Most AI prototypes never reach production. Here's our battle-tested approach to building LLM workflows that handle millions of requests reliably.

Why Most LLM Prototypes Fail in Production


Building an LLM demo is easy. Getting it to handle 10,000 concurrent users with sub-500ms P99 latency, consistent output quality, and observable failure modes? That's engineering.


The Production Stack


Our production LLM architecture includes:


  • Async request queuing with Redis for traffic spikes
  • Response caching for common queries (reduces costs 40-60%)
  • Fallback chains when primary model is unavailable
  • Structured output validation with Pydantic/Zod
  • Prompt versioning tracked in git alongside code

Observability First


Every LLM call should emit: latency, token count, model version, prompt hash, and output quality score. Without this, you're flying blind.


Cost Control


LLM API costs can explode quickly. Implement tiered routing — cheap fast model for simple queries, expensive model for complex reasoning. We typically achieve 70% cost reduction without quality degradation.