Production AI evaluation: latency, token cost, throughput and failure rates
goyalshaliniuk · x · 2026-10-13
Part of a thread on evaluating AI applications before production. This piece covers performance & cost: quality alone doesn't make a system production-ready — measure response latency, token consumption, cost per task, throughput, and timeout/failure rates. A companion fragment adds robustness evaluation: real user inputs are messy, so test misspellings, ambiguous instructions, unexpected formats, missing info and adversarial inputs to surface edge cases early.
Related event: Evaluating Production AI: Performance, Cost and Robustness(2 posts)→
More from coding & agent
- MCP for tools, A2A for agent-to-agent: is anyone actually running both? — SnooDingos9560 · 2026-10-13
- How do you eval agents when outputs are never the same twice? — SnooDingos9560 · 2026-10-13
- Indie dev ships hosted scraping MCP: pay nothing when sites block you, 1000 free credits — SidMishraDev · 2026-10-13
- Cursor's new pricing: Claude and other third-party models billed at API rates, plans include only Grok — iannuttall · 2026-10-13
- Dev: vibe coders with Claude can't yet beat 15-year principal engineers — but he expects yes within a year — facontidavide · 2026-10-13
- Open-Source MCP Security Gate Blocks Prompt Injections in Under 5ms — EstablishmentTough18 · 2026-10-13