Was thinking about this over the weekend. What if LLMs were Italian food? Let me know what you think of the list (but remember you're arguing with an Italian). ChatGPT → Margherita Pizza The crowd-pleaser. Gemini → Lasagna Built with data layers (Search, YouTube, Android, etc.) Claude → Osso Buco Slow-cooked, meticulous, and expensive. Grok → Spicy 'Nduja Loud, punchy, sometimes TOO punchy. DeepSeek → Cacio e Pepe Simple ingredients, hard to make, shockingly good. Llama → Family-Style Antipasto Platter Open and shareable. Kimi → Tiramisu New on the menu and winning fans over fast.

the @kong AI gateway 2.0 is a piece of art. Not only it scales to quadrillions of token without missing a beat but has the best policy management in the industry. Of course all you see here is usable headless via API and CLI. Try it out 🦍

3 root causes of "The AI Fragmentation Tax" 1) Visibility is fragmented: About half of companies don't track LLM API costs at all, even when AI is core to the product. 2) Forecasting is broken: Only 15% of companies can predict AI spend within ±10% accuracy, which makes budgeting and margin protection nearly impossible. 3) Infrastructure is sprawling: Agents, MCP servers, LLMs, APIs, and event streams all connect across on-prem, cloud, and hybrid environments. Every touchpoint generates cost that's invisible in isolation but huge in aggregate. Even companies not integrating AI into their product directly are still heavy consumers of third-party LLMs, so they add cost with no measured revenue to offset it. The fragmentation tax compounds the longer it exists. Every release cycle makes it worse if you don't address the 3 root causes. That's why we're obsessed with building a unified cost governance layer for our customers. As AI speeds up the convergence of many capabilities, margin discipline becomes a differentiator.

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.