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Both models are supported, and the choice is usually a capacity question rather than a capability one. Most clients start with us building the platform and running it, while their engineers work in the same repositories and reviews from day one. Enablement is part of the engagement, not an afterthought: paired delivery, documented runbooks and a defined handover path if you want to bring operations in house later. If your intent is to build an internal platform team, say so early, it changes how we structure the work.

AWS, Azure and Google Cloud, with EKS, AKS, GKE, Red Hat OpenShift and self managed Kubernetes on VMware or bare metal. Tooling is standard CNCF aligned, Terraform, Helm, Argo CD or Flux, Prometheus and Grafana, OpenTelemetry, so nothing here locks you to us. Multi cloud is achievable, but it is a cost and complexity decision, not a default. We’ll model what portability actually costs you in practice before recommending it.

We assess the estate and sort applications into rehost, replatform and refactor, then sequence by business risk and effort. The first wave is deliberately low risk, stateless services with clear boundaries, so the platform proves itself before anything critical moves. Legacy and stateful workloads stay where they are until there’s a business reason to move them. Some never should. A platform that runs alongside your existing estate is a normal, and often correct, end state.

Controls are codified rather than documented: image scanning and signing in the pipeline, SBOMs per build, OPA or Kyverno admission policy, network policy by default, secrets managed in a vault, and runtime threat detection in the cluster. We map controls to the frameworks you’re assessed against, ISO 27001, SOC 2, PCI DSS, GDPR and data residency requirements, and the platform generates the evidence continuously, so audit preparation stops being a project.

It will if nobody owns utilisation. Default requests and limits, over provisioned node pools and idle non production environments are where container spend leaks, and the cluster won’t flag any of it for you. Cost engineering is in scope from day one: right sizing from actual usage data, autoscaling and scale to zero for non production, spot and committed use strategy, and namespace level showback so each team sees its own consumption. Clients typically see 25–40% lower container run cost against their baseline before the platform.

Two components. The platform build is a fixed scope, fixed price engagement with defined deliverables and gates. Ongoing operations are priced per environment and cluster tier, or as a dedicated pod where you need reserved capacity and named engineers. Cloud consumption stays in your own accounts and is billed directly to you, we don’t resell your infrastructure or mark it up. During the review we’ll model build plus run against your current spend so you can see total cost, not a day rate.

Everything we build lives in your repositories, your cloud accounts and your identity provider, using open, widely adopted tooling. There are no proprietary Teceze components in the delivery path. Exit terms, documentation standards and a knowledge transfer plan are written into the contract from the start. Ask every platform vendor you shortlist for the same, it’s the clearest test of whether you’re buying capability or dependency.