Overview
AI products in production need a different operational discipline than traditional software. Model drift, inference costs, deployment safety, and continuous retraining are real challenges that compound at scale. We build the cloud infrastructure and ML operations layer that keeps your AI product reliable and economical as you grow.
Why this matters
An AI product that costs $0.02 per query at 10k requests/day quietly becomes $20,000/month at 1M requests/day. Without MLOps discipline, 30–50% of your AI spend is waste — and you only find out when your CFO flags the invoice. We prevent that.
How we run it
Infrastructure Audit
Map your current compute, storage, and inference costs. Identify where caching, batching, or model-swapping can reduce spend without hurting quality.
CI/CD for AI
Build deployment pipelines that test prompts, retrieval quality, and latency before changes hit production. No more 'the prompt change broke everything in prod.'
Observability
Set up AI-specific observability — prompt logs, token usage, latency percentiles, hallucination rate — with alerts that fire before customers notice.
Cost Optimization
Implement caching, smart model routing (cheap model by default, premium for hard queries), and request batching. Typical outcome: 40–60% cost reduction at iso-quality.
What you get
- Cloud architecture design — AWS, GCP, or Azure
- CI/CD pipeline implementation for AI products
- Model versioning, registry, and rollback capability
- Inference infrastructure optimization for cost and latency
- Automated testing for ML pipelines
- Deployment runbooks and on-call documentation
Our technology choice
Kubernetes where scale demands it, serverless where it doesn't. Datadog or Grafana for observability. LangSmith or Helicone for LLM-specific monitoring. We pick the stack that matches your team's ops maturity, not the one that looks best on a resume.