Top 5 MCP Gateways for Built-in Routing and Control
TL;DR
MCP gateways have become essential infrastructure for production AI agents, centralizing tool access, routing, and governance behind a single control plane. This article compares five leading MCP gateways with strong built-in routing and control capabilities: Bifrost, Docker MCP Gateway, Kong AI Gateway, TrueFoundry, and Lasso Security. Each serves a different architectural need, from unified LLM-plus-tool governance to container-native isolation to API-first management.
As agentic AI workloads move from demos into production, the gap between "connecting to a tool" and "governing tool access at scale" becomes impossible to ignore. A single AI agent might interact with databases, issue trackers, filesystems, and external APIs within one session. Without a centralized layer managing those interactions, teams face authentication sprawl, zero observability into tool calls, and no way to enforce access policies consistently.
This is the problem MCP gateways solve. An MCP gateway sits between AI agents and the tools they access, providing a governed entry point that centralizes routing, authentication, rate limiting, and observability. Think of it as an API gateway purpose-built for the Model Context Protocol.
But not all MCP gateways handle routing and control the same way. Some are extensions of existing API management platforms. Others are purpose-built for MCP-native workflows. The right choice depends on your team's infrastructure, security requirements, and whether you need tool governance alongside LLM routing or as a standalone layer.
Here are five MCP gateways that stand out for their built-in routing and control capabilities.
1. Bifrost
Bifrost is an open-source, high-performance AI gateway built in Go that functions as both an LLM gateway and an MCP gateway within a single unified platform. This dual architecture is what sets Bifrost apart: production AI agents need both model routing and tool access governance, and Bifrost delivers both through one control plane rather than requiring teams to deploy and manage separate infrastructure for each.
How Bifrost handles routing and control
At the core of Bifrost's MCP capability is its dual server-client architecture. Bifrost acts as both an MCP server (exposing tools to agents) and an MCP client (connecting to upstream MCP servers). This enables advanced routing, caching, and access control patterns that single-role gateways cannot replicate.
When an agent sends a request, Bifrost's gateway layer handles tool discovery automatically. It connects to configured MCP servers, learns available tools, and injects them into model requests without application code changes. Routing decisions happen at the infrastructure level, not inside agent logic.
Tool-level RBAC through virtual keys gives teams granular control over which agents, teams, or customers can access which tools. A customer-facing agent might only see read-only database tools, while an internal DevOps agent gets access to CI/CD pipeline tools. This isolation is enforced at the gateway, not in application code.
Rate limiting prevents runaway agent loops, a real production risk when autonomous agents trigger cascading tool calls. Bifrost's governance layer lets teams set limits per virtual key, per team, or per project, ensuring that a misconfigured agent cannot exhaust API quotas or rack up uncontrolled costs.
Bifrost also introduces Code Mode, which reduces token usage by 50%+ for multi-tool orchestration. Instead of loading hundreds of tool schemas into the context window, Code Mode lets AI models generate TypeScript orchestration code using three meta-tools: listToolFiles, readToolFile, and executeToolCode. The result is faster execution, lower costs, and more deterministic behavior.
On the performance side, Bifrost adds roughly 11 microseconds of overhead at 5,000 requests per second. In agentic workflows where a single user action triggers multiple LLM calls and tool interactions, that latency advantage compounds quickly compared to Python-based alternatives.
Bifrost integrates natively with Maxim AI's observability platform, giving teams end-to-end tracing across both model calls and tool invocations. Every MCP tool execution flows through a single audit trail, making debugging multi-step agent failures significantly easier. Organizations like Clinc, Thoughtful, and Atomicwork rely on Bifrost for production AI infrastructure where both LLM routing and tool access run through a single governed control plane.
Best for: Engineering teams that need MCP tool governance unified with LLM routing, ultra-low latency, and production-grade observability in a single gateway.
2. Docker MCP Gateway
Platform overview
Docker MCP Gateway brings familiar container orchestration workflows to MCP server management. Rather than building a standalone governance platform, Docker leverages container isolation as its primary security and routing mechanism, making it a natural fit for teams already running containerized infrastructure.
Features
Docker MCP Gateway provides container-level isolation for each MCP server, with CPU and memory limits that prevent resource exhaustion attacks. It includes access to the Docker MCP Catalog with hundreds of pre-built servers, cryptographically signed images for supply chain protection, and integration with existing Docker Compose and Kubernetes workflows. Routing is handled through container orchestration primitives rather than a dedicated policy engine. The container-based approach does add 50-200ms latency overhead compared to purpose-built gateways, which is worth considering for latency-sensitive agent workflows.
Best for
DevOps teams already using Docker for infrastructure that want container-native MCP server management with strong isolation guarantees but are comfortable layering additional governance tooling on top.
3. Kong AI Gateway
Platform overview
Kong is one of the most established enterprise API gateway platforms, and its AI Gateway 3.12 release (October 2025) extended that foundation to MCP with a dedicated MCP Proxy plugin, OAuth 2.1 support, and LLM-as-a-Judge validation capabilities.
Features
Kong's MCP integration includes centralized OAuth that secures all MCP servers simultaneously, rate limiting and policy enforcement inherited from its mature API management layer, and an MCP Proxy plugin that routes tool calls through Kong's existing traffic management infrastructure. For teams already managing hundreds of APIs through Kong, the MCP extension is a natural addition that leverages proven scalability. The trade-off is that this is a general API gateway extended for MCP rather than an MCP-native solution, so the learning curve can be steep for teams without existing Kong deployments.
Best for
Enterprises with existing Kong API management deployments that want to extend their current infrastructure to handle MCP traffic without adopting a separate gateway.
4. TrueFoundry
Platform overview
TrueFoundry offers a unified control plane that manages both LLM calls and MCP tool interactions from a single dashboard. Recognized in the 2025 Gartner Market Guide for AI Gateways, it focuses on minimizing operational complexity for platform teams managing AI infrastructure at scale.
Features
TrueFoundry provides a unified LLM and tool gateway built on an optimized Node.js backend with in-memory policy enforcement, delivering less than 5ms of p95 latency overhead. It includes real-time observability with built-in logs, metrics, and traces for all traffic, plus enterprise governance features like RBAC and centralized credential management. The platform is strong for infrastructure and routing, though teams will need to build or onboard most MCP servers themselves.
Best for
Enterprises with dedicated platform or infrastructure teams that need strong governance and centralized control over both model and tool access, and are willing to invest engineering effort in building out their MCP server ecosystem.
5. Lasso Security
Platform overview
Lasso MCP Gateway is an open-source proxy and orchestration layer launched in April 2025, designed to sit between AI agents and multiple MCP servers as a central coordination point. Its primary differentiator is a plugin-based security architecture that allows deep inspection and filtering of MCP traffic at the request and response level.
Features
Lasso provides a customizable plugin-based guardrail system where developers can enforce security at the request and response level. Plugins like Presidio for PII detection can be added to inspect, sanitize, or block traffic for enterprise-grade data protection. All tool calls, prompt executions, and resource reads are logged in a structured JSON format for auditability. The gateway also supports centralized routing with session tracking across multiple MCP servers.
Best for
Security-focused teams that need deep request-level inspection, PII detection, and data protection guardrails as a core part of their MCP gateway layer.
Choosing the right MCP gateway
The right MCP gateway depends on where your team sits architecturally.
If you need both LLM routing and MCP tool governance under one roof with minimal latency overhead, Bifrost eliminates the need for separate infrastructure. Its unified control plane, Code Mode for efficient tool orchestration, and native integration with Maxim AI's evaluation and observability stack make it the strongest choice for teams building production-grade agentic systems.
If container isolation is your primary security model, Docker MCP Gateway fits naturally into existing workflows. Teams with established Kong or TrueFoundry deployments can extend what they already have. And if request-level security inspection is non-negotiable, Lasso's plugin architecture gives you that granular control.
What matters most is that your gateway layer does not become an afterthought. As AI agents scale in production, the routing and control layer between agents and tools determines whether your system is governable, debuggable, and reliable or a liability waiting to surface in production.
