Kubernetes, GitOps, IaC, CI/CD, observability, cost guardrails. The boring foundation that prevents 67% of AI production incidents (per our research). In business terms: features reach customers faster, and your cloud bill becomes a number you can plan around.
EKS / GKE / AKS. Karpenter or cluster-autoscaler. Hardened defaults. Multi-environment from day one. We don't build snowflake clusters.
ArgoCD for delivery, Terraform for everything else, Atlantis for PR-driven plan/apply review. Drift detection wired into Slack.
OpenTelemetry traces, structured logs, SLOs and error budgets. Honeycomb / Datadog / self-hosted Grafana stack — your call.
GitHub Actions or GitLab CI. Cached, parallel, fast. Secrets via OIDC, never long-lived. Same pipeline runs in PR and prod.
Budgets per namespace, per service, per AI feature. Alerts on velocity, not just totals. Monthly cost report you can read.
Incident playbooks, on-call rotation setup, post-mortem templates. We oncall with your team during the first launch week.
Console-clicked infra, drifted Terraform, no SLOs. Adding AI workloads to that is asking for incidents you'll diagnose at 2 AM.
A previous team / vendor built something fragile. You need a working version that your team can actually evolve.
Multi-week release trains. Manual deploys. Nobody trusts the build. We get you to same-day deploys without a rewrite.
Cloud bill grew faster than headcount. We instrument, find the leaks, and put guardrails in place — typically 25–40% off the cloud bill within a quarter.
On a production SaaS platform our founding CTO ran, per-service cost instrumentation, rightsizing, and autoscaling guardrails cut cloud spend 45% in a single quarter — while eliminating 97% of recurring infrastructure incidents.
Founder track record, anonymized. Your numbers depend on your starting point — we'll give you a realistic estimate on the intro call.
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