9 docs tagged with "llm-gateways"

Track LLM spend per user, team, and feature in real time. Enforce hard budget limits and trigger alerts before costs spiral - because the invoice arrives 30 days too late.

Deploy LiteLLM as a universal LLM proxy supporting 100+ providers. Configure routing, load balancing, fallbacks, semantic caching, and cost tracking through a single OpenAI-compatible endpoint.

Distribute LLM traffic across multiple API keys and providers using round-robin, weighted, least-connections, and latency-based routing to scale throughput beyond single-key limits.

Design resilient LLM clients with configurable fallback chains, exponential backoff with jitter, and circuit breakers that handle provider failures gracefully without any user-facing impact.

Learn how to build and operate a production LLM gateway - the unified infrastructure layer for routing, caching, cost control, and observability across every AI service your team runs.

Use Portkey as a managed LLM gateway with built-in observability, virtual keys, guardrails, request tracing, feedback collection, and automated fallbacks across Claude, GPT-4o, and 250+ providers.

Protect your LLM infrastructure from abuse and cost overruns with token bucket rate limiting and sliding window quotas per user, team, and feature - enforced at the gateway before any tokens are consumed.

Return cached LLM responses for semantically similar queries using embedding-based vector similarity. Cut costs 40–60% by never paying for the same question twice regardless of how it is phrased.

The case for centralizing all LLM traffic through a single gateway layer - routing, cost control, fallbacks, and observability without rewriting application code.