Every production AI system needs five layers of operations. Here's the landscape:
┌─────────────────────────────────────────────────────────────────┐
│ AI OPS STACK │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 5: COST & GOVERNANCE │
│ Token budgets, usage attribution, billing alerts │
│ Tools: Custom dashboards, provider billing APIs │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 4: QUALITY & EVALUATION │
│ Automated evals, LLM-as-judge, A/B testing │
│ Tools: RAGAS, TruLens, Braintrust, custom eval pipelines │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 3: TRACING & OBSERVABILITY │
│ Request tracing, prompt/response logging, latency tracking │
│ Tools: Langfuse, LangSmith, Arize Phoenix, Helicone │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 2: CI/CD & TESTING │
│ Prompt testing, eval gates, model version management │
│ Tools: GitHub Actions, Promptfoo, custom test harnesses │
├─────────────────────────────────────────────────────────────────┤
│ LAYER 1: INFRASTRUCTURE │
│ Model serving, vector DB, caching, rate limiting │
│ Tools: Vercel AI SDK, Pinecone, Redis, API gateways │
└─────────────────────────────────────────────────────────────────┘