Lilly Tech Systems
Build an AI API Gateway
Build a production-ready AI API gateway from scratch. Route requests across OpenAI, Anthropic, and local models with fallback chains, rate limiting, semantic caching, cost tracking, and an admin dashboard — all with full working code.
Project Build Path
Follow these lessons in order to build the complete project step by step, or jump to any section you need.
1. Project Setup
Architecture, FastAPI and Redis setup, and project scaffolding.
2. Multi-Provider Routing
OpenAI, Anthropic, local models, and fallback chains.
3. Rate Limiting
Per-user, per-team, and token-based rate limits.
4. Semantic Caching
Embedding-based cache and hit rate monitoring.
5. Cost Tracking
Per-request costs, department budgets, and alerts.
6. Admin Dashboard
Usage stats, cost reports, and provider health.
7. Enhancements
PII filtering, audit logging, multi-region, and FAQ.
What You Will Build
By the end of this project, you will have a fully functional application that can:
Route Across Providers
Send requests to OpenAI, Anthropic, or local models with automatic fallback on failure.
Enforce Rate Limits
Control API usage per user, team, and token count with Redis-backed rate limiting.
Cache Semantically
Use embedding similarity to cache and return similar previous responses, reducing costs.
Track Costs
Monitor per-request costs, enforce department budgets, and alert on spending anomalies.