Cloud & DevOps Company in San Francisco

What this costs turns on the shape of the work: a focused SF DevOps engagement covering a Kubernetes platform stand-up on EKS, Terraform-managed AWS landing zone, GitHub Actions or GitLab CI pipelines, OpenTelemetry observability through Datadog or Grafana Cloud, a full platform engineering build with Backstage developer portal, Crossplane or Pulumi self-service infrastructure, Argo CD GitOps, progressive delivery on Argo Rollouts, and feature flags via LaunchDarkly or Statsig, or enterprise-scale migrations off legacy ECS, VMs, or Heroku to Kubernetes with multi-region resilience, FinOps tooling, and FedRAMP Moderate or CMMC 2.0 readiness. SF rates sit above most US markets because of the Stripe, Airbnb, and HashiCorp talent premium, but we deliver from Edmonton and Chandigarh on PST hours so you get Bay Area velocity without Bay Area headcount cost. Fixed-fee proposals in USD, no open T and M.

Stripe, Airbnb, and Slack ship to production multiple times per day on trunk-based development, and the SOC 2 auditors signed off because the control set was redesigned around the pipeline, not against it. Our default pattern is short-lived feature branches with required reviews enforced in GitHub Actions or GitLab CI, automated SAST and dependency scanning on every commit (Snyk, GitHub Advanced Security, or Semgrep), Terraform plan output as the change record, and deploys routed through Argo CD GitOps with LaunchDarkly or Statsig feature flags hiding incomplete work behind controlled rollouts. The audit trail is the merged pull request plus the GitOps commit plus the flag activation log, which Vanta or Drata maps to CC8.1 change management. Auditors at Coalfire and Schellman accept this pattern routinely on SF SaaS engagements.

AWS us-west-2 (Oregon) is the primary default for SF workloads because it has the broadest service availability, the cleanest cost profile, and the latency profile most Bay Area engineering teams already optimize against. AWS us-west-1 (Northern California) covers latency-sensitive edges and disaster recovery. GCP defaults to us-west1 (Oregon) with us-central1 (Iowa) as the multi-region partner where Spanner or BigQuery footprint matters. Azure shows up as West US 2 or West US 3 (Phoenix) when customers landed through enterprise credit agreements. For HIPAA workloads we route through AWS or Azure with BAAs in place and keep PHI inside the customer VPC. For FedRAMP Moderate and CMMC 2.0 scope we move to AWS GovCloud (US-West) or Azure Government, with FIPS 140-3 validated cryptography enabled across the stack.

CMMC 2.0 is the Department of Defense Cybersecurity Maturity Model Certification program, with the final rule effective in 2025, that gates DoD contracts on third-party assessed cybersecurity controls. Bay Area defense and dual-use companies (Anduril in Costa Mesa with Bay Area engineering, Palantir, SpaceX, Shield AI, Saronic, and the broader prime and sub-prime ecosystem) now require CMMC Level 2 alignment from cloud vendors handling Controlled Unclassified Information. We build CUI enclaves on AWS GovCloud (US-West) or Azure Government, map NIST SP 800-171 Rev. 2 controls to platform implementations, run CMMC pre-assessments with a registered C3PAO referral partner, and document evidence in a System Security Plan and Plan of Action and Milestones your assessor will accept. FIPS 140-3 validated cryptography is on by default and the boundary diagram, asset inventory, and audit log retention all line up against the assessor checklist.

Yes. Backstage (originated at Spotify, heavily adopted across SF SaaS including American Airlines, Expedia, and a long list of YC scaleups) is our default developer portal for engineering teams past fifty engineers where service catalog, software templates, TechDocs, and scorecard hygiene start to matter. We stand up Backstage with the GitHub or GitLab integration, wire the Kubernetes plugin for service ownership and SLO views, add scaffolder templates that generate new services with Terraform, Helm, and CI pipelines pre-wired, and integrate cost insights through OpenCost or Vantage. For platform teams that prefer self-service infrastructure as code, we layer Crossplane or Pulumi underneath Backstage so application engineers can request a new RDS instance, S3 bucket, or Kubernetes namespace through a pull request rather than a Jira ticket.

Most SF cloud bills carry twenty to forty percent waste that does not affect the deploy cadence engineering teams care about. Our FinOps engagement starts with Vantage, CloudHealth, or AWS Cost Explorer plus a Kubernetes cost view through OpenCost or Kubecost broken down by team, service, and tenant. Quick wins include right-sizing EC2 and RDS, moving stateless workloads to Graviton, applying Savings Plans and Reserved Instance coverage on the predictable base load, switching cold S3 data to Glacier Instant Retrieval or Glacier Deep Archive, and pruning idle EBS volumes and stale snapshots. Structural wins come from spot capacity on EKS through Karpenter, request and limit tuning on Kubernetes pods, query optimization on Snowflake and BigQuery, and CloudFront caching that pulls origin traffic down. We publish a weekly cost report your CFO and VP of Engineering can both quote in board updates.

A production-ready Kubernetes platform on EKS with Terraform-managed networking, IAM, Argo CD GitOps, Istio or Linkerd service mesh, OpenTelemetry observability through Datadog or Grafana Cloud, and SOC 2 Type II control mapping ships in twelve to twenty weeks. Week 1 to 3 is platform audit, target state design, and Terraform module scaffolding. Week 4 to 10 is cluster build, networking, IAM, base GitOps and CI pipelines, and the first workload migration. Week 11 to 16 is observability, progressive delivery, feature flag wiring, runbook documentation, and SOC 2 evidence packaging. Week 17 onward is the production cutover behind feature flags with shadow traffic comparison. Enterprise scope (multi-region, multi-cluster, federated identity, FedRAMP Moderate) adds eight to twelve weeks. We coordinate with your existing platform team rather than replacing them.

Datadog is the most common observability stack across SF SaaS because the sales motion and procurement defaults make it the path of least resistance, and the APM, RUM, and log management coverage is broad enough that most teams will not justify a switch. Honeycomb is the default for teams that have invested in trace-first debugging culture, especially anything inheriting the Charity Majors-era SRE patterns. Grafana Cloud and self-hosted Prometheus plus Loki plus Tempo show up where cost matters more than turnkey ergonomics, especially for Series A and B teams with strong platform engineers. New Relic and Splunk show up in enterprise environments. We standardize on OpenTelemetry as the instrumentation layer so the backend stays swappable, which matters more than any specific vendor choice once you cross fifty services and the bills start to compound.