Scaling AI Workflows: Lessons from 1 Billion Requests

Scaling an AI platform is unlike scaling a traditional web application. The bottlenecks are different - LLM inference latency, vector search throughput, and workflow orchestration complexity all present unique challenges.

Our architecture centers on async execution. Every workflow step runs as an independent task in a Redis-backed job queue. This enables horizontal scaling - we simply add more workers to handle increased load, without any single point of contention.

LLM provider diversity is a scaling strategy, not just a feature. By distributing requests across 9+ providers with intelligent routing and automatic fallback, we maintain sub-second response times even when individual providers experience degradation.

Database optimization was crucial. PostgreSQL with pgvector handles both relational data and vector embeddings. Careful index tuning, connection pooling, and read replicas keep query latency under 10ms even at scale.

Observability is non-negotiable at scale. Every request generates a distributed trace that follows it through the entire system - from API gateway through workflow engine to LLM provider and back. This makes debugging production issues tractable.