A side-by-side comparison of the leading AI gateways in 2026 — WSO2, Kong, Portkey, LiteLLM, Cloudflare, and Helicone — across deployment, cost control, security, and observability.
A guide to the concrete controls that secure LLM and agent traffic: prompt-injection defense, PII redaction, authentication and RBAC, guardrails, and audit logging.
A side-by-side breakdown of how AI gateways and API gateways differ, where they overlap, and how to decide where each one belongs in your stack.
What AI gateway observability is, the key metrics to track (tokens, cost, latency, errors), and how logging, tracing, and OpenTelemetry fit in.
Compare the best LiteLLM alternatives in 2026 on routing, governance, security, and deployment after the LiteLLM supply-chain attack.
How to control LLM costs with token tracking, budgets, caching, model routing, and prompt optimization, and why a gateway is the best place to enforce it.
What LLM routing is, static vs dynamic vs model-aware strategies, and how routing cuts LLM cost and improves reliability at the gateway layer.
How to defend against prompt injection: input validation, prompt templating, trust boundaries, human-in-the-loop, and gateway guardrails. Practical techniques.
Benchmark results for the WSO2 API Platform Gateway; a single 16-core instance sustained 82,809 req/sec with zero errors, and how Router Concurrency and policy choice affect throughput and latency.
What LLM fallback is and how to implement it: fallback chains, retries with backoff, and circuit breakers, to keep AI apps running through provider outages.
Learn how to turn a REST API into an MCP server: map endpoints to tools, handle auth and schemas, and avoid the auto-convert trap. Step-by-step with examples.