LLM clients, gateways, and routers provide a unified interface for interacting with multiple AI model providers. These tools let developers and agents switch between OpenAI, Anthropic, Mistral, Google, and open-source models through a single API — enabling cost optimization, fallback routing, load balancing, and model comparison.

The category includes both SDK-level clients (thin wrappers around provider APIs) and full gateway deployments (proxy servers that add rate limiting, caching, cost tracking, and observability on top of raw model calls). LiteLLM is the dominant open-source option, with commercial alternatives like Portkey and Helicone for teams needing managed infrastructure.

Key features to evaluate: OpenAI-compatible API surface (so existing code works with zero changes), streaming support, token usage tracking, retry logic, and semantic caching. For production deployments, look for tools with Redis caching support to reduce costs on repeated queries.