AI Gateways: from “just a proxy” to the GenAI control plane

A year ago, an “AI/LLM Gateway” felt like a thin layer: auth + simple routing across a few model providers. That era’s over. As teams ship agentic apps with many moving parts (models, tools via MCP, prompts, guardrails) the complex problems are now control, standardization, and observability.

What a modern gateway really does:

• Unified interface & routing: Swap models/providers without code changes; policy-based routing (latency/cost/quality), failover.

• Centralized access & governance: One place for keys, RBAC, per-team quotas, audit logs, and data residency.

• Guardrails at the edge: PII redaction, safety/moderation, jailbreak & prompt-injection checks, tool permissioning.

• Experimentation & evals: Prompt/version management, playgrounds to connect models + MCPs and build agents

• Deep observability: Traces for prompts/responses/tools, tokens/cost, latency SLOs, drift signals; caching/rate-limits/batching.

Net effect: faster experiments across models, safer-by-default deployments, lower provider lock-in, and a single control plane for the GenAI stack.

Curious: Have you used an AI gateway at work? Which capabilities were must-haves, and where did the gateway fall short?