The fastest LLM gateway in the world

Vrinda Kohli

AI Governance and Observability in Bifrost

AI Governance and Observability in Bifrost

Most teams that run AI in production eventually get the same question from finance. Spending went up last month, and someone wants to know which team, feature, or model caused it. Often there is no clear answer. The numbers exist, but they sit in two systems that were never built

Shadow AI Is the Insider Threat You Already Hired

Shadow AI: The Insider Threat You Never Noticed

The most damaging data leak in your organization this year probably won't come from an attacker. It'll come from an employee trying to hit a deadline. Right now, across nearly every company, people are pasting source code, customer records, contracts, and clinical notes into AI tools

MCP: The Protocol, the Plumbing, and the Gateway

MCP: The Protocol, the Plumbing, and the Gateway

If you've spent any time around AI tooling in the last year, you've probably heard the phrase "Model Context Protocol" thrown around, usually followed by the analogy "it's USB-C for AI." It's a useful starting point, but

Why MCP Tool Discovery Belongs in a Filesystem, Not the Context Window

Why MCP Tool Discovery Belongs in a Filesystem, Not the Context Window

Every MCP tool definition the model doesn't end up using is wasted context. That's the quiet bug in the way most teams run the Model Context Protocol today. Connect five MCP servers with thirty tools each, and the model receives 150 tool definitions before it ever

Code Mode and the Architecture of Token-Efficient MCP AgentsCode Mode

Code Mode and the Architecture of Token-Efficient MCP Agents

The Context Window Problem If you've built an agent that connects to more than a handful of MCP servers, you've already run into this. Every tool definition from every connected server gets loaded into the model's context window on every request. Five servers with

Your Semantic Cache Has a Grey Zone Problem

Your Semantic Cache Has a Grey Zone Problem

If you're running LLMs in production, you've probably built (or thought about building) a semantic cache. The pitch is simple: embed incoming prompts, find near-duplicates, serve the cached response, skip inference. It works. Except when it doesn't. A new paper from Apple introduces