01Module 03: LLM GatewaysLearn 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.02Why an LLM GatewayThe case for centralizing all LLM traffic through a single gateway layer - routing, cost control, fallbacks, and observability without rewriting application code.03LiteLLMDeploy 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.04PortkeyUse 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.05Semantic CachingReturn 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.06Model Fallback and RetryDesign resilient LLM clients with configurable fallback chains, exponential backoff with jitter, and circuit breakers that handle provider failures gracefully without any user-facing impact.07Load Balancing Across ProvidersDistribute 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.08Cost Management and Budget AlertsTrack 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.09Rate Limiting and QuotasProtect 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.
