We proxy trillions of API requests every day for the largest organizations but @kong AI token traffic has enter a whole new level of exponential growth. APIs <> Agents <> LLMs. No AI without APIs.

Gartner just released a new Magic Quadrant, and it's forcing the industry to answer a tough question: What does AI governance actually mean? In the new MQ, Gartner formalized AI governance as a distinct enterprise buying category, and they project the market will be worth $1.4 trillion by 2030. But we have to be precise about what this category does and does NOT include. As outlined by Gartner, AI governance platforms are built for CISOs, compliance officers, legal teams, and risk functions. Their job is to manage things like dynamic risk scoring and compliance framework mapping (EU AI Act, NIST AI RMF, ISO 42001). This is the "what" part of AI governance. But it doesn't cover the "how". Gartner is explicit about this point: governance platforms do NOT enforce policy in isolation. They depend on something beneath them to make those decisions operational at runtime. That's the "how" layer, where @Kong lives. Applying AI governance at the traffic layer. Rate limiting, access controls, prompt inspection, PII sanitization, content filtering, etc. This is the enforcement infra that makes governance decisions scalable. It's like traffic law vs traffic lights. You can set broad policies, but you need the traffic layer enforcement to make it actually work. A policy that says "no PII crosses this boundary" does nothing until something in the request path actually checks and enforces it. So what is AI governance? It depends on who you are. CISOs can focus on the "what" layer, while builders need to obsess over the "how". Orgs have to treat governance and AI connectivity as complementary infrastructure decisions. One layer defines the rules. The other makes them real. Traffic law AND traffic lights.

Token spend becoming a problem? An AI gateway at the traffic layer gives you 3 levers that can drastically reduce token consumption. 1) Prompt Compression: strips unnecessary characters from a prompt before it ever reaches the foundation model. 2) Semantic Caching: caches responses based on meaning, not exact wording, so duplicate intent doesn't trigger a redundant model call. 3) Semantic Routing: routes prompts to lower-cost models based on intent, reserving expensive models for complex tasks and cheaper ones for simple requests. These are valuable at any size org, but at enterprise scale (millions of daily requests) the token cost optimization could reshape your budget entirely.

MCP: new enough to be exciting, old enough to create a sprawl problem. Tell me if this sounds familiar. One team spins up an MCP server. Then five more. All built differently of course! Nobody knows what's out there or who owns what. Yikes. Agents got flooded with tools they don't need and you accidentally spent a months worth of tokens in a day (and you can't even blame Fable). Shadow infra at it's finest! But now it's got AI speed, so it's like supercharged chaos. That's why we built a central gateway for all AI context. One place to enforce authentication and security for all your data and tools. One point of observability to see which tools are being used, by which agents, and at what cost.