ServicesSupporting practice
Cloud & DevOps for AI
AI workloads fail in infrastructure ways: rate limits under load, queues backing up, deploys that silently break retrieval, bills that surprise. We build the platform layer that keeps AI systems up and affordable — and we run it after launch if you want us to.
In production that has meant idempotent consumers, retries with backoff, dead-letter queues and backpressure that cut 429 incidents 55% and doubled ingestion throughput; CI/CD for prompts, models and indexes, not just code; and observability that answers 'why did this answer come out wrong' rather than just 'is it up'.
What's included
Named deliverables, not vague verbs
Cloud architecture
Azure and AWS — containers, serverless, queues, storage — designed for the workload you have.
Resilience engineering
Idempotency, retries, DLQs, backpressure, rate-limit handling. Boring on purpose; boring is what uptime looks like.
CI/CD & infrastructure as code
GitHub Actions, Azure DevOps, Docker, Kubernetes, Bicep — deploys that are reviewable and reversible.
Observability & cost control
Tracing, eval-aware monitoring, spend caps and budget alerts across model and cloud bills.
The evidence
Where we've done this before
Stack for this practice
What it's built with
- Azure
- AWS
- Docker
- Kubernetes
- GitHub Actions
- Azure DevOps
- Bicep
- Terraform
- Datadog
- OpenTelemetry
Evaluator questions
Asked by every serious buyer
Can you work inside our tenant and security review?
Yes — several engagements have been delivered entirely inside the client's own Azure tenant, against their data, through their security review.
Ready to scope cloud & devops for your business?
Thirty minutes with a founder. You leave with an honest read on feasibility, a first-step recommendation, and no obligation.