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Production AI14 min read·Mar 10, 2026
Production AI Monitoring & Observability: Tracing, Latency SLAs & Cost Caps
Setting up OpenTelemetry, LangSmith, semantic caching with Redis, and automatic circuit breakers.
DK
David Kim
SRE & Infrastructure Lead
Executive Summary
Operating AI applications in production requires real-time observability into token budgets, TTFT (time-to-first-token), ITL (inter-token latency), and semantic cache hit rates.
Essential Telemetry Metrics
Track TTFT, generation throughput (tokens/sec), provider fallback counts, and validation error rates.
Implement semantic caching with Redis vector search to serve repeated queries with sub-10ms response times and zero model token cost.
Key Takeaways
- Track p95 Time to First Token (TTFT)
- Implement semantic caching for common queries
- Configure automatic fallback cascades between model providers