Enterprise LLM Gateway Implementation Skill

To build a multi-provider LLM gateway, this Skill provides implementation guidance for a FastAPI-based infrastructure with adapters for OpenAI, Claude, Gemini, and local models, exposing a unified OpenAI-compatible API endpoint.

Semantic caching in an LLM gateway stores and retrieves responses based on meaning rather than exact text, reducing redundant LLM calls and lowering API usage costs while decreasing response latency.

Implementing routing across multiple LLM providers requires an intelligent routing layer with automatic fallback, exponential backoff retry, and load balancing capabilities, all detailed in the Skill's implementation guidance.

Yes, you can enforce per-tenant budget limits and track LLM costs in a production gateway using the cost tracking and budget enforcement features included in this Skill's architecture.

Yes, this LLM gateway implementation includes guardrails for PII detection and content safety checks, utilizing a plugin-based architecture to allow extensibility for enterprise compliance requirements.

Setting up observability for an enterprise LLM gateway involves configuring structured logging and Prometheus metrics to generate Grafana-ready dashboards, as outlined in the Skill's deployment guide.