In the log we repeat an idea that appears in almost every project: the starting point is not defined by the tool you choose, but by the order in which you make decisions. Containerizing everything at once, or jumping straight to a managed cluster without having measured the real load, usually costs weeks of rework. These are the five things we document as a concrete starting point, with the expected outcome of each one.
Identify which processes write to disk, which absolute paths the code assumes, and which network dependencies exist between components. Without that map, the container starts locally and fails in the cluster for reasons no one sees in the Dockerfile. The result is a short list of services that can indeed be moved first and which ones need prior refactoring.
Kubernetes is not the automatic answer. For a single service with stable traffic, a container with supervised restart is enough and operates with a smaller error surface. The decision is documented with criteria: number of replicas, tolerance to outages, need for internal balancing, and deployment frequency. This avoids setting up a cluster that nobody maintains afterward.
Defining subnets, ingress rules, and service names resolves half of startup incidents. If there are components that communicate through queues, configuring retries with backoff and a dead-letter queue from the start prevents a slow consumer from dragging down the rest. It is cheaper to design it now than to debug it in production on a Friday.
Latency, error rate, and resource saturation metrics, plus logs with a trace identifier. There is no need to accumulate everything: the series you will look at during an incident are enough. The result is being able to compare the behavior of the legacy service against the containerized version with data, not with impressions.
Define the module structure that teams will reuse and move state to a remote backend with locking. Working with accidentally shared local state produces plans that overwrite each other and orphaned resources that are hard to track. The result is that two people can apply changes on the same day without breaking the infrastructure.
Each of these points has its own detailed entry in the log, with configuration snippets and screenshots of dashboards from the initial state of a deployment. If you want to see how they are applied to a specific case, you can review the topics we cover or start with the reference paths that we put together for platform teams.
There is no single start date or master plan that is followed to the letter. What does appear repeatedly in the notes is an order of work: first understand what runs today, then isolate, then automate. This page describes that starting point as we recorded it in the log, without promising timelines or results.
The start almost never begins with choosing tools. It begins with inventorying legacy services, network dependencies, and assumptions that the code took for granted. Only afterward does it make sense to discuss whether Kubernetes or something simpler is appropriate.
Before containerizing anything, we review which processes write to disk, which absolute paths they use, and which network dependencies they assume. That assessment usually reveals that the service is not as stateless as the diagram claimed. In the entries about migrating a legacy service to containers, there are examples of these assumptions that break the first version in the cluster.
The decision is not "all or nothing." In practice, you start with components that have clear boundaries and low coupling to local state. Services that keep sessions in memory or write to shared files are usually left for a second stage, once there is an isolated network and a configuration policy defined outside the image.
Separating environments without rebuilding the image is one of the first things documented. That means defining variables, secrets, and network segmentation before exposing traffic. The most common mistakes we record appear right here: credentials embedded in the image, firewall rules copied from one environment to another, and service names resolved by convention that no one verified.
The initial infrastructure is described with small, reusable modules: network, compute, storage, and permissions. The goal is not to cover the provider's entire catalog, but to have a reproducible base. In the note on Terraform with remote state, we explain why the local backend stops working as soon as more than one person works on the same plan.
Instrumenting metrics, logs, and traces before moving the first service to distributed production prevents migrating blindly. The question guiding this stage is not how many signals we can collect, but which ones we will look at during an incident. The article on observability without noise develops that selection criterion based on operational value.
When two services start depending on each other's response time, the temptation to insert a queue appears. The decision has consequences: you must define a retry policy, dead-letter handling, and idempotency keys. Without those definitions, the queue amplifies errors instead of absorbing them, as documented in the entry on retries and dead letters.