LLM Gateway — AI Infrastructure

Problem

Organizations using multiple LLM providers (OpenAI, Anthropic, etc.) face fragmented integration, inconsistent rate limiting, duplicated cost tracking, and no unified observability. Each provider has different APIs, pricing models, and rate limits, creating operational complexity that grows with each new model adoption. A unified gateway abstracts these differences behind a single API.

Architecture Overview

The gateway provides a unified REST API (POST /v1/chat, POST /v1/embeddings) that routes requests to the optimal provider based on configurable strategies. Key infrastructure components include:

Model Routing Engine — supports cost-optimized, latency-optimized, capability-based, and round-robin strategies
Rate Limiter — sliding-window algorithm with per-user and per-tenant limits (Redis-backed for multi-instance deployment)
Response Cache — SHA-256 keyed cache with configurable TTL, reducing redundant API calls
Cost Tracker — per-request token usage and USD cost stored in database with analytics API
Observability — structured logging with OpenTelemetry metrics and traces

Routing Strategies

| Strategy | Description | |----------|-------------| | Cost (default) | Lowest combined input+output cost | | Latency | Lowest typical response latency | | Capability | Highest context window | | Round Robin | Rotate through available providers |

Automatic fallback chain ensures availability: small requests go to gpt-4o-mini, large context goes to claude-3-5-sonnet, and a local echo provider is always available when no API keys are configured.

Technical Decisions

Provider abstraction — each LLM provider is an adapter implementing a common interface, making new provider integration a single-file addition
Redis-optional — rate limiting and caching work with in-memory fallbacks when Redis is unavailable, enabling single-process development
SQLite for development, PostgreSQL for production — cost tracking database is configurable via connection string

Tech Stack