceOrRecreate. Think of this mode as a highly skilled couples therapist. It sits quietly in the background, observing the relationship between your application and its memory usage. When the application needs more space to grow, the VPA simply slides the walls outward without causing a scene. It only resorts to the nuclear option of recreating the pod if an in-place resize is technically impossible.

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: smooth-scaling-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: frontend-service
  updatePolicy:
    # The magic word that prevents the firing squad
    updateMode: "InPlaceOrRecreate"

With this single setting, your stateful workloads, long-running batch jobs, and memory-hungry APIs can seamlessly adapt to traffic spikes without ever dropping a user request.

The fine print and the rottweiler problem

As with all things in distributed systems, there are rules. You cannot cheat the laws of physics. If a node is completely exhausted of resources, your in-place resize will sit in the Deferred state forever. You still need the Cluster Autoscaler to add fresh physical nodes to your cluster. In-place resizing is a tool for distributing existing wealth, not for printing new money.

Furthermore, we must talk about the danger of scaling down. Increasing memory is a joyous occasion. Reducing memory limits on a running container is like trying to take a juicy steak out of the mouth of a hungry Rottweiler.

The Linux kernel takes memory limits very seriously. If your application is currently using 3 Gigabytes of memory, and you smugly patch the pod to limit it to 2 Gigabytes, the kernel does not politely ask the application to clean up its garbage. The Out Of Memory killer wakes up, grabs an axe, and immediately slaughters your application.

Always scale down memory with extreme caution. It is almost always safer to wait for a natural deployment cycle to reduce memory requests than to try to forcefully shrink a running process.

In the end, in-place pod resizing is not just a neat party trick. It is a fundamental maturation of Kubernetes as an operating system for the cloud. We are no longer treating our workloads like disposable cattle to be slaughtered at the first sign of trouble. We are treating them like slightly demanding house pets. Just give them the bigger bowl of food and let them sleep.

April 5, 2026 by DevOps stuff Kubernetes SRE stuff

The upskilling industry is selling you expired AI anxiety

Last week, someone in your LinkedIn feed posted about being thrilled to build AI agents over the weekend. The post had forty-seven likes, a handful of rocket emojis, and several comments praising their growth mindset. You stared at the screen and felt a familiar, dull panic in your gut. It was not inspiration. It was the exact same feeling you get when you watch someone pretend to genuinely enjoy a room-temperature kale and gravel smoothie.

Nobody with a healthy central nervous system is genuinely thrilled to learn prompt engineering frameworks on a Saturday morning. They are just terrified of what happens to their mortgage if they do not.

You probably have your own personal monument to this anxiety. It is a browser tab you keep meaning to open. A course you bought during a Black Friday panic sale and never started. A corporate Slack thread about AI readiness that you skimmed, starred, and immediately buried under a pile of actual work. It is the quiet admission that you do not know enough to stay relevant, paired with the even quieter admission that simply bookmarking the resource made you feel slightly less like a dinosaur.

You have been writing production code, configuring infrastructure, and surviving catastrophic deployment rollbacks for years. By most reasonable measures, you know exactly what you are doing. And yet, that browser tab sits there. It is a digital talisman against obsolescence.

There is a name for this modern condition. I call it competence debt. It is the silent, creeping rot that happens when you trade durable mastery for perishable certifications. And an entire multi-billion-dollar upskilling industry is banking on you never figuring out the difference.

The ecosystem of the forgotten browser tab

That Udemy or Coursera tab has been open in your browser for so long that it has practically developed its own microbial ecosystem. It sits there, glowing faintly between Jira and Slack, judging you with the silent, suffocating disappointment of a stationary bicycle that you now use exclusively for drying wet dress shirts.

You will click it eventually. You will watch the first module at 1.5x speed. Not because the course will teach you something deeply structural about computer science. Not because it will make you meaningfully better at the architectural work that actually keeps your company afloat. You will do it because the credential economy demands constant proof of currency, and currency is exactly what expires.

Buying a deeply discounted course on the latest Large Language Model API is not the acquisition of knowledge. It is the purchase of a psychological suppository for imposter syndrome. You administer it, you feel a warm rush of proactive professional development for exactly twelve minutes, and for the rest of the quarter, the only thing you actually retain is a PDF certificate and a vague, persistent sense of guilt.

This is the business model. The upskilling industry operates exactly like a budget gym in January. They do not want you to use the equipment. If everyone who bought a tech course actually logged in, the servers would melt. The industry relies on the fact that an astonishing ninety percent of Massive Open Online Courses are never completed. They are selling you the sensation of having done something about your career anxiety without the caloric expenditure of actually doing it.

Selling suppositories for imposter syndrome

The pressure does not just come from the manic performance art of LinkedIn. It comes from inside the house.

One morning, you get an email from HR about a new corporate AI readiness initiative. The phrasing strongly suggests that participation is voluntary. Of course it is. It is voluntary in the same way that handing over your wallet to a nervous man holding a broken bottle in a dark alley is voluntary. You do not have to do it, but the alternative involves a lot of messy paperwork and a sudden career transition.

Companies love these initiatives because they are trackable. You can put a dashboard on a PowerPoint slide and show the board of directors that eighty percent of the engineering department has been upskilled.

But Gartner research shows that nearly half of all corporate training is what they elegantly call scrap learning. This is knowledge that is delivered but never actually applied to the job. It is corporate junk food. You spend three hours learning how to write the perfect prompt for a proprietary AI tool, and by the time your performance review rolls around, the tool has been deprecated, the vendor has pivoted to a different business model, and you are still just trying to figure out why the production database is locking up every Tuesday at 3 PM.

Early in your career, you learned a new technology because it was genuinely exciting. It provided a new mental model for building things. You stayed up late reading documentation, not because a middle manager sent you a calendar invite, but because you could not stop thinking about the possibilities. The learning felt like building an extension onto a house you were just beginning to inhabit.

Now, you open an AI course because your company panicked after reading a Forbes article. The curiosity has been entirely surgically removed, replaced by the grim mechanics of survival.

The shelf life of a prompt engineer

Here is the fundamental trick the training industry plays on us. They conflate perishable knowledge with durable skill.

Perishable knowledge has the shelf life of an unrefrigerated avocado. It is the exact syntax for a specific API that will change completely in version two. It is a list of magic words to trick a specific chatbot into ignoring its safety constraints. It is knowing how to navigate the user interface of a cloud vendor dashboard that is scheduled for a total redesign next month.

Durable skill is entirely different. A durable skill is understanding how relational databases handle concurrency. It is the ability to read a latency graph like a seasoned cardiologist reads an electrocardiogram, instantly spotting the flutter of a failing network switch. It is knowing how to design a system that fails gracefully instead of taking the entire company down with it. It is the agonizing, hard-won intuition of knowing when an external vendor is lying to you about their uptime guarantees.

Durable skills do not look good on a digital badge. You cannot take a weekend course on how to develop a gut feeling about a poorly designed architecture. It takes years of getting burned by bad code, surviving late-night outages, and staring at logs until your eyes bleed.

The tragedy of the current AI hype cycle is that it forces brilliant engineers to abandon their compounding, durable skills to chase perishable trivia. It is like telling a master carpenter to drop his tools and spend three months learning how to optimize the instruction manual for an automated nail gun.

Compounding interest in the wrong direction

This brings us back to competence debt.

Every hour you spend forcing yourself to memorize the transient, undocumented quirks of an AI wrapper is an hour you did not spend deeply understanding the legacy systems you are actually paid to keep alive. Every superficial certificate you collect is a minimum payment on a debt of fundamental knowledge that keeps growing in the background.

You look productive. Your corporate training dashboard is completely green. Your profile is heavily peppered with the right buzzwords. But underneath it all, the foundational skills that would actually make you irreplaceable are quietly rusting from neglect.

The industry has taught us to call this frantic hamster wheel growth. The corporate rubrics and performance metrics were meticulously designed to measure it. But the word we are all actually looking for is depreciation.

It is perfectly fine to ignore that browser tab. Let the microbial ecosystem thrive. Close the tab. Close the guilt. The next time you feel the panic rising when someone posts about their weekend AI project, take a deep breath. Remember that the ability to keep a messy, chaotic, real-world system running is a skill that no weekend bootcamp can teach.

Stop buying their expired anxiety, and go back to doing the real work.

March 31, 2026 by Computer Science stuff

They left AWS to save money. Coming back cost even more

Not long ago, a partner I work with told me about a company that decided it had finally had enough of AWS.

The monthly bill had become the sort of document people opened with the facial expression usually reserved for dental estimates. Consultants were invited in. Spreadsheets were produced. Serious people said serious things about control, efficiency, and the wisdom of getting off the cloud treadmill.

The conclusion sounded almost virtuous. Leave AWS, move the workloads to a colocation facility, buy the hardware, and stop renting what could surely be owned more cheaply.

It was neat. It was rational. It was, for a while, deeply satisfying.

And then reality arrived, carrying invoices.

The company spent a substantial sum getting out of AWS. Servers were bought. Contracts were signed. Staff had to be hired to manage all the things cloud providers manage quietly in the background while everyone else gets on with their jobs. Not long after, the economics began to fray. Reversing course costs even more than leaving in the first place.

That is the part worth paying attention to.

Not because it makes for a dramatic story, though it does. Not because it is especially rare, but because it is not. It matters because it exposes one of the oldest tricks in infrastructure decision-making. Companies compare a visible bill with an invisible burden, decide the bill is the scandal, and only later discover that the burden was doing quite a lot of useful work.

The spreadsheet seduction

On paper, the move away from AWS looked wonderfully sensible.

The cloud bill was obvious, monthly, and impolite enough to keep turning up. On-premises looked calmer. Hardware could be amortized. Rack space, power, and bandwidth could be priced. With a bit of care, the whole thing could be made to resemble prudence.

This is where many repatriation plans become dangerously persuasive. The cloud is cast as an extravagant landlord. On-premises is presented as the mature decision to stop renting and finally buy the house.

Unfortunately, a data center is not a house. It is closer to owning a very large hotel whose plumbing, wiring, keys, security, fire precautions, laundry, and unexpected midnight incidents are all your responsibility, except the guests are servers and none of them leave a tip.

The spreadsheet had done a decent job of pricing the obvious things. Hardware. Colocation space. Power. Connectivity.

What was priced badly were all the dull, expensive capabilities that public cloud tends to bundle into the bill. Managed failover. Backup automation. Key rotation. Elastic capacity. Security controls. Compliance support. Monitoring that does not depend on a specific engineer being awake, available, and emotionally prepared.

What looked like cloud excess turned out to include a great deal of cloud competence.

That distinction matters.

A large cloud bill is easy to resent because it is visible. Operational competence is harder to resent because it tends to be hidden in the walls.

What the cloud had been doing all along

One of the costliest mistakes in infrastructure is confusing convenience with fluff.

A managed database can look expensive right up to the moment you have to build and test failover yourself, define recovery objectives, handle maintenance windows, rotate credentials, validate backups, and explain to auditors why one awkward part of the process still depends on a human remembering to do something after lunch.

A content delivery network may seem like a luxury until you try to reproduce low-latency delivery, edge caching, certificate handling, resilience, and attack mitigation with a mixture of hardware, internal effort, procurement delays, and hope.

The company, in this case, had not really been paying AWS only for compute and storage. It had been paying AWS to absorb a long list of repetitive operational chores, specialized platform decisions, and uncomfortable edge cases.

Once those chores came back in-house, they did not return politely.

Redundancy stopped being a feature and became a budget line, followed by an implementation plan, followed by a maintenance burden. Security controls that had once been inherited now had to be selected, deployed, documented, checked, and defended. Compliance work that had once been partly automated became a steady stream of evidence gathering, procedural discipline, and administrative repetition.

Cloud bills can look high. So can plumbing. You only discover its emotional value when it stops working.

The talent tax

The easiest part of moving on premises is buying equipment.

The harder part is finding enough people who know how to run the surrounding world properly.

Cloud expertise is now common enough that many companies can hire engineers comfortable with infrastructure as code, IAM, managed services, container platforms, observability, autoscaling, and cost controls. Strong cloud engineers are not cheap, but they are at least visible in the market.

Deep on-premises expertise is another matter. People who are strong in storage, backup infrastructure, virtualization, physical networking, hardware lifecycle, and operational recovery still exist, but they are not standing about in large numbers waiting to be discovered. They are experienced, expensive, and often well aware of their market value.

There is also a cultural issue that rarely appears in repatriation slide decks. A great many engineers would rather write Terraform than troubleshoot a hardware issue under unflattering lighting at two in the morning. This is not a moral failure. It is simple market gravity. The industry has spent years abstracting away routine infrastructure pain because abstraction is usually a better use of skilled human attention.

The partner who told me this story was particularly clear on this point. The staffing line looked manageable in planning. In practice, it turned into one of the most stubborn and underestimated parts of the whole effort.

Cloud is not cheap because expertise is cheap. Cloud is often cheaper because rebuilding enough expertise inside one company is very expensive.

Why does utilization lie so beautifully

Projected utilization is one of those numbers that becomes more charming the less time it spends near reality.

Many repatriation models assume that servers will be well used, capacity will be planned sensibly, and waste will be modest. It sounds disciplined. Responsible, even.

Real workloads behave less like equations and more like kitchens during a family gathering. There are quiet periods, sudden rushes, abandoned experiments, quarter-end panics, new projects that arrive with urgency and no warning, and services no one remembers until they break.

Elasticity is not a decorative feature added by cloud providers to justify themselves. It is one of the main ways organizations avoid buying for peak demand and then spending the rest of the year paying for machinery to sit about waiting.

Without elasticity, you provision for the busiest day and fund the silence in between.

Silence, in infrastructure, is expensive.

A half-used on-premises platform still consumes power, occupies space, demands maintenance, requires patching, and waits patiently for a workload spike that visits only now and then. Spare capacity has excellent manners. It makes no fuss. It simply eats money quietly and on schedule.

This was one of the turning points in the story I heard. Forecast utilization turned out to be far more flattering than actual utilization. Once that happened, the economics began to sag under their own good intentions.

The cost of becoming slower

Traditional total-cost comparisons handle direct spending reasonably well. They are much worse at pricing lost momentum.

When a company runs on a large cloud platform, it does not merely rent infrastructure. It also gains access to a constant flow of improvements and options. Better analytics tools. New security integrations. Managed AI services. Identity features. Database capabilities. Deployment patterns. Networking enhancements. Observability tooling.

No single addition changes everything overnight. The effect is cumulative. It is a thousand small conveniences arriving over time and sparing teams from having to rebuild ordinary civilization every quarter.

An on-premises platform can be stable and well run. For the right workloads, that may be perfectly acceptable. But it does not evolve at the pace of a hyperscaler. Upgrades become projects. New capabilities require procurement, testing, staffing, and patience. The platform becomes more careful and, usually, slower.

That slower pace does not always show up neatly in a spreadsheet, but engineers feel it almost immediately.

While competitors are experimenting with new managed services or shipping new capabilities faster, the repatriated organization may be spending its time improving backup procedures, standardizing tools, negotiating maintenance arrangements, or replacing hardware that has chosen an inconvenient moment to become philosophical.

There is nothing glamorous about that. There is also nothing free about it.

Who should actually consider on-premises

None of this means on-premises is foolish.

That would be a lazy conclusion, and lazy conclusions are where expensive architecture plans begin.

For some organizations, on-premises remains entirely reasonable. It makes sense for highly predictable workloads with very little variability. It can make sense in tightly regulated environments where legal, sovereignty, or operational constraints sharply limit the use of public cloud. And at a very large scale, some organizations genuinely can justify building substantial parts of their own platform.

But most companies tempted by repatriation are not in that category.

They are not hyperscalers. They are not all running flat, perfectly predictable workloads. They are not all boxed in by constraints that make public cloud impossible. More often, they are reacting to a painful cloud bill caused by weak cost governance, poor workload fit, loose architecture discipline, or a lack of serious FinOps.

That is a very different problem.

Leaving AWS because you are using AWS badly is a bit like selling your refrigerator because the groceries keep going off while the door is open. The appliance may not be the heart of the matter.

The middle ground companies skip past

One of the stranger features of cloud debates is how quickly they become binary.

Either remain in public cloud forever, or march solemnly back to racks and cages as if returning to a lost ancestral craft.

There is, of course, a middle ground.

Some workloads do benefit from local placement because of latency, residency, plant integration, or operational constraints. But needing hardware closer to the ground does not automatically mean rebuilding the entire service model from scratch. The more useful question is often not whether the hardware should be local, but whether the control plane, automation model, and day-to-day operations should still feel cloud-like.

That is a much more practical conversation.

A company may need some infrastructure nearby while still gaining enormous value from managed identity, familiar APIs, consistent automation, and operational patterns learned in the cloud. This tends to sound less heroic than a full repatriation story, but heroism is not a particularly reliable basis for infrastructure strategy.

The partner who described this case said as much. If they had explored the middle road earlier, they might have kept the local advantages they wanted without assuming quite so much of the surrounding operational burden.

What a real repatriation audit should include

Any company seriously considering a move off AWS should pause long enough to perform an audit that is a little less enchanted by ownership.

Start with the full cloud picture, not just the line items everyone enjoys complaining about. Include engineering effort, compliance automation, security services, platform speed, operational overhead, and the cost of scaling quickly when demand changes.

Then build the on-premises model with uncommon honesty. Price round-the-clock operations. Price redundancy properly. Price backup and recovery as if they matter, because they do. Price refresh cycles, maintenance contracts, spare capacity, patching, testing, physical security, audit evidence, and the awkward certainty that hardware fails when it is least convenient.

Then ask a cultural question, not just a financial one. How many of your engineers actually want to spend more of their time dealing with the physical stack and the operational plumbing that comes with it?

That answer matters more than many executives would like.

A strategy that looks cheaper on paper but nudges your best engineers toward the door is not, in any meaningful sense, cheaper.

Finally, compare repatriation not only against your current cloud bill, but against what a disciplined cloud optimization program could achieve. Rightsizing, storage improvements, better instance strategy, autoscaling discipline, reserved capacity planning, architecture cleanup, and proper FinOps can all change the economics without requiring anyone to rediscover the intimate emotional texture of broken hardware.

The bill behind the bill

What has stayed with me about this story is that it was never really a story about AWS.

It was a story about accounting for the wrong thing.

The visible bill was treated as the entire problem. The hidden work behind the bill was treated as background scenery. Once the company moved off AWS, the scenery walked to the front of the stage and began sending invoices.

That is the trap.

Cloud can absolutely be expensive. Plenty of organizations run it badly and pay for the privilege. But on-premises is not automatically the sober adult in the room. Quite often, it is simply a different payment model, one that hides more of the cost in staffing, slower delivery, operational fragility, maintenance overhead, and all the unlovely little chores that cloud platforms had been taking care of out of sight.

The lesson from this case was not that every workload belongs in AWS forever. It was that infrastructure decisions become dangerous when they are made in reaction to irritation rather than in response to a full economic picture.

Leaving the cloud may still be the right answer for some organizations. For many others, the more useful answer is much less theatrical. Use the cloud better. Govern it better. Design it properly. Understand what you are paying for before deciding you would prefer to rebuild it yourself.

A large monthly cloud bill can be offensive to look at.

The bill that arrives after a bad attempt to escape it is usually less offensive than heartbreaking.

And heartbreak, unlike EC2, rarely comes with autoscaling.

March 30, 2026 by Cloud stuff Computer Science stuff

Why Crossplane is the Kubernetes therapy your multi-cloud setup needs

Let us be perfectly honest about multi-cloud environments. They are not a harmonious symphony of computing power. They are a logistical nightmare, roughly equivalent to hosting a dinner party where one guest only eats raw vegan food, another demands a deep-fried turkey, and the third will only consume blue candy. You are running around three different kitchens trying to keep everyone alive and happy while speaking three different languages.

For years, we relied on Terraform or its open-source sibling OpenTofu to manage this chaos. These tools are fantastic, but they come with a terrifying piece of baggage known as the state file. The state file is essentially a fragile, highly sensitive diary holding the deepest, darkest secrets of your infrastructure. If that file gets corrupted or someone forgets to lock it, your cloud provider develops sudden amnesia and forgets where it put the database.

Kubernetes evolved quite a bit while we were busy babysitting our state files. It stopped being just a container orchestrator and started trying to run the whole house. Every major cloud provider released their own Kubernetes operator. Suddenly, you could manage a storage bucket or a database directly from inside your cluster. But there was a catch. The operators refused to speak to each other. You essentially hired a team of brilliant specialists who absolutely hate each other.

This is exactly where Crossplane steps in to act as the universal, unbothered therapist for your infrastructure.

Meet your new obsessive infrastructure butler

Crossplane does not care about vendor rivalries. It installs itself into your Kubernetes cluster and uses the native Kubernetes reconciliation loop to manage your external cloud resources.

If you are unfamiliar with the reconciliation loop, think of it as an aggressively helpful, obsessive-compulsive butler. You hand this butler a piece of YAML paper stating that you require a specific storage bucket in a specific region. The butler goes out, builds the bucket, and then stands there staring at it forever. If a rogue developer logs into the cloud console and manually deletes that bucket, the butler simply builds it again before the developer has even finished their morning coffee. It is relentless, slightly unnerving, and exactly what you want to keep your infrastructure in check.

Because Crossplane lives inside Kubernetes, you do not need to run a separate pipeline just to execute an infrastructure plan. The cluster itself is the engine. You declare what you want, and the cluster makes reality match your desires.

The anatomy of a multi-cloud combo meal

To understand how this actually works without getting bogged down in endless documentation, you only need to understand three main concepts.

First, you have Providers. These are the translator modules. You install the AWS Provider, the Azure Provider, or the Google Cloud Provider, and suddenly your Kubernetes cluster knows how to speak their specific dialects.

Next, you have Managed Resources. These are the raw ingredients. A single virtual machine, a single virtual network, or a single database instance. You can deploy these directly, but asking a developer to configure twenty different Managed Resources just to get a working application is like handing them a live chicken, a sack of flour, and telling them to make a sandwich.

This brings us to the real magic of Crossplane, which is the Composite Resource.

Composite Resources allow you to bundle all those raw ingredients into a single, easy-to-digest package. It is the infrastructure equivalent of a fast-food drive-through. A developer does not need to know about subnets, security groups, or routing tables. They just submitted a claim for a “Standard Web Database” value meal. Crossplane takes that simple request and translates it into the complex web of resources required behind the scenes.

Looking at the code without falling asleep

To prove that this is not just theoretical nonsense, let us look at what it takes to command two completely different cloud providers from the exact same place.

Normally, doing this requires switching between different tools, authenticating multiple times, and praying you do not execute the wrong command in the wrong terminal. With Crossplane, you just throw your YAML files into the cluster.

Here is a sanitized, totally harmless example of how you might ask AWS for a storage bucket.

apiVersion: s3.aws.upbound.io/v1beta1
kind: Bucket
metadata:
  name: acme-corp-financial-reports
spec:
  forProvider:
    region: eu-west-1
  providerConfigRef:
    name: aws-default-provider

And right next to it, in the exact same directory, you can drop this snippet to demand a Resource Group from Azure.

apiVersion: azure.upbound.io/v1beta1
kind: ResourceGroup
metadata:
  name: rg-marketing-dev-01
spec:
  forProvider:
    location: West Europe
  providerConfigRef:
    name: azure-default-provider

You apply these manifests, and Crossplane handles the authentication, the API calls, and the aggressive babysitting of the resources. No Terraform state file is required. It is completely stateless GitOps magic.

The ugly truth about operating at scale

Of course, getting rid of the state file is like going to a music festival without a cell phone. It sounds incredibly liberating until you lose your friends and cannot find your way home.

Operating Crossplane at scale is not always a walk in the park. When things go wrong during provisioning, and they absolutely will go wrong, you do not get a neatly formatted error summary. Because there is no central state file to reference, finding out why a resource failed requires interrogating the Kubernetes API directly.

You type a command to check the status of your resources, and the cluster vomits a massive wall of text onto your screen. It is like trying to find a typo in a phone book while someone shouts at you in a foreign language. Running multiple kubectl commands just to figure out why an Azure database refused to spin up gets very old, very fast.

To survive this chaos, you cannot rely on manual terminal commands. You must pair Crossplane with a dedicated GitOps tool like ArgoCD or FluxCD.

These tools act as the adult in the room. They keep track of what was actually deployed, provide a visual dashboard, and translate the cluster’s internal panic into something a human being can actually read. They give you the visibility that Crossplane lacks out of the box.

Ultimately, moving to Crossplane is a paradigm shift. It requires letting go of the comfortable, procedural workflows of traditional infrastructure as code and embracing the chaotic, eventual consistency of Kubernetes. It has a learning curve that might make you pull your hair out initially, but once you set up your Composite Resources and your GitOps pipelines, you will never want to go back to juggling state files again.

March 29, 2026 by Cloud stuff DevOps stuff Kubernetes SRE stuff

Azure DevOps to GCP without static keys

Static service account keys have an odd domestic quality to them. They begin life as a sensible convenience and, after a few months, end up tucked into variable groups, copied into wikis, or lurking in a repository with the innocent menace of a spare house key under a flowerpot. They work, certainly. So does leaving your front door on the latch. The problem is not whether it works. The problem is how long you can keep pretending it is a good idea.

This article shows how to let Azure DevOps authenticate to Google Cloud without creating or storing a long-lived service account key. Instead, Azure DevOps presents a short-lived OIDC token, Google Cloud checks that token against a workload identity provider, and the pipeline receives temporary Google credentials only for the duration of the job.

The result is cleaner, safer, and far less likely to produce the sort of sentence nobody enjoys reading in a postmortem, namely, “we found an old credential in a place that should not have contained a credential.”

Why this setup is worth the trouble

The old pattern is familiar. You create a Google Cloud service account, download a JSON key, store it somewhere “temporary”, and then spend the next year hoping nobody has copied it into four other places. Even if the key never leaks, it still becomes one more secret to rotate, one more thing to explain to auditors, and one more awkward dependency between your pipeline and a file that should not really exist.

Workload Identity Federation replaces that with short-lived trust. Azure DevOps proves who it is at runtime. Google Cloud verifies that proof. No static key is issued, no secret needs to be rotated, and there is much less housekeeping disguised as security.

Strictly speaking, you can grant permissions directly to the federated principal in Google Cloud. In this article, I am using service account impersonation instead. It is a little easier to reason about, it fits neatly with how many teams already model CI identities, and it behaves consistently across a wide range of Google Cloud services.

What is actually happening

Under the hood, the flow is less mystical than it first appears.

Azure DevOps has a service connection that can mint an OIDC ID token for the running pipeline. Google Cloud has a workload identity pool and an OIDC provider configured to trust tokens issued by that Azure DevOps organization. When the pipeline runs, it retrieves the token, writes a small credential configuration file, and uses that file to exchange the token for temporary Google credentials. Those credentials are then used to impersonate a Google Cloud service account with the exact roles needed for the job.

If you prefer a more ordinary analogy, think of it as a reception desk in an office building. Azure DevOps arrives with a temporary visitor badge. Google Cloud checks whether the badge was issued by a reception desk it trusts, whether it belongs to the expected visitor, and whether that visitor is allowed through the next door. If all of that checks out, access is granted for a while and then expires. Nobody hands over the master keys to the building.

Preparing Azure DevOps

The Azure DevOps side is simpler than it first looks, although the menus do their best to suggest otherwise.

Create an Azure Resource Manager service connection in your Azure DevOps project and use these settings:

  • Identity type: App registration (automatic)
  • Credential: Workload identity federation
  • Scope level: Subscription

Yes, you still need to select a subscription even if your real destination is Google Cloud. It feels slightly like being asked for your train ticket while boarding a ferry, but that is the supported path.

Once the service connection is saved, note down two values from the Workload Identity federation details section:

  • Issuer
  • Subject identifier

The issuer identifies your Azure DevOps organization. The subject identifier identifies the service connection. In practice, the subject identifier follows this pattern:

sc://your-organization/your-project/your-service-connection

That detail matters because Google Cloud will ultimately trust this specific identity, not merely “some pipeline from somewhere in the general direction of Azure.”

A practical naming note is worth making here. Choose a stable, descriptive service connection name early. Renaming things later is always possible in the same way as replacing the plumbing in a bathroom is possible. The word possible is doing quite a lot of work.

Teaching Google Cloud to trust Azure DevOps

Now we move to Google Cloud, where the important trick is to trust the right thing in the right way.

Create a dedicated workload identity pool and OIDC provider. You can do this from the console, but the CLI version is easier to keep, review, and repeat.

export IDENTITY_PROJECT_ID="acme-identity-hub"
export IDENTITY_PROJECT_NUMBER="998877665544"
export POOL_ID="ado-pool"
export PROVIDER_ID="ado-oidc"
export ISSUER_URI="https://vstoken.dev.azure.com/11111111-2222-3333-4444-555555555555"

# Enable the required APIs

gcloud services enable \
  iam.googleapis.com \
  cloudresourcemanager.googleapis.com \
  iamcredentials.googleapis.com \
  sts.googleapis.com \
  --project="$IDENTITY_PROJECT_ID"

# Create the workload identity pool

gcloud iam workload-identity-pools create "$POOL_ID" \
  --project="$IDENTITY_PROJECT_ID" \
  --location="global" \
  --display-name="Azure DevOps pool" \
  --description="Federation trust for Azure DevOps pipelines"

# Create the OIDC provider

gcloud iam workload-identity-pools providers create-oidc "$PROVIDER_ID" \
  --project="$IDENTITY_PROJECT_ID" \
  --location="global" \
  --workload-identity-pool="$POOL_ID" \
  --display-name="Azure DevOps provider" \
  --issuer-uri="$ISSUER_URI" \
  --allowed-audiences="api://AzureADTokenExchange" \
  --attribute-mapping="google.subject=assertion.sub.extract('/sc/{service_connection}')"

There are two details here that are easy to get wrong.

First, the allowed audience for the provider is “api://AzureADTokenExchange”. It is not a random per-connection UUID, and it is not the audience string that later appears inside the external account credential file used by the pipeline.

Second, the attribute mapping should not map “google.subject” to “assertion.aud”. For Azure DevOps, the supported workaround for the 127 byte subject limit is to extract the service connection portion from the “sub” claim:

google.subject=assertion.sub.extract('/sc/{service_connection}')

This matters because the raw Azure DevOps subject can be too long for “google.subject”. Extracting the useful part solves the length issue neatly and still gives Google Cloud a stable subject to authorize.

You do not need an attribute condition for Azure DevOps. The issuer is already tenant-specific, which keeps this case pleasantly less dramatic than some other CI systems.

Creating the service account

Next, create the Google Cloud service account that your pipeline will impersonate.

The exact roles depend on what your pipeline needs to do. If the job only uploads artifacts to Cloud Storage, grant a storage role and stop there. If it deploys Cloud Run services, grant the Cloud Run roles it genuinely needs. This is one of those rare moments in cloud engineering where restraint is both morally admirable and operationally useful.

Here is a simple example:

export DEPLOY_PROJECT_ID="acme-observability-dev"
export SERVICE_ACCOUNT_NAME="ci-deployer"
export SERVICE_ACCOUNT_EMAIL="${SERVICE_ACCOUNT_NAME}@${DEPLOY_PROJECT_ID}.iam.gserviceaccount.com"
export FEDERATED_SUBJECT="your-organization/your-project/your-service-connection"

# Create the service account

gcloud iam service-accounts create "$SERVICE_ACCOUNT_NAME" \
  --project="$DEPLOY_PROJECT_ID" \
  --display-name="CI deployer for Azure DevOps"

# Grant only the roles your pipeline really needs

gcloud projects add-iam-policy-binding "$DEPLOY_PROJECT_ID" \
  --member="serviceAccount:${SERVICE_ACCOUNT_EMAIL}" \
  --role="roles/storage.objectAdmin"

# Allow the federated Azure DevOps identity to impersonate the service account

gcloud iam service-accounts add-iam-policy-binding "$SERVICE_ACCOUNT_EMAIL" \
  --project="$DEPLOY_PROJECT_ID" \
  --role="roles/iam.workloadIdentityUser" \
  --member="principal://iam.googleapis.com/projects/${IDENTITY_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${POOL_ID}/subject/${FEDERATED_SUBJECT}"

The “FEDERATED_SUBJECT” value must match the subject produced by your attribute mapping. In plain English, that means the service connection identity that Google Cloud should trust. If the pool lives in one project and the service account lives in another, that is fine, but be careful to use the project number of the identity project in the principal URI.

Building the pipeline

Now for the part everyone actually came for.

The pipeline below uses the AzureCLI task to obtain the Azure DevOps OIDC token, stores it in a temporary file, writes an external account credential file for Google Cloud, signs in with “gcloud”, and then runs a test command.

trigger:
- main

pool:
  vmImage: 'ubuntu-latest'

variables:
  azureServiceConnection: 'gcp-federation-prod'
  gcpProjectId: 'acme-observability-dev'
  gcpProjectNumber: '998877665544'
  gcpPoolId: 'ado-pool'
  gcpProviderId: 'ado-oidc'
  gcpServiceAccount: 'ci-deployer@acme-observability-dev.iam.gserviceaccount.com'
  GOOGLE_APPLICATION_CREDENTIALS: '$(Pipeline.Workspace)/gcp-wif.json'

steps:
- checkout: self

- task: AzureCLI@2
  displayName: 'Authenticate to Google Cloud with workload identity federation'
  inputs:
    azureSubscription: '$(azureServiceConnection)'
    addSpnToEnvironment: true
    scriptType: 'bash'
    scriptLocation: 'inlineScript'
    inlineScript: |
      set -euo pipefail

      TOKEN_FILE="$(Pipeline.Workspace)/ado-token.jwt"
      printf '%s' "$idToken" > "$TOKEN_FILE"

      cat > "$GOOGLE_APPLICATION_CREDENTIALS" <<EOF
      {
        "type": "external_account",
        "audience": "//iam.googleapis.com/projects/$(gcpProjectNumber)/locations/global/workloadIdentityPools/$(gcpPoolId)/providers/$(gcpProviderId)",
        "subject_token_type": "urn:ietf:params:oauth:token-type:jwt",
        "token_url": "https://sts.googleapis.com/v1/token",
        "credential_source": {
          "file": "$TOKEN_FILE"
        },
        "service_account_impersonation_url": "https://iamcredentials.googleapis.com/v1/projects/-/serviceAccounts/$(gcpServiceAccount):generateAccessToken"
      }
      EOF

      gcloud auth login --cred-file="$GOOGLE_APPLICATION_CREDENTIALS" --quiet
      gcloud config set project "$(gcpProjectId)" --quiet

      echo "Authenticated as federated workload"
      gcloud storage buckets list --limit=5

A couple of details are doing more work here than they appear to be doing.

“addSpnToEnvironment: true” is essential. Without it, the task does not expose the “idToken” variable to your script. The pipeline then behaves like a very polite person who has shown up for an exam without bringing a pen.

The “audience” inside the generated JSON file is also important. This is the full resource name of the workload identity provider in Google Cloud. It is not the same thing as the allowed audience configured on the provider itself. The two values serve different purposes, which is perfectly reasonable once you know it and deeply annoying before you do.

An alternative credential file approach

If you prefer to generate the configuration file with “gcloud” rather than writing JSON inline, you can do that too:

gcloud iam workload-identity-pools create-cred-config \
  "projects/${gcpProjectNumber}/locations/global/workloadIdentityPools/${gcpPoolId}/providers/${gcpProviderId}" \
  --service-account="${gcpServiceAccount}" \
  --credential-source-file="$TOKEN_FILE" \
  --output-file="$GOOGLE_APPLICATION_CREDENTIALS"

That version is perfectly serviceable and often a little tidier if you dislike heredocs. I have shown the explicit JSON version in the main pipeline because it makes each moving part visible, which is useful while learning or troubleshooting.

Common pitfalls

There are a few places where people lose an afternoon.

The token exists, but the pipeline still fails

Make sure the AzureCLI task is using the correct service connection and that “addSpnToEnvironment” is enabled. If “$idToken” is empty, the problem is usually on the Azure DevOps side, not in Google Cloud.

The principal binding looks right, but impersonation is denied

Check the project number in the principal URI. It must be the project number that owns the workload identity pool, not necessarily the project where the service account lives.

Also, check the federated subject. Because of the attribute mapping, the subject is the extracted service connection path, not the raw OIDC subject, and not a made-up shorthand invented during a stressful coffee break.

The pipeline freezes on an authentication prompt

Use ‘–quiet’ with ‘gcloud auth login’ and similar commands. CI jobs are many things, but conversationalists they are not.

Hosted agents are not available

If your Azure DevOps organization has not yet been granted hosted parallelism, use a self-hosted agent temporarily. In that case, make sure the machine already has ‘az’ and ‘gcloud’ installed and available on the ‘PATH’.

A minimal self-hosted pool declaration looks like this:

pool:
  name: 'Default'

On Windows, remember to switch the script type to PowerShell or PowerShell Core and adjust the environment variable syntax accordingly.

Leaving the keys behind

This setup removes one of the more tiresome habits of cross-cloud automation, namely, manufacturing a secret only to spend the rest of its natural life protecting it from yourself. Azure DevOps can obtain a short-lived token, Google Cloud can verify it, and your pipeline can impersonate a tightly scoped service account without anybody downloading a JSON key and promising to delete it later.

That is the technical benefit. The practical benefit is even nicer. Once this is in place, your pipeline starts to feel less like a cupboard full of labelled jars, some of which may or may not contain explosives, and more like a system that knows who it is, proves it when asked, and then gets on with the job.

Which, in cloud engineering, is about as close as one gets to elegance.

March 23, 2026 by Cloud stuff DevOps stuff SRE stuff

Surviving the Ingress NGINX apocalypse without breaking a sweat

Look at the calendar. It is March 2026. The deadline we have been hearing about for months has officially arrived, and across the globe, engineers are clutching their coffee mugs, staring at their terminals, and waiting for their Kubernetes clusters to spontaneously combust. There is a palpable panic in the air. Tech forums are overflowing with dramatic declarations that the internet is broken, all because a specific piece of software is officially retiring.

Take a deep breath. Your servers are not going to melt. Traffic is not going to suddenly hit a brick wall. But you do need to pack up your things and move, because the building you are living in just fired its maintenance staff.

To understand how we got here and how to get out alive, we need to stop treating this retirement like a digital Greek tragedy and start looking at it like a mundane eviction notice. We are going to peel back the layers of this particular onion, dry our eyes, and figure out how to migrate our traffic routing without breaking a sweat.

The great misunderstanding of what is actually dying

Before we start packing boxes, we need to address the rampant identity confusion that has turned a routine software lifecycle event into a source of mass hysteria. A lot of online discussion has mixed up three entirely different things, treating them like a single, multi-headed beast. Let us separate them.

First, there is NGINX. This is the web server and reverse proxy that has been moving packets around the internet since you were still excited about flip phones. NGINX is fine. Nobody is retiring NGINX. It is healthy, wealthy, and continues to route a massive chunk of the global internet.

Second, there is the Ingress API. This is the Kubernetes object you use to describe your HTTP and HTTPS routing rules. It is just a set of instructions. The Ingress API is not being removed. The Kubernetes maintainers are not going to sneak into your cluster at night and delete your YAML files.

Finally, there is the Ingress NGINX controller. This is the community-maintained piece of software that reads your Ingress API instructions and configures NGINX to actually execute them. This specific controller, maintained by a group of incredibly exhausted volunteers, is the thing that is retiring. As of right now, March 2026, it is no longer receiving updates, bug fixes, or security patches.

That distinction avoids most of the confusion. The bouncer at the door of your nightclub is retiring, but the nightclub itself is still open, and the rules of who gets in remain the same. You just need to hire a new bouncer.

Why the bouncer finally walked off the job

To understand why the community Ingress NGINX controller is packing its bags, you have to look at what we forced it to do. For years, this controller has been the stoic bouncer at the entrance of your Kubernetes cluster. It stood in the rain, checked the TLS certificates, and decided which request got into the VIP pod and which one got thrown out into the alley.

But the Ingress API itself was fundamentally limited. It only understood the basics. It knew about hostnames and paths, but it had no idea how to handle anything complex, like weighted canary deployments, custom header manipulation, or rate limiting.

Because we developers are needy creatures who demand complex routing, we found a workaround. We started using annotations. We slapped sticky notes all over the bouncer’s forehead. We wrote cryptic instructions on these notes, telling the controller to inject custom configuration snippets directly into the underlying NGINX engine.

Eventually, the bouncer was walking around completely blinded by thousands of contradictory sticky notes. Maintaining this chaotic system became a nightmare for the open-source volunteers. They were basically performing amateur dental surgery in the dark, trying to patch security holes in a system entirely built out of user-injected string workarounds. The technical debt became a mountain, and the maintainers rightly decided they had had enough.

The terrifying reality of unpatched edge components

If the controller is not going to suddenly stop working today, you might be tempted to just leave it running. This is a terrible idea.

Leaving an obsolete, unmaintained Ingress controller facing the public internet is exactly like leaving the front door of your house under the strict protection of a scarecrow. The crows might stay away for the first week. But eventually, the local burglars will realize your security system is made of straw and old clothes.

Edge proxies are the absolute favorite targets for attackers. They sit right on the boundary between the wild, unfiltered internet and your soft, vulnerable application data. When a new vulnerability is discovered next month, there will be no patch for your retired Ingress NGINX controller. Attackers will scan the internet for that specific outdated signature, and they will walk right past your scarecrow. Do not be the person explaining to your boss that the company data was stolen because you did not want to write a few new YAML files.

Meet the new security firm known as Gateway API

If Ingress was a single bouncer overwhelmed by sticky notes, the new standard, known as Gateway API, is a professional security firm with distinct departments.

The core problem with Ingress was that it forced the infrastructure team and the application developers to fight over the same file. The platform engineer wanted to manage the TLS certificates, while the developer just wanted to route traffic to their new shopping cart service.

Gateway API fixes this by splitting the responsibilities into different objects. You have a GatewayClass (the type of security firm), a Gateway (the physical building entrance managed by the platform team), and an HTTPRoute (the specific room VIP lists managed by the developers). It is structured, it is typed, and most importantly, it drastically reduces the need for those horrible annotation sticky notes.

You do not have to migrate to the Gateway API. You can simply switch to a different, commercially supported Ingress controller that still reads your old files. But if you are going to rip off the bandage and change your routing infrastructure, you might as well upgrade to the modern standard.

A before-and-after infomercial for your YAML files

Let us look at a practical example. Has this ever happened to you? Are your YAML files bloated, confusing, and causing you physical pain to read? Look at this disastrous piece of legacy Ingress configuration.

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: desperate-cries-for-help
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /$2
    nginx.ingress.kubernetes.io/use-regex: "true"
    nginx.ingress.kubernetes.io/server-snippet: |
      location ~* ^/really-bad-regex/ {
        return 418;
      }
    nginx.ingress.kubernetes.io/canary: "true"
    nginx.ingress.kubernetes.io/canary-weight: "15"
spec:
  ingressClassName: nginx
  rules:
  - host: chaotic-store.example.local
    http:
      paths:
      - path: /catalog(/|$)(.*)
        pathType: Prefix
        backend:
          service:
            name: catalog-service-v2
            port:
              number: 8080

This is not a configuration. This is a hostage note. You are begging the controller to understand regex rewrites and canary deployments by passing simple strings through annotations.

Now, wipe away those tears and look at the clean, structured beauty of an HTTPRoute in the Gateway API world.

apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: calm-and-collected-routing
spec:
  parentRefs:
  - name: main-company-gateway
  hostnames:
  - "smooth-store.example.local"
  rules:
  - matches:
    - path:
        type: PathPrefix
        value: /catalog
    filters:
    - type: URLRewrite
      urlRewrite:
        path:
          type: ReplacePrefixMatch
          replacePrefixMatch: /
    backendRefs:
    - name: catalog-service-v1
      port: 8080
      weight: 85
    - name: catalog-service-v2
      port: 8080
      weight: 15

Look at that. No sticky notes. No injected server snippets. The routing weights and the URL rewrites are native, structured fields. Your linter can actually read this and tell you if you made a typo before you deploy it and take down the entire production environment.

A twelve-step rehabilitation program for your cluster

You cannot just delete the old controller on a Friday afternoon and hope for the best. You need a controlled rehabilitation program for your cluster. Treat this as a serious infrastructure project.

Phase 1: The honest inventory

You need to look at yourself in the mirror and figure out exactly what you have deployed. Find every single Ingress object in your cluster. Document every bizarre annotation your developers have added over the years. You will likely find routing rules for services that were decommissioned three years ago.

Phase 2: Choosing your new guardian

Evaluate the replacements. If you want to stick with NGINX, look at the official F5 NGINX Ingress Controller. If you want something modern, look at Envoy-based solutions like Gateway API implementations from Cilium, Istio, or Contour. Deploy your choice into a sandbox environment.

Phase 3: The great translation

Start converting those sticky notes. Take your legacy Ingress objects and translate them into Gateway API routes, or at least clean them up for your new controller. This is the hardest part. You will have to decipher what nginx.ingress.kubernetes.io/configuration-snippet actually does in your specific context.

Phase 4: The side-by-side test

Run the new controller alongside the retiring community one. Use a test domain. Throw traffic at it. Watch the metrics. Ensure that your monitoring dashboards and alerting rules still work, because the new controller will expose entirely different metric formats.

Phase 5: The DNS switch

Slowly move your DNS records from the old load balancer to the new one. Do this during business hours when everyone is awake and heavily caffeinated, not at 2 AM on a Sunday.

The final word on not panicking

If you need a message to send to your management team today, keep it simple. Tell them the community ingress-nginx controller is now officially unmaintained. Assure them the website is not down, but inform them that staying on this software is a ticking security time bomb. You need time and resources to move to a modern implementation.

The real lesson here is not that Kubernetes is unstable. It is that the software world relies heavily on the unpaid labor of open-source maintainers. When a critical project no longer has enough volunteers to hold back the tide of technical debt, responsible engineering teams do not sit around complaining on internet forums. They say thank you for the years of free service, they roll up their sleeves, and they migrate before the lack of maintenance becomes an incident report.

March 17, 2026 by DevOps stuff Kubernetes SRE stuff

AWSMap for smarter AWS migrations

Most AWS migrations begin with a noble ambition and a faintly ridiculous problem.

The ambition is to modernise an estate, reduce risk, tidy the architecture, and perhaps, if fortune smiles, stop paying for three things nobody remembers creating.

The ridiculous problem is that before you can migrate anything, you must first work out what is actually there.

That sounds straightforward until you inherit an AWS account with the accumulated habits of several teams, three naming conventions, resources scattered across regions, and the sort of IAM sprawl that suggests people were granting permissions with the calm restraint of a man feeding pigeons. At that point, architecture gives way to archaeology.

I do not work for AWS, and this is not a sponsored love letter to a shiny console feature. I am an AWS and GCP architect working in the industry, and I have used AWSMap when assessing environments ahead of migration work. The reason I am writing about it is simple enough. It is one of those practical tools that solves a very real problem, and somehow remains less widely known than it deserves.

AWSMap is a third-party command-line utility that inventories AWS resources across regions and services, then lets you explore the result through HTML reports, SQL queries, and plain-English questions. In other words, it turns the early phase of a migration from endless clicking into something closer to a repeatable assessment process.

That does not make it perfect, and it certainly does not replace native AWS services. But in the awkward first hours of understanding an inherited environment, it can be remarkably useful.

The migration problem before the migration

A cloud migration plan usually looks sensible on paper. There will be discovery, analysis, target architecture, dependency mapping, sequencing, testing, cutover, and the usual brave optimism seen in project plans everywhere.

In reality, the first task is often much humbler. You are trying to answer questions that should be easy and rarely are.

What is running in this account?

Which regions are actually in use?

Are there old snapshots, orphaned EIPs, forgotten load balancers, or buckets with names that sound important enough to frighten everyone into leaving them alone?

Which workloads are genuinely active, and which are just historical luggage with a monthly invoice attached?

You can answer those questions from the AWS Management Console, of course. Given enough tabs, enough patience, and a willingness to spend part of your afternoon wandering through services you had not planned to visit, you will eventually get there. But that is not a particularly elegant way to begin a migration.

This is where AWSMap becomes handy. Instead of treating discovery as a long guided tour of the console, it treats it as a data collection exercise.

What AWSMap does well

At its core, AWSMap scans an AWS environment and produces an inventory of resources. The current public package description on PyPI describes it as covering more than 150 AWS services, while version 1.5.0 covers 140 plus services, which is a good reminder that the coverage evolves. The important point is not the exact number on a given Tuesday morning, but that it covers a broad enough slice of the estate to be genuinely useful in early assessments.

What makes the tool more interesting is what it does after the scan.

It can generate a standalone HTML report, store results locally in SQLite, let you query the inventory with SQL, run named audit queries, and translate plain-English prompts into database queries without sending your infrastructure metadata off to an LLM service. The release notes for v1.5.0 describe local SQLite storage, raw SQL querying, named queries, typo-tolerant natural language questions, tag filtering, scoped account views, and browsable examples.

That combination matters because migrations are rarely single, clean events. They are usually a series of discoveries, corrections, and mildly awkward conversations. Having the inventory preserved locally means the account does not need to be rediscovered from scratch every time someone asks a new question two days later.

The report you can actually hand to people

One of the surprisingly practical parts of AWSMap is the report output.

The tool can generate a self-contained HTML report that opens locally in a browser. That sounds almost suspiciously modest, but it is useful precisely because it is modest. You can attach it to a ticket, share it with a teammate, or open it during a workshop without building a whole reporting pipeline first. The v1.5.0 release notes describe the report as a single, standalone HTML file with filtering, search, charts, and export options.

That makes it suitable for the sort of migration meeting where someone says, “Can we quickly check whether eu-west-1 is really the only active region?” and you would rather not spend the next ten minutes performing a slow ritual through five console pages.

A simple scan might look like this:

awsmap -p client-prod

If you want to narrow the blast radius a bit and focus on a few services that often matter early in migration discovery, you could do this:

awsmap -p client-prod -s ec2,rds,elb,lambda,iam

And if the account is a thicket of shared infrastructure, tags can help reduce the noise:

awsmap -p client-prod -t Environment=Production -t Owner=platform-team

That kind of filtering is helpful when the account contains equal parts business workload and historical clutter, which is to say, most real accounts.

Why SQLite is more important than it sounds

The feature I like most is not the report. It is the local SQLite database.

Every scan can be stored locally, so the inventory becomes queryable over time instead of vanishing the moment the terminal output scrolls away. The default local database path is ‘~/.awsmap/inventory.db’, and the scan results from different runs can accumulate there for later analysis.

This changes the character of the tool quite a bit. It stops being a disposable scanner and becomes something closer to a field notebook.

Suppose you scan a client account today, then return to the same work three days later, after someone mentions an old DR region nobody had documented. Without persistence, you start from scratch. With persistence, you ask the database.

That is a much more civilised way to work.

A query for the busiest services in the collected inventory might look like this:

awsmap query "SELECT service, COUNT(*) AS total
FROM resources
GROUP BY service
ORDER BY total DESC
LIMIT 12"

And a more migration-focused query might be something like:

awsmap query "SELECT account, region, service, name
FROM resources
WHERE service IN ('ec2', 'rds', 'elb', 'lambda')
ORDER BY account, region, service, name"

Neither query is glamorous, but migrations are not built on glamour. They are built on being able to answer dull, important questions reliably.

Security and hygiene checks without the acrobatics

AWSMap also includes named queries for common audit scenarios, which is useful for two reasons.

First, most people do not wake up eager to write SQL joins against IAM relationships. Second, migration assessments almost always drift into security checks sooner or later.

The public release notes describe named queries for scenarios such as admin users, public S3 buckets, unencrypted EBS volumes, unused Elastic IPs, and secrets without rotation.

That means you can move from “What exists?” to “What looks questionable?” without much ceremony.

For example:

awsmap query -n admin-users
awsmap query -n public-s3-buckets
awsmap query -n ebs-unencrypted
awsmap query -n unused-eips

Those are not, strictly speaking, migration-only questions. But they are precisely the kind of questions that surface during migration planning, especially when the destination design is meant to improve governance rather than merely relocate the furniture.

Asking questions in plain English

One of the nicer additions in the newer version is the ability to ask plain-English questions.

That is the sort of feature that normally causes one to brace for disappointment. But here the approach is intentionally local and deterministic. This functionality is a built-in parser rather than an LLM-based service, which means no API keys, no network calls to an external model, and no need to ship resource metadata somewhere mysterious.

That matters in enterprise environments where the phrase “just send the metadata to a third-party AI service” tends to receive the warm reception usually reserved for wasps.

Some examples:

awsmap ask show me lambda functions by region
awsmap ask list databases older than 180 days
awsmap ask find ec2 instances without Owner tag

Even when the exact wording varies, the basic idea is appealing. Team members who do not want to write SQL can still interrogate the inventory. That lowers the barrier for using the tool during workshops, handovers, and review sessions.

Where AWSMap fits next to AWS native services

This is the part worth stating clearly.

AWSMap is useful, but it is not a replacement for AWS Resource Explorer, AWS Config, or every other native mechanism you might use for discovery, governance, and inventory.

AWS Resource Explorer can search across supported resource types and, since 2024, can also discover all tagged AWS resources using the ‘tag:all’ operator. AWS documentation also notes an important limitation for IAM tags in Resource Explorer search.

AWS Config, meanwhile, continues to expand the resource types it can record, assess, and aggregate. AWS has announced multiple additions in 2025 and 2026 alone, which underlines that the native inventory and compliance story is still moving quickly.

So why use AWSMap at all?

Because its strengths are slightly different.

It is local.

It is quick to run.

It gives you a portable HTML report.

It stores results in SQLite for later interrogation.

It lets you query the inventory directly without setting up a broader governance platform first.

That makes it particularly handy in the early assessment phase, in consultancy-style discovery work, or in those awkward inherited environments where you need a fast baseline before deciding what the more permanent controls should be.

The weak points worth admitting

No serious article about a tool should pretend the tool has descended from heaven in perfect condition, so here are the caveats.

First, coverage breadth is not the same thing as universal depth. A tool can support a large number of services and still provide uneven detail between them. That is true of almost every inventory tool ever made.

Second, the quality of the result still depends on the credentials and permissions you use. If your access is partial, your inventory will be partial, and no amount of cheerful HTML will alter that fact.

Third, local storage is convenient, but it also means you should be disciplined about how scan outputs are handled on your machine, especially if you are working with client environments. Convenience and hygiene should remain on speaking terms.

Fourth, for organisation-wide governance, compliance history, managed rules, and native integrations, AWS services such as Config still have an obvious place. AWSMap is best seen as a sharp assessment tool, not a universal control plane.

That is not a criticism so much as a matter of proper expectations.

A practical workflow for migration discovery

If I were using AWSMap at the start of a migration assessment, the workflow would be something like this.

First, run a broad scan of the account or profile you care about.

awsmap -p client-prod

Then, if the account is noisy, refine the scope.

awsmap -p client-prod -s ec2,rds,elb,iam,route53
awsmap -p client-prod --exclude-defaults

Next, use a few named queries to surface obvious issues.

awsmap query -n public-s3-buckets
awsmap query -n secrets-no-rotation
awsmap query -n admin-users

After that, ask targeted questions in either SQL or plain English.

awsmap ask list load balancers by region
awsmap ask show databases with no backup tag
awsmap query "SELECT region, COUNT(*) AS total
FROM resources
WHERE service='ec2'
GROUP BY region
ORDER BY total DESC"

And finally, keep the HTML report and local inventory as a baseline for later design discussions.

That is where the tool earns its keep. It gives you a reasonably fast, reasonably structured picture of an estate before the migration plan turns into a debate based on memory, folklore, and screenshots in old slide decks.

When the guessing stops

There is a particular kind of misery in cloud work that comes from being asked to improve an environment before anyone has properly described it.

Tools do not eliminate that misery, but some of them reduce it to a more manageable size.

AWSMap is one of those.

It is not the only way to inventory AWS resources. It is not a substitute for native governance services. It is not magic. But it is practical, fast to understand, and surprisingly helpful when the first job in a migration is simply to stop guessing.

That alone makes it worth knowing about.

And in cloud migrations, a tool that helps replace guessing with evidence is already doing better than half the room.

March 14, 2026 by Cloud stuff DevOps stuff SRE stuff

How a Kubernetes Pod comes to life

Run ‘kubectl apply -f pod.yaml’ and Kubernetes has the good manners to make it look simple. You hand over a neat little YAML file, press Enter, and for a brief moment, it feels as if you have politely asked the cluster to start a container.

That is not what happened.

What you actually did was file a request with a distributed bureaucracy. Several components now need to validate your paperwork, record your wishes for posterity, decide where your Pod should live, prepare networking and storage, ask a container runtime to do the heavy lifting, and keep watching the whole arrangement in case it misbehaves. Kubernetes is extremely good at hiding all this. It has the same talent as a hotel lobby. Everything looks calm and polished, while somewhere behind the walls, people are hauling luggage, changing sheets, arguing about room allocation, and trying not to let anything catch fire.

This article follows that process from the moment you submit a manifest to the moment the Pod disappears again. To keep the story tidy, I will use a standalone Pod. In real production environments, Pods are usually created by higher-level controllers such as Deployments, Jobs, or StatefulSets. The Pod is still the thing that ultimately gets scheduled and runs, so it remains the most useful unit to study when you want to understand what Kubernetes is really doing.

The YAML lands on the front desk

Let us start with a very small Pod manifest:

apiVersion: v1
kind: Pod
metadata:
  name: demo-pod
  labels:
    app: demo
spec:
  containers:
    - name: web
      image: nginx:1.27
      ports:
        - containerPort: 80
      resources:
        requests:
          cpu: "100m"
          memory: "128Mi"
        limits:
          cpu: "250m"
          memory: "256Mi"

When you apply this file, the request goes to the Kubernetes API server. That is the front door of the cluster. Nothing important happens without passing through it first.

The API server does more than nod politely and stamp the form. It checks authentication and authorization, validates the object schema, and sends the request through admission control. Admission controllers can modify or reject the request based on policies, quotas, defaults, or security rules. Only when that process is complete does the API server persist the desired state in etcd, the key-value store Kubernetes uses as its source of truth.

At that point, the Pod officially exists as an object in the cluster.

That does not mean it is running.

It means Kubernetes has written down your intentions in a very serious ledger and is now obliged to make reality catch up.

The scheduler looks for a home

Once the Pod exists but has no node assigned, the scheduler takes interest. Its job is not to run the Pod. Its job is to decide where the Pod should run.

This is less mystical than it sounds and more like trying to seat one extra party in a crowded restaurant without blocking the fire exit.

The scheduler first filters out nodes that cannot host the Pod. A node may be ruled out because it lacks CPU or memory, does not match nodeSelector labels, has taints the Pod does not tolerate, violates affinity or anti-affinity rules, or fails other placement constraints.

From the nodes that survive this round of rejection, the scheduler scores the viable candidates and picks one. Different scoring plugins influence the choice, including resource balance and topology preferences. Kubernetes is not asking, “Which node feels lucky today?” It is performing a structured selection process, even if the result arrives so quickly that it looks like instinct.

When the decision is made, the scheduler updates the Pod object with the chosen node.

That is all.

It does not pull images, start containers, mount storage, or wave a wand. It points at a node and says, in effect, “This one. Good luck to everyone involved.”

The kubelet picks up the job

Each node runs an agent called the kubelet. The kubelet watches the API server and notices when a Pod has been assigned to its node.

This is where the abstract promise turns into physical work.

The kubelet reads the Pod specification and starts coordinating with the local container runtime, such as ‘containerd’, to make the Pod real. If there are volumes to mount, secrets to project, environment variables to inject, or images to fetch, the kubelet is the one making sure those steps happen in the correct order.

The kubelet is not glamorous. It is the floor manager. It does not write the policies, it does not choose the table, and it does not get invited to keynote conferences. It simply has to make the plan work on an actual machine with actual limits. That makes it one of the most important components in the whole affair.

The sandbox appears before the containers do

Before your application container starts, Kubernetes prepares a Pod sandbox.

This is one of those wonderfully unglamorous details that turns out to matter a great deal. A Pod is not just “a container.” It is a small execution environment that may contain one or more containers sharing networking and, often, storage.

To build that environment, several things need to happen.

First, the container runtime may need to pull the image from a registry if it is not already cached on the node. This step alone can keep a Pod waiting for longer than people expect, especially when the image is huge, the registry is slow, or somebody has built an image as if hard disk space were a personal insult.

Second, networking must be prepared. Kubernetes relies on a CNI plugin to create the Pod’s network namespace and assign an IP address. All containers in the same Pod share that network namespace, which is why they can communicate over ‘localhost’. This is convenient and occasionally dangerous, much like sharing a flat with someone who assumes every shelf in the fridge belongs to them.

Third, volumes are mounted. If the Pod references ‘emptyDir’, ‘configMap’, ‘secret’, or persistent volumes, those mounts have to be prepared before the containers can use them.

There is also a small infrastructure container, commonly called the ‘pause’ container, whose job is to hold the Pod’s shared namespaces in place. It is not famous, but it is essential. The ‘pause’ container is a bit like the quiet relative at a family gathering who does no storytelling, makes no dramatic entrance, and is nevertheless the reason the chairs are still standing.

Only after this setup is complete can the application containers begin.

Watching the lifecycle from the outside

You can observe part of this process with a few simple commands:

kubectl apply -f pod.yaml
kubectl get pod demo-pod -w
kubectl describe pod demo-pod

The watch output often gives the first visible clue that the cluster is busy doing considerably more than the neatness of YAML would suggest.

A Pod typically moves through a small set of phases:

  • ‘Pending’ means the Pod has been accepted but is still waiting for scheduling, image pulls, volume setup, or other preparation.
  • ‘Running’ means the Pod has been bound to a node and at least one container is running or starting.
  • ‘Succeeded’ means all containers completed successfully and will not be restarted.
  • ‘Failed’ means all containers finished, but at least one exited with an error.
  • ‘Unknown’ means the control plane cannot reliably determine the Pod state, usually because communication with the node has gone sideways.

These phases are useful, but they do not tell the whole story. One of the more common sources of confusion is ‘CrashLoopBackOff’. That is not a Pod phase. It is a container state pattern shown in ‘kubectl get pods’ output when a container keeps crashing, and Kubernetes backs off before trying again.

This matters because people often stare at ‘Running’ and assume everything is fine. Kubernetes, meanwhile, is quietly muttering, “Technically yes, but only in the way a car is technically functional while smoke comes out of the bonnet.”

Running is not the same as ready

Another detail worth understanding is that a Pod can be running without being ready to receive traffic.

This distinction matters in real systems because applications often need a few moments to warm up, load configuration, establish database connections, or otherwise stop acting like startled wildlife.

A readiness probe tells Kubernetes when the container is actually prepared to serve requests. Until that probe succeeds, the Pod should not be considered a healthy backend for a Service.

Here is a minimal example:

readinessProbe:
  httpGet:
    path: /
    port: 80
  initialDelaySeconds: 5
  periodSeconds: 10

With this in place, the container may be running, but Kubernetes will wait before routing traffic to it. This is one of those details that prevents very expensive forms of optimism.

Deletion is a polite process until it is not

Now, let us look at the other end of the Pod’s life.

When you run the following command, the Pod does not vanish in a puff of administrative smoke:

kubectl delete pod demo-pod

Instead, the API server marks the Pod for deletion and sets a grace period. The Pod enters a terminating state. The kubelet on the node sees that instruction and begins shutdown.

The normal sequence looks like this:

  1. Kubernetes may first stop sending new traffic to the Pod if it is behind a Service and no longer considered ready.
  2. A ‘preStop’ hook runs if one has been defined.
  3. The kubelet asks the runtime to send ‘SIGTERM’ to the container’s main process.
  4. Kubernetes waits for the grace period, which is ‘30’ seconds by default and controlled by ‘terminationGracePeriodSeconds’.
  5. If the process still refuses to exit, Kubernetes sends ‘SIGKILL’ and ends the discussion.

That grace period exists for good reasons. Applications may need time to flush logs, finish requests, close connections, write buffers, or otherwise clean up after themselves. Production systems tend to appreciate this courtesy.

Here is a small example of a graceful shutdown configuration:

terminationGracePeriodSeconds: 30
containers:
  - name: web
    image: nginx:1.27
    lifecycle:
      preStop:
        exec:
          command: ["/bin/sh", "-c", "sleep 5"]

Once the containers stop, Kubernetes cleans up the sandbox, releases network resources, unmounts volumes as needed, and frees the node’s CPU and memory.

If the Pod was managed by a Deployment, a replacement Pod will usually be created to maintain the desired replica count. This is an important point. In Kubernetes, individual Pods are disposable. The desired state is what matters. Pods come and go. The controller remains stubborn.

Why this matters in the real world

Understanding this lifecycle is not trivia for people who enjoy suffering through conference diagrams. It is practical.

If a Pod is stuck in ‘Pending’, you need to know whether the issue is scheduling, image pulling, volume attachment, or policy rejection.

If a container is ‘CrashLoopBackOff’, you need to know that the Pod object exists, has probably been scheduled, and that the failure is happening later in the chain.

If traffic is not reaching the application, you need to remember that ‘Running’ and ‘Ready’ are not the same thing.

If shutdowns are ugly, logs are truncated, or users get errors during rollout, you need to inspect readiness probes, ‘preStop’ hooks, and grace periods rather than blaming Kubernetes in the abstract, which it will survive, but your incident report may not.

This is also where commands like these become genuinely useful:

kubectl get pod demo-pod -o wide
kubectl describe pod demo-pod
kubectl logs demo-pod
kubectl get events --sort-by=.metadata.creationTimestamp

Those commands let you inspect node placement, container events, log output, and recent cluster activity. Most Kubernetes troubleshooting starts by figuring out which stage of the Pod lifecycle has gone wrong, then narrowing the problem from there.

The quiet machinery behind a simple command

The next time you type ‘kubectl apply -f pod.yaml’, it is worth remembering that you are not merely starting a container. You are triggering a chain of decisions and side effects across the control plane and a worker node.

The API server validates and records the request. The scheduler finds a suitable home. The kubelet coordinates the local work. The runtime pulls images and starts containers. The CNI plugin wires up networking. Volumes are mounted. Probes decide whether the Pod is truly ready. And when the time comes, Kubernetes tears the whole thing down with the brisk professionalism of hotel staff clearing a room before the next guest arrives.

Which is impressive, really.

Particularly when you consider that from your side of the terminal, it still looks as though you only asked for one modest little Pod.

March 10, 2026 by DevOps stuff Kubernetes SRE stuff
NivelEpsilon
Exploring the Cloud: Insights into DevOps, Linux & Architectural Evolution