DevOps Services

Typical timeline: 1-2 weeks audit + target architecture, 4-8 weeks incremental migration service-by-service (with parallel run on the legacy stack), 2-4 weeks cutover and decommissioning. Full Kubernetes migration with ArgoCD GitOps, Helm charts and observability typically runs 3-6 months for a mid-size SaaS platform. We do not recommend big-bang cutovers - every service migrates on its own timeline with its own rollback plan.

Typical first-year savings: 30-55% on compute through right-sizing + Savings Plans + spot for batch workloads, 20-40% on storage through lifecycle policies, 15-30% on data transfer by fixing architectural issues. Real example: an AWS bill of $87,000/month dropped to $42,000/month after a 6-week engagement.

Use Kubernetes when you have multiple services with independent scaling needs, multiple teams deploying independently or specialized workloads (GPU, spot instances, stateful services with complex orchestration). Do NOT use Kubernetes if your team has fewer than 3 engineers who can debug it at 3am, if a managed PaaS covers your needs or if you want to use it as a resume-builder. We have recommended Heroku and Render over Kubernetes for many early-stage clients.

Yes, and we recommend them for every production service. SLOs reframe alerting from "alert on 5xx errors" to "alert when burn rate threatens monthly error budget".

DR planning is part of discovery, not a late-stage add-on. Targets: Recovery Time Objective (RTO, how fast to restore service) and Recovery Point Objective (RPO, how much data loss is tolerable).

We offer SRE retainers with tiered coverage. Tier 1 (business hours, 4h SLA): $8,000/month. Tier 2 (24/7 monitoring, 1h SLA on P1): $18,000/month. Tier 3 (dedicated SRE with on-call rotation): $35,000/month. All tiers include monthly reliability reviews, quarterly SLO retrospectives and a shared runbook repository.

AWS for the deepest service catalog and mature enterprise features. GCP for BigQuery + Kubernetes + ML tooling. Azure for .NET-heavy enterprises and Microsoft integrations. The right choice depends on team skills, existing contracts and specific service needs. We are not vendor-exclusive - we run production workloads on all three and will recommend the one that fits your team and workload, not the one with the biggest kickback.

The scope covers CI/CD pipeline design, infrastructure as code (Terraform), container orchestration on Kubernetes or a managed PaaS, observability with metrics, logs and traces, incident response with SLOs and error budgets and cloud cost management. Engagements range from a one-off pipeline or migration project to an ongoing SRE retainer. We shape the scope to the reliability or cost problem you actually have, not a full platform rebuild by default.

CI/CD (continuous integration and continuous delivery) automates building, testing and shipping code so a change reaches production in minutes with a repeatable, auditable path. Continuous integration runs the test suite, linters and security scans on every pull request, so problems surface at merge time. Continuous delivery packages and deploys the passing build through staged environments with health checks and automatic rollback. The payoff is faster releases, fewer manual mistakes and a deploy any engineer can trust.

Infrastructure as code means your servers, networks and cloud resources are defined in version-controlled files rather than clicked together by hand, so every environment is reproducible and every change is reviewed like application code. Terraform is our default, with Pulumi when a team prefers a general-purpose language. Modules are reused across environments, state is stored remotely with locking and plans run in CI so nobody applies an unreviewed change to production.

Every critical service ships with metrics (Prometheus), structured logs and distributed traces (OpenTelemetry) wired into dashboards and SLO-based alerts, so on-call gets paged on user-facing symptoms rather than raw error counts. Runbooks live next to the alerts. After a P1 we run a blameless postmortem with tracked action items, and we tune alert thresholds so the on-call rotation keeps its trust in the pager. Alert volume typically drops by half once SLOs replace threshold alerts.

Usually yes, and that is the cheaper path. Most engagements open with an assessment of your current pipeline, infrastructure and incident history, then target the highest-impact fixes: flaky or slow CI stages, missing rollback, manual deploy steps, weak observability or a cost hotspot. We rebuild only when the existing setup blocks the goal outright. You get a prioritized roadmap with effort and payback so you can decide what is worth doing before we touch anything.

We decline managed-platform use cases where Heroku/Render/Vercel would solve the problem, Kubernetes adoption without operational capacity, multi-region projects for single-region apps and observability projects where the client wants dashboards but not SLO accountability. We also decline "modernization" work without a measured reliability or cost problem - if it is not broken, do not refactor it.