DevOps Services That Leave You With Infrastructure Your Own Team Can Run

The infrastructure review is free. A dedicated senior platform engineer is $4,800 a month, about £3,800. Managed DevOps with an SLA starts at $3,200 a month. Most transformation engagements run three to five months.

We typically find 20 to 40 percent, mostly through rightsizing, commitment planning, storage lifecycle policies and switching off things nobody owns. The free review gives you a specific estimate for your account. Occasionally the answer is that your bill is already tight, and we say so.

Probably not, and that is an unusual thing for a DevOps vendor to say. Kubernetes earns its place with genuine multi-service complexity or scaling requirements. For a single application, managed container services or even a well-configured VM setup is cheaper to run and far cheaper to be on call for.

The cost work usually shows up on the following month's bill. Deploy frequency and lead time improve meaningfully by week eight, once infrastructure as code and CI/CD are in place. We baseline DORA metrics at the start so the improvement is a number rather than a feeling.

That is the explicit goal. We pair rather than deliver in isolation, use only standard tooling, write runbooks as we go, and finish with a shadow on-call period where your team drives. If you cannot run it without us, we did the job badly.

Scoped, time-boxed credentials in your own accounts, with all activity logged. We do not ask for root or permanent admin keys, and you should be wary of any vendor that does.

Yes, the technical half. Policy as code, secrets management, vulnerability scanning in CI, access reviews and evidence generated continuously rather than assembled the fortnight before an audit. We are not an auditor and will not sign anything off.

Yes, as a tier on the managed DevOps plan. For many teams a business-hours SLA with a documented escalation path is sufficient and considerably cheaper, and we will tell you which you actually need.

Some of it is. Log and trace clustering genuinely shortens incident investigation, and cost anomaly detection catches spend changes before month-end. What we will not do is let an AI system take remediation actions in production unsupervised. It suggests, a human approves, everything is logged.

Usually whichever one your team already knows. Migration costs are real and the feature differences rarely justify them. If you are starting fresh, AWS has the deepest ecosystem, Azure fits Microsoft-heavy estates, and GCP is strong for data workloads.

Frequently, and it is often the best arrangement. We come in for a specific capability, or to give an independent view of work already in flight. We will tell you honestly where we think your team already has it right.

Deployment frequency, lead time for changes, change failure rate and time to restore service. They are the most reliable available measure of delivery health, they are hard to game, and reporting them monthly keeps the engagement accountable to something other than our own opinion.