AI workloads punish poor foundations. We design and build the cloud substrate that scales with the leap — secure by construction, observable by default, economical at the scale your models demand.
The cloud bills of the AI era are not the cloud bills of the SaaS era. Inference rewrites the unit economics of every workload it touches.
Most cloud estates were sized for predictable, mostly-stateless services. AI estates need GPU density, low-latency retrieval, ruthless cost discipline, and observability that goes deeper than logs.
Our cloud practice is not commodity. It is the foundation that decides whether your leap actually lands.
From greenfield landing zones to deeply-entangled modernisation programmes — built to a single bar.
Lift-and-shift considered harmful. We re-architect for elasticity, governance, and the compute density that inference workloads require — across the long-tail estate, not just the green-field.
Account hierarchy, network topology, identity, guardrails. AWS, Azure, GCP — designed for the workloads, not the brochure. Built to pass the audits you have not had yet.
Kubernetes, service mesh, secrets, GPU-aware scheduling, ingress, certificate lifecycle. The substrate that makes everything else possible — and stays out of the team's way.
Token economics, GPU rationalisation, commitment shape, autoscaling fences. AI bills are the new headline risk; we design them down — without mortgaging future flexibility.
Zero-trust patterns, workload identity, supply-chain attestation, secrets, KMS hierarchy. Compliance becomes a side effect, not a project.
OpenTelemetry-first telemetry, SLOs that mean something, error budgets that get respected. The plumbing that makes 24×7 operations possible.
We are technology-agnostic, not technology-naïve. This is the broad surface our partners spend most of their time on.
The cloud is no longer the leap.— Cloud Engineering at Revoleap
It is the floor.
A 90-minute working session with our cloud principals — landing-zone review, cost benchmark, GPU readiness.
Begin a conversation— The strategy at the top is decided by the leadership team. The operating model in the middle is what most transformations under-engineer. The platform at the bottom is where engineering teams spend 80% of their time. We work from the top down and the bottom up at the same time.
