In a tech company the edge no longer comes from the application code alone, but from how quickly and safely that code reaches production. As product teams multiply and the number of environments and services keeps growing, manual deployments and ad-hoc infrastructure start to slow the whole engineering org down. At that point, the cloud platform and CI/CD stop being a side topic and become the foundation of delivery speed.
In our experience, the hard part is not standing up the first pipeline but building a coherent platform layer where dozens of teams work by the same rules. We have run exactly these projects for technology leaders, engineering cloud platform components, DevOps automation and cloud-to-cloud migrations. In this article we show, step by step, how we approach platform, cloud and CI/CD.
Why does a tech company need a separate platform and cloud layer?
Because without one, every team solves the same problems from scratch. One configures deployments its own way, another handles secrets differently, a third invents its own way to spin up a test environment, and the security team has no way to tie it all together. The result is less chaos than a rising cost on every subsequent change.
A dedicated platform layer flips that logic. We treat it as an internal product for development teams: it delivers ready, repeatable ways to build, test and deploy, so a product engineer does not have to be a cloud expert to ship a change safely. For an engineering director that means predictability, faster onboarding and fewer incidents caused by manual configuration. We usually build that layer together with the client’s team, rather than imposing it detached from their reality.
How do you automate releases with CI/CD?
The goal is simple: from commit to production there should be one repeatable, auditable path, with no manual steps left to get wrong. This is how we design the release pipeline:
- automatic builds and tests on every change, so a defect surfaces in minutes, not in production,
- consistent pipelines (e.g. Jenkins, GitLab CI, GitHub Actions) shared across teams, instead of dozens of variations,
- deployments across environments (dev, staging, production) following the same scheme,
- safe rollout strategies (blue-green, canary) with a fast way to roll a change back,
- quality and security gates built into the pipeline, not bolted on afterwards.
A well-automated CI/CD cuts the path of a change from days to hours and takes the stress of deployment off the teams, because every release looks the same.
Why is infrastructure as code the foundation of a platform?
Because infrastructure clicked together by hand in a cloud console cannot be reproduced, and at a tech company’s scale that is a real risk. When we describe the environment as code (Terraform, Ansible, Helm), it stops being one person’s tribal knowledge and becomes a versioned, reviewed artifact like any other part of the system.
The practical difference is huge. A new environment comes up repeatably, not “from memory”. A change to infrastructure goes through code review and the same pipeline as the application, so you can see who changed what and why. Recovering the whole stack after a failure stops being a heroic effort, because everything is written down. Infrastructure as code is precisely what makes a platform predictable and auditable, rather than dependent on trust in individual admins.
How do Kubernetes and containerisation bring order to environments?
Containerisation solves the old “works on my machine” problem, because the application travels together with its runtime. Kubernetes adds an orchestration layer on top that makes a difference once you have many services. On platform projects we use them to:
- unify how services run, regardless of which team wrote them,
- scale the load up and down with traffic, without manually adding machines,
- isolate teams and environments through namespaces and policies,
- standardise deployments via Helm and repeatable manifests,
- increase resilience through automatic restarts and health checks.
We do not treat Kubernetes as an end in itself, though. It is a tool for a particular scale, and we reach for it where it genuinely simplifies the teams’ work, not everywhere on principle.
How do you run a cloud migration and optimise costs when moving from AWS to GCP?
A cloud migration is rarely a one-to-one “lift and shift”. We start with an inventory of what actually runs: which services, which dependencies, how much traffic and what data. Only then do we decide what to move as is, what to rebuild around the target platform’s cloud services, and what to retire because no one uses it any more.
When moving from AWS to GCP we watch two things especially: continuity and cost. We run the migration in stages so the business feels no outage, and in parallel we optimise cloud cost by picking the right resource types, autoscaling and a policy for shutting things down. The cloud can get expensive not because it is expensive by nature, but because no one keeps track of what is running and why. We have carried out this kind of cloud-to-cloud migration in practice and know where the pitfalls usually hide.
How do you set up observability and alerting for a platform?
A platform you cannot see is a platform you fix after the fact and in the dark. So we treat observability as part of the project from the start, not an add-on at the end. In practice we build:
- metrics for the performance and health of services in one coherent view,
- centralised logs instead of grepping through individual machines,
- request tracing across many services, to find the bottleneck,
- alerts based on real user-facing symptoms, not on every twitch of a chart,
- dashboards that both the on-call engineer and a manager can read.
Well-tuned alerting wakes people when it should and stays quiet when there is no reason. That is the difference between a team that fights fires and a team that prevents incidents.
Which platform skills do we provide for the project?
We shape the team around the specific stage and scale of the platform, not “just in case”. Most often we staff roles such as:
- a platform / DevOps engineer building pipelines, IaC and automation,
- a cloud engineer owning architecture and migrations (AWS, GCP),
- a Kubernetes / SRE specialist looking after scaling and reliability,
- an architect who ties the platform into the rest of the system and sets the standards.
We verify these people before the start, and as a nearshore partner from Poland we work in a close time zone, so they join code reviews, on-call and meetings without a long ramp-up.
How do we do it at RITS?
We do it as a nearshore partner from Poland, with real experience in platform and cloud projects for technology leaders. For a Silicon Valley leader building a digital platform we provided DevOps skills and executed a cloud migration from AWS to GCP, together with work on cloud platform components. For a global digital services provider we ran cloud platform projects and cloud-to-cloud migrations, which is exactly the kind of work that leaves no room for experimenting on a live system.
In both cases we bring the same thing: engineers who have done it in production, repeatable deployments built on CI/CD and infrastructure as code, and a team shaped around the project. Most often we work here as a team building and developing the platform and, where skills need topping up fast, in a team augmentation model.
Where to start with platform, cloud and CI/CD?
Start from the single most painful path: from commit to production for one chosen team. That path shows where you actually lose time, whether in manual deployments, in spinning up environments, or in a lack of visibility. From there we define the scope of the first platform stage, the cloud architecture and the roles you need. We covered the differences between engagement models in our article on IT engagement models.
Building platform, cloud or CI/CD for a tech company? Let’s talk - we’ll match a team and an architecture to your scale.