Platform Engineering and AI Infrastructure

Building the internal platform that lets developers do the everyday work themselves: create a repository, a wiki space or a project, get access, start a new service from a template, and ship through a pipeline that already has the tests and checks in it. The platform is run like a product, with internal users, a backlog and measured outcomes, instead of as a ticket queue in front of the operations team.

When the same requests keep reaching the operations team: new repositories, new environments, tool access, pipelines copied from the last project and changed by hand. At that point the time spent waiting costs more than building the self-service. Below it, good templates and a written runbook are cheaper, and we say so in the first week of an engagement.

From the pipelines, not from a survey. Build time, deployment frequency, lead time for changes, test-execution time and change failure rate, captured automatically and reported per team and per line of business. Our engineers built exactly this reporting for a 20,000-person engineering community. Adoption is the other number: if teams route around the paved road, the road needs work.

Not necessarily. A portal is only the front door; the value is in the automation behind it that creates the repository, the space or the project and grants the access. A full portal product suits organisations with many teams and the people to maintain it. A smaller self-service app, or templates and pipeline triggers, often does the job with far less upkeep. We pick based on how many teams will use it and who will own it.

The same paved road applied to the org. Scratch orgs and sandboxes provisioned on request, metadata and second-generation packages deployed through a pipeline on DevOps Center or your existing tooling, Playwright test suites running on every change, and a release process your admins run themselves. The integration services and data pipelines around the org sit on the same road, with the same delivery metrics.

Yes. We serve open-weight language models from GPU instances inside a client's own AWS account today, with no third-party endpoint in the serving path, so prompts and documents never leave their network. The model runs behind a private address the application calls, the instance is started on demand and stopped when idle, and the whole thing is defined as code so it can be rebuilt or moved.

Cost: the instance only runs when there is work. A small control plane starts it on request, watches for idle time and stops it, so the bill follows usage rather than the calendar. Capacity: GPU instance types are often sold out in a given zone, so the same control plane keeps a baked image and launch template ready and rotates through zones, and regions if needed, until one comes up, with a stable address in front so the application never has to change.

Start with a hosted API if your data can go there and your volume is low; it is the fastest way to find out whether the use case works. Move to your own GPUs when the data is regulated, when the monthly API bill passes what a GPU instance would cost, when latency to the provider is a problem, or when you need a specific open model. We build both paths and we write down the crossover point for your workload so the decision is a number, not a preference.

Not always, and we say so when the answer is no. For a handful of services on AWS, the managed container and serverless services cost less to run and have far less to operate. Kubernetes earns its place with many services and several teams, a platform that has to run on-premises or in more than one cloud, tooling that assumes it, or a VM fleet that now costs more than a well-run cluster would. The first week of an engagement answers this in writing.

EKS unless something stops you. AWS runs the control plane, upgrades are a managed operation and the surrounding services are already there. Your own hardware makes sense when data cannot leave the building, when a residency rule says so, or when you already own a fleet and the cost comparison comes out in its favour. We have built both, and the delivery, observability and upgrade tooling is the same on each.

The upgrade is a pipeline, not a runbook someone follows by hand. It drains one node at a time, waits for the workloads to reschedule, checks that no service has dropped below its minimum replicas, and only then moves on. Every step is idempotent, so a failed run can be restarted from where it stopped. On the 57-node on-premises cluster a version moves in an afternoon.

Yes, with namespaces, resource quotas, network policies and role bindings set up so each tenant sees only its own workloads, and cost reported per namespace so each team sees its own bill. The EKS platform for a 20,000-person engineering organisation ran Jira, Confluence, GitLab, Nexus and SonarQube as tenants of one cluster with single sign-on across all of them.

Your team, and the platform is built on that assumption. Everything is in the repository, every alert has a runbook, upgrades and backups run as pipelines, and knowledge transfer sessions happen before go-live rather than after. Thirty days of support after go-live is inside the price, and if you want us to keep running it, that is a separate conversation, not a dependency we built in.