Production LLM costs spiral quickly. This talk shares the cost-control patterns that work at enterprise scale: model tiering and routing with LiteLLM/Portkey, semantic caching with GPTCache, prompt compression, batch inference where appropriate, eval-driven model downgrades, and the observability discipline (Datadog LLMObs, Langfuse) that surfaces cost regressions before the bill arrives.
Takeaways: A model-routing decision framework. Semantic-caching patterns that actually save money. A cost-observability checklist for production LLMs.

Preferred length: 30 min.
Audience: AI engineers, engineering managers, AI platform leads.
Level: Intermediate.
First public delivery: 2026.