AI DevOps that keeps AI workloads in control
Deployment topology, CI/CD with evaluation gates, observability, cost controls, and secrets management for Claude-powered systems. Built to fit your existing platform, not replace it.
Scope of engagement
- Platform engineering for AI workloads: deployment topology, environments, traffic management, and rollout patterns.
- Infrastructure as code for Claude-powered services across AWS, GCP, Azure, and private clouds.
- CI/CD pipelines with evaluation gates so prompt and model changes cannot ship blind.
- Observability: metrics, tracing, and log pipelines designed for AI-specific signals like cache hit rate, tool outcomes, and cost.
- Secrets, auth, and access control for AI API keys, tool permissions, and customer data flows.
- Cost management and guardrails: budgets, rate limits, and provider-level controls that hold under real load.
Platform discipline that scales with the workload
Running AI workloads in production is a platform problem first. Our team already runs platforms that handle the steep operational requirements of Cassandra and Kafka estates, so the habits we bring — observability that actually answers questions, change control that holds under pressure, cost discipline that does not drift — transfer directly to AI workloads. The result is a platform your team can trust instead of fight.
A predictable path from scope to running system
Assess
Review the existing platform, delivery pipeline, and operational posture. Identify the gaps specific to shipping and running AI workloads.
Design
Target deployment topology, pipeline, observability stack, and control plane. Documented so engineering and security can review before we build.
Implement
Build the platform alongside your team: IaC modules, pipelines, dashboards, guardrails. Everything version-controlled and reviewable.
Embed
Pair with your platform and SRE teams to make sure the practices stick after we leave. Runbooks, on-call integration, and change cadence documented.
What we build with our clients
Platform your team can run
Deployment, observability, and controls that fit your existing practices instead of standing up a parallel AI silo.
Safe change cadence
Prompt, model, and code changes promoted through pipelines with evaluation gates. No silent regressions into production.
Cost and access under control
AI spend visible per workload, budgets and rate limits enforced at the provider level, secrets and permissions managed centrally.
Common questions
Do you work with our existing cloud and platform choices?
Yes. Engagements fit your stack — AWS, GCP, Azure, on-prem, or hybrid — and your existing IaC and pipeline tooling. We do not insist on a bespoke platform.
How is this different from Managed AI Operations?
DevOps focuses on the platform and delivery pipeline: how AI workloads get built, deployed, and observed. Managed Operations is about running those workloads after launch with SLOs and incident response. The two complement each other.
Do you write infrastructure-as-code for us?
Yes. We write IaC alongside your platform team, using whatever tool you already use — Terraform, Pulumi, CloudFormation, or similar. Code is version-controlled in your repositories.
Can evaluation really gate a CI/CD pipeline?
Yes, and it should. Prompt, model, and retrieval changes run against an evaluation set before promotion. We build that gate as part of the engagement.
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