Platform engineering

Platform engineering is about building internal platforms, tooling and workflows that make it easier for development teams to deliver software safely, quickly and consistently.

That can include cloud infrastructure, CI/CDCI/CDCI/CD stands for continuous integration and continuous delivery/deployment: automated flows where code is built, tested and delivered to production in small, frequent steps instead of big, risky releases.Read more in the glossary →, Kubernetes, container platforms, infrastructure as codeInfrastructure as Code (IaC)Infrastructure as Code means environments, networks, permissions and resources are defined in version-controlled code instead of being clicked together manually — making infrastructure traceable, testable and repeatable.Read more in the glossary →, observability, IAM, security, self-service, developer portalsDeveloper portal (Backstage)A developer portal is the internal website where an organisation's services, teams, ownership, documentation and self-service templates are gathered in one place. Backstage, open-sourced by Spotify in 2020, is the most common framework to build one on.Read more in the glossary →, templates and support for modern AI workloads.

The goal is not to centralise everything or add yet another layer of process. The goal is clear standards and smart automation that let teams work autonomously without reinventing the same solutions over and over.

A strong platform teamPlatform teamA platform team builds and runs the internal platform other teams deliver on, so they don't each solve infrastructure, CI/CD and operations alone. In Team Topologies it is one of four team types, alongside stream-aligned, enabling and complicated-subsystem teams.Read more in the glossary → therefore doesn't just build infrastructure. It builds developer experiencesDeveloper experience (DX)Developer experience (DX) is the sum of how it feels and how long it takes for developers to do their job: starting a service, finding documentation, getting feedback from tests and deploying to production.Read more in the glossary →.

Cloud platforms like AWS, Google Cloud and Microsoft Azure have made it possible to scale systems faster than ever. But they have also introduced enormous complexity.

Many developers know the situation: ”cloud native” suddenly means multiple repositories, different CI systems, hundreds of Terraform resources, Kubernetes manifests nobody dares touch and IAM policies only one person in the organisation understands.

That's not where the value is.

Properly built cloud infrastructure should make it easy to deploy, scale and operate applications without every team becoming experts in all the underlying details. The platform should provide confidence through standardised patterns for security, networking, cost control, logging, monitoring and availability.

We build and optimise platforms on AWS, Google Cloud and Azure with a focus on scalability, security, cost efficiency, automation and high availability. But above all with a focus on real usability.

Because a platform nobody wants to use creates no value, no matter how technically advanced it is.

Terraform has become a standard for infrastructure as code for good reason. When infrastructure is defined declaratively, teams get version control, code review, reproducible environments, clearer change management and less dependence on manual intervention.

It also creates a shared model where infrastructure is managed with the same quality and control as application code.

But Terraform can be used well or badly.

One common trap is building enormous monolithic Terraform projects that over time become hard to understand, test and change. Another is creating modules that try to solve everything and end up requiring specialist knowledge just to be used.

Our experience is that good Terraform is built from small, clear modules with a bounded responsibility. They should expose few inputs, be easy to understand and make the right way the easy way.

Environments should also be clearly separated. Explicit state files, clear pipelines and controlled flows beat ”workspace magic”, manual overrides and copy-paste configuration nobody dares change anymore.

Terraform without automation quickly becomes risky. The platform should support pull request plans, policy checks, drift detection and automatic documentation. Then infrastructure as code isn't just a way of creating resources, but a way of working that gives safer changes and faster onboarding.

Kubernetes is one of the most powerful tools in modern infrastructure. It's also one of the most overused.

Many organisations adopt Kubernetes before they actually need it. The result is often a platform that demands more energy than it gives back. It's rarely Kubernetes itself that's the problem. It's everything around it: too many operators, too many bespoke solutions, too many ways to deploy and too few shared standards.

Used right, Kubernetes can deliver serious impact. It enables scalability, workload isolation, standardised deployment strategies, efficient resource usage and better conditions for both multi-cloud and modern application architectures.

The key is not letting every team do everything its own way.

A good Kubernetes platform should offer golden pathsGolden pathA golden path is a ready-made, recommended, self-service route through a common developer workflow — for example from new repo to service in production — with standards, security and automation built in.Read more in the glossary →, standard templates, safe defaults, recommended CI/CD flows, observability from the start and clear guardrails for how applications are built, deployed and operated.

Then Kubernetes isn't something developers struggle with. It's a stable foundation that helps them deliver.

CI/CD should reduce lead time, not create bureaucracy.

A good pipeline gives fast feedback, few manual steps and a clear path from code to production. A bad pipeline makes developers uncertain, slow and afraid of releases.

With tools like GitHub Actions, GitLab CI/CD and Argo CD we build automated delivery flows that give shorter lead times, safer releases, easier rollback and higher developer productivity.

What matters most is often not which tool you choose, but how the flow feels in practice.

Builds should be fast. Tests should run in parallel where possible. Caching should be used aggressively. Feedback should arrive within minutes, not after lunch.

We also see that trunk-based developmentTrunk-based developmentTrunk-based development is a way of working where all developers integrate small changes into a shared main branch (trunk) several times a day, instead of working for long periods in separate feature branches.Read more in the glossary → and small, frequent deploys almost always work better than long feature branches and big release bundles. Shorter feedback loops reduce merge conflicts, integration problems and release anxiety.

GitOpsGitOpsGitOps is a way of working where Git is the source of truth for both infrastructure and application configuration: desired state is described in code, reviewed in pull requests and rolled out automatically by tools that keep reality in sync.Read more in the glossary → with, for example, Argo CD can bring better traceability, declarative deploys and easier rollback. But it doesn't have to be used everywhere. Simplicity often wins. The right level of automation is the one that makes the delivery process fast, stable and predictable without adding unnecessary complexity.

Logs are no longer enough.

Modern systems require observability across metrics, logs, traces, events and sometimes profiling. Tools like Prometheus, Grafana, OpenTelemetry and Datadog can deliver enormous value — but only if they're used to answer real questions.

Observability is not primarily about pretty dashboards. It's about quickly understanding what's happening in the system.

Why did response times get worse? Which service is causing the error? Is the problem tied to a deployment, a specific customer, a database query or an external integration? How are users affected?

Good tracing can save hours or days of troubleshooting. The right metrics can reduce MTTR and make incidents less dramatic. Clear alerts help teams act before customers are affected.

But observability also creates product value. When teams understand system behaviour better, they make better technical and business decisions.

Developer experience is often the difference between a platform that gets used and a platform that gets bypassed.

Poor DX shows up quickly: long onboarding, local environments that never work, deploy processes people fear, internal documentation nobody can find and tribal knowledge that lives in the heads of a few people.

Good DX means standardised workflows, self-service, clear templates, documentation that works, fast feedback and thoughtful defaults.

The best internal platforms feel almost invisible. The developer doesn't need to understand the whole Kubernetes cluster, configure IAM by hand, write deployment YAML from scratch or memorise internal scripts. The platform removes repetitive work and makes the right thing easy to do.

This isn't just a technical improvement. It's a competitive advantage.

When developers don't have to spend time on manual, repetitive and error-prone steps, they can focus on creating business value. Onboarding gets faster. Delivery gets steadier. Operations get safer. Teams become more autonomous.

AI is rapidly changing what modern infrastructure needs to handle.

It's no longer just web apps, APIs and databases. More organisations now need to run GPU workloads, model serving, embeddings, vector databases, batch inference and real-time inference.

That puts new demands on the platform.

GPU resources need efficient scheduling. Models need to be deployed, monitored and updated. Data flows need to be robust. Costs need to be controlled. Latency, scaling and security become central concerns.

We work with modern solutions for GPU workloads, model hosting, batch inference, real-time inference, embeddings and semantic search using technologies like NVIDIA CUDA, Ray, Kubeflow and relevant cloud services.

For AI applications we also build solutions with vector databases like Pinecone, Weaviate and Qdrant. That enables semantic search, Retrieval-Augmented Generation, intelligent AI assistants and scalable handling of embeddings.

But just as with Kubernetes, the same principle applies: the technology should solve a real problem. Not be introduced because it's trendy.

We believe in pragmatic solutions over over-architecture.

That means simplicity where possible, automation where it creates value and standardisation without limiting teams unnecessarily. It also means the platform must be built with respect for the organisation's reality: existing systems, team skills, security requirements, budget, delivery pace and business goals.

A good platform is not the one with the most components. It's the one that makes it easier to deliver.

In practice, that means we help organisations to:

• build stable, scalable cloud infrastructure
• introduce or improve infrastructure as code
• create Kubernetes and container platforms developers can actually use
• automate CI/CD flows
• improve observability and incident management
• create self-service and better developer experience
• build infrastructure for AI workloads and semantic search
• reduce operational complexity without losing control

The right platform is not just about technology. It creates the conditions for faster time-to-market, safer operations, better developer experience and sustainable scaling.

When platform engineering works well, the organisation gets shorter development cycles, higher delivery capacity, more stable systems and less dependence on manual processes. Teams can move faster without compromising security, quality or control.

That's where modern platform engineering makes a real difference.

Not through more layers of abstraction for abstraction's sake. Not by making the infrastructure more impressive than it needs to be. But through thoughtful standards, smart automation and tools that help developers focus on the right problems.

The best platforms are rarely the most complex.

The best platforms are the ones that let teams deliver without having to think about the infrastructure all the time.