AI Infrastructure, MLOps & DevOps Consulting
AI workloads break normal cloud assumptions: GPU scheduling, token-cost economics, and latency budgets that user-facing inference can't miss. We build the infrastructure layer that makes AI features fast, observable, and affordable.
What it includes
Low-latency serving on AWS SageMaker, GCP Vertex, or Kubernetes with autoscaling, caching, and fallback models — sized for your traffic, not the vendor's demo.
Pipelines on GitHub Actions and ArgoCD that gate deploys on eval scores, not just unit tests — prompts and models ship with the same rigor as code.
Token budgets, semantic caching, model tiering, and GPU spot strategies — visibility into cost per feature before the invoice teaches you.
Tracing across the full request path — prompt, retrieval, inference, post-processing — with quality signals, not just uptime.
What you get
What lands in your hands at the end, not activity reports.
- Infrastructure as code (Terraform) for the full serving stack
- CI/CD pipelines with eval gates and one-command rollback
- Cost dashboard with per-feature and per-model breakdowns
- On-call runbook covering the failure modes we tested
Where it is used
- 01A product team shipping LLM features weekly without regression roulette
- 02Batch scoring pipelines that spin up GPU capacity only when queues demand it
- 03A finance-visible dashboard showing exactly what each AI feature costs to run
Tools and platforms we work with
Solutions that use it
The same service, framed by the problem it solves.