Prometheas Technologies
Product Engineering practiceProduct engineering · Cloud & DevOps

Infrastructure as product, not as scripts.

Most platform teams inherit three generations of ad-hoc infrastructure. We help teams treat the platform itself as a product with users, SLAs, and a roadmap — not a shared spreadsheet of tribal knowledge.

What it is

Cloud, DevOps & platform engineering

Cloud architecture (AWS, Azure, GCP), container and serverless delivery, IaC (Terraform, Pulumi), CI/CD, observability, and the platform-engineering practices that make all of it developer-usable rather than just operator-manageable.

When we recommend it

Fit signals.

  • Your deploy pipeline takes hours, fails unpredictably, and nobody wants to own it
  • You're scaling past 30 engineers and the 'one person who knows the infra' pattern is breaking
  • Cloud costs are growing faster than the business and nobody can say why
  • You're adopting Kubernetes and want to avoid the two-year stabilization tax
  • Observability is three disconnected tools and an incident takes hours to diagnose
Capabilities

What we deliver in Cloud & DevOps.

Every capability below is practiced across multiple production engagements — not a scoping checklist.

Cloud architecture

  • AWS (primary), Azure, GCP — multi-cloud where it earns the complexity
  • Landing zones, account structures, network design
  • Serverless-first for event-driven work, containers for everything else
  • Cost visibility + FinOps practices from day one

Infrastructure as code

  • Terraform (primary), Pulumi, CDK
  • Modular patterns with shared libraries and versioning
  • GitOps workflows — Atlantis, Terragrunt, Terraform Cloud
  • Drift detection + policy-as-code (OPA, Checkov, Sentinel)

Containers & Kubernetes

  • EKS, AKS, GKE — managed by default
  • Service mesh only when it earns the cost
  • ArgoCD / Flux for deployment
  • Pod security, network policies, secrets management that actually works

Observability & SRE

  • OpenTelemetry as the default instrumentation story
  • Datadog, Grafana, Honeycomb, Splunk — picked by use case
  • SLO definition + error budgets + real alerting hygiene
  • Incident response + postmortem culture
Engagement patterns

The shapes this work
usually takes.

Platform rebuild

Typical: 14–24 weeks. IaC, pipelines, observability, and developer experience shipped together.

Cloud cost optimization

Typical: 8–12 weeks. Audit + quick wins + sustained practices. 20–40% savings are common.

Kubernetes adoption

Typical: 12–18 weeks. Managed K8s with sane defaults, IaC, and a team-ready runbook.

Platform managed service

Monthly. Platform-team extension, on-call assistance, roadmap facilitation.

What goes wrong

Pitfalls we've seen
and how we avoid them.

Kubernetes for the sake of it

Teams adopt K8s before they need it and spend a year stabilizing. We'll tell you when a managed PaaS (Fly, Render, ECS) beats K8s.

Observability without SLOs

Dashboards everywhere, nobody on call for anything specific. SLOs give observability a purpose.

IaC as a side project

IaC written but not enforced. Drift accumulates. We make it the only deployment path, not one of several.

FinOps as an after-the-fact surprise

Cost alerts retrofitted after a billing shock. Tags + budgets + policy-as-code in week one.

FAQ

Common questions about Cloud & DevOps.

We support it; we don't recommend it by default. Multi-cloud earns its complexity for specific regulated or sovereignty requirements, not for vendor-leverage reasons. Most of our engagements are AWS-primary.

Other Product Engineering modules

Cloud & DevOps on your roadmap?

Thirty minutes with Kabir. Architecture sketch, candid second opinion, scope estimate — no slides.

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