This cluster covers everything you need to understand, evaluate, and operate an AI gateway — from what they are and how they differ from traditional API gateways, to advanced topics like semantic caching, prompt injection defense, multi-model routing, and observability. These guides help you build an AI infrastructure layer that's reliable, cost-efficient, and secure.
A complete introduction to AI gateways: what they do, how they differ from API gateways, and when you need one.
What an AI gateway adds on top of a traditional API gateway, and when you need both in your stack.
LiteLLM, Portkey, Kong AI Gateway, WSO2 AI Gateway, and others compared on routing, security, and observability.
Input validation, output filtering, PII redaction, and prompt injection detection at the AI gateway layer.
Alternatives to LiteLLM for multi-model routing, evaluated on enterprise readiness, governance, and support.
How semantic caching reduces LLM costs and latency by reusing responses to semantically similar queries.
Cost routing, latency routing, capability routing, and fallback chains for resilient multi-model deployments.
How AI gateways detect and block prompt injection attacks in user-facing LLM applications at scale.
Automatically detecting and masking personally identifiable information before it reaches an LLM.
How to enforce per-user, per-app, and per-model token budgets and spend caps in your AI gateway.
Handling model downtime, context length errors, and rate limits with automatic fallback routing.
Tracing, logging, and metrics for AI gateways: what to instrument and what dashboards to build.
Using an AI gateway as a unified front door for on-premise and cloud LLM deployments.
Explore WSO2's AI Gateway capabilities
WSO2 AI Gateway provides routing, rate limiting, security policies, and observability for LLM traffic across your AI services and agent deployments.