Cloud & DevOps

Overview

Infrastructure should be predictable. We design, deploy, and automate cloud environments that scale from early-stage to enterprise. Whether you're migrating from on-prem, consolidating cloud accounts, or building a new platform, we focus on infrastructure as code, repeatable deployments, and observability that surfaces problems before users notice.

Most teams we meet don't need a platform department—they need a small set of things that work every time: deployments that don't require a specific person, environments that can be rebuilt from code, alerts that fire before customers email, and a cloud bill someone actually understands. That's the outcome we engineer, sized to your team rather than to a conference talk.

Capabilities in Detail

Cloud migration from on-prem or legacy cloud—we plan phases, minimize downtime, and validate data integrity. Every migration gets a rehearsal on non-critical workloads, a measured cutover window, and a tested rollback path before anything production-critical moves.

Infrastructure as code with Terraform or Pulumi so environments are reproducible and versioned. Staging stops drifting from production, new environments spin up in minutes, and every infrastructure change is reviewed in a pull request instead of applied by hand in a console.

CI/CD pipelines that build, test, and deploy on every merge. We tune for fast feedback—caching, parallel jobs, preview environments per pull request—because a pipeline the team waits on is a pipeline the team routes around.

Container orchestration with Kubernetes or managed services when you need portability and scale. Just as often we'll recommend the simpler runtime—Cloud Run, Fargate, or Vercel—because the right amount of orchestration is the least you can operate confidently.

Monitoring and observability—metrics, logs, traces—so you know when something breaks. We define alerts on symptoms users feel rather than every metric that twitches, and wire dashboards to the questions you ask during an incident.

Security and compliance baked into design: least privilege, encryption, and audit trails. SSO across your tooling, scoped IAM roles, secret rotation, and the evidence trail that makes SOC 2 or ISO conversations shorter.

Our Approach

We automate everything that repeats. We prefer managed services over self-hosted when the tradeoff favors reliability. We document runbooks and failure modes. We design for rollback—every deployment should be reversible. And we treat cost as an engineering constraint: right-sized instances, sensible storage tiers, and a bill reviewed like any other metric.

How an engagement runs

1. Infrastructure review (weeks 1–2). Read-only access first: we map what exists, how it deploys, what it costs, and where the single points of failure hide—including the ones only one employee knows about.
2. Target architecture and plan. A written design with phases, effort, and tradeoffs—what moves, what gets rebuilt, what should simply be deleted. You approve the plan before anything changes.
3. Phased execution. Non-critical workloads first, validation gates between phases, and production cutovers in agreed windows with rollback rehearsed—not improvised.
4. Handover and runbooks. Documentation, incident playbooks, and working sessions with your team so the infrastructure isn't a black box we've left behind.
5. Ongoing operations (optional). A monthly retainer for patching, cost reviews, incident support, and the steady stream of small improvements infrastructure always needs.

A CI/CD and infrastructure-as-code baseline typically lands in two to four weeks; a full migration runs six to twelve weeks depending on how much state has to move. Scope and fees are set after the review call.

Platforms and tooling

AWS and GCP for core infrastructure, Vercel for frontend and full-stack workloads that benefit from its preview-deployment flow. Terraform and Pulumi for infrastructure as code, Docker for packaging, Kubernetes (EKS, GKE) where orchestration earns its complexity—Cloud Run and Fargate where it doesn't. GitHub Actions for CI/CD, Prometheus, Grafana, and OpenTelemetry for observability, Sentry for application errors, and provider-native secret managers everywhere. We also use Claude to keep the unglamorous parts honest—generating runbooks from real incident history and summarizing infrastructure changes for review—because documentation that writes itself is documentation that exists.

FAQs

AWS, GCP, or multi-cloud?
We work across providers. We recommend a primary cloud for most workloads and add multi-cloud only when you have clear requirements—compliance, vendor lock-in, or geographic distribution.

How do you handle secrets and credentials?
We use provider-native secret managers (AWS Secrets Manager, GCP Secret Manager) and never commit secrets. We rotate credentials on a schedule and audit access.

What does a typical migration look like?
We assess the current state, define target architecture, and execute in phases—often starting with non-critical workloads. We validate each phase before moving to the next.

Do we actually need Kubernetes?
Often, no. If a managed runtime can carry your workload, we'll say so—it's less to operate and less to page you at night. We reach for Kubernetes when scale, portability, or workload shape genuinely demands it, and then we run it with managed control planes.

Can you reduce our cloud bill?
Usually, yes. Cost review is part of every infrastructure review: right-sizing, storage lifecycle policies, reserved capacity, and deleting the zombie resources every account accumulates. Savings vary, but "nobody knows what this instance does" is a line we hear a lot—and retire often.

Do you hand over or stick around?
Your choice. Some teams take the keys after handover and runbooks; others keep us on retainer for operations, cost reviews, and incident support. Either way the infrastructure is defined in code you own, so switching modes later is easy.