Ignasi Barrera

Today we're thrilled to announce Envoy AI Gateway 1.0 — the first stable, generally available release of the open source AI gateway built on CNCF's Envoy Gateway.

When we shipped v0.1 in February 2025, we closed that post with three words: "Onward to 1.0!" Sixteen months and many releases later, backed by a community of maintainers and adopters across the industry, we're here. 1.0 means you can build on Envoy AI Gateway with confidence: a control-plane API we're committing to keep stable, running on the same battle-tested Envoy foundation that already powers production traffic at the world's largest companies.

Envoy AI Gateway (AIGW) provides a production-ready bridge between AI agents and their tools by handling Model Context Protocol (MCP) traffic. As teams adopt MCP, questions about scale, performance, and architecture naturally arise.

This post addresses those questions by first clearing up common misunderstandings, then diving into the actual architecture, the design choices we made (and why), and how you can test and evaluate whether it is the right solution for your system.

This post will give you the context you need to:

• Evaluate MCP routing in Envoy AI Gateway with realistic expectations
• Understand the design decisions, their impact, and how they impact you
• Learn about how you can configure and tune MCP routing in Envoy AI Gateway to meet your needs

We’re excited to announce that the next release of Envoy AI Gateway will introduce first-class support for Model Context Protocol (MCP), cementing Envoy AI Gateway (EAIGW) as the universal gateway for modern production AI workloads.

Envoy AI Gateway started in close collaboration with Bloomberg and Tetrate to meet production-scale AI workload demands, combining real-world expertise and innovation from some of the industry’s largest adopters. Built upon the battle-tested Envoy Proxy data plane as the AI extension of Envoy Gateway, it is trusted for critical workloads by thousands of enterprises worldwide. EAIGW already provides unified LLM access, cost and quota enforcement, credential management, intelligent routing, resiliency, and robust observability for mission-critical AI traffic.

With the addition of MCP, we have brought these features to the communication between Agents and external tools, making EAIGW even more versatile for enterprise-scale AI deployments. For a deeper look at the collaborative story and technical vision, see the Bloomberg partnership announcement, their official release coverage, and previous project announcements.