Best AI Gateways with Multi-LLM Support for Enterprises

TL;DR

• An MCP gateway is a control layer that centralizes authentication, tool discovery, and access policy for every MCP server an AI agent can reach.
• Bifrost and LiteLLM both combine MCP tool governance with LLM routing in one component; Bifrost differs in hosting MCP servers directly and filtering tools per virtual key rather than handling MCP at the request layer.
• Bifrost supports six MCP authentication types: none, static headers, admin OAuth 2.0, per-user OAuth, per-user headers, and token exchange.
• Bifrost adds 11 microseconds of overhead per request in sustained benchmarks at 5,000 requests per second in a t3.xlarge instance, which matters when a single agent turn triggers dozens of tool calls.
• Code Mode cuts input token usage by up to 92.8% in large MCP deployments by letting the model write code against tool stubs instead of loading every tool definition into context.

An MCP gateway is a control layer that sits between AI agents and the MCP servers they call, giving one governed entry point for every tool invocation. the Model Context Protocol (MCP) standardized how agents discover and invoke external tools, from databases and file systems to APIs and SaaS platforms, but connecting agents directly to dozens of MCP servers becomes unmanageable in production: authentication sprawls across servers, tool calls go unlogged, and a single misconfigured server can expose sensitive data. Gateways close those gaps by centralizing authentication, enforcing access policies, adding audit trails, and making every tool call observable. This guide evaluates the top 5 MCP gateways for connecting tools and MCP servers to production AI agents, judged on governance, performance, tool management, and developer experience. Bifrost, the open-source AI gateway maintained on GitHub and built by Maxim AI, leads the list because it is the only option that governs model traffic and MCP tool traffic through one control plane, and it is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability.

What Is an MCP Gateway?

An MCP gateway is a control layer that centralizes authentication, discovery, and access policy for every MCP server an AI agent can reach. Instead of each agent holding credentials for each tool server, agents connect to one endpoint, and the gateway resolves which tools that caller may see, authenticates upstream on their behalf, executes the call, and records it.

Three terms get used interchangeably and mean different things:

The distinction matters at procurement time. A proxy solves connectivity; a gateway solves governance. The MCP specification defines the server and transport layers but says nothing about who is allowed to call what, which is the gap a gateway fills. For a longer treatment of the category, see what an MCP gateway is and how it works.

Why Your AI Agents Need an MCP Gateway

Running MCP servers directly is workable for a prototype and unworkable in production, because three gaps widen as the tool count grows: credentials multiply faster than anyone can rotate them, tool calls leave no trace to debug, and each server becomes its own deployment to maintain across every environment.

• Security boundaries: Each MCP server executes with whatever permissions it is granted. As the tool ecosystem grows, managing authentication, role-based access, and security boundaries across dozens of servers becomes a liability
• No observability: Direct MCP connections provide no insight into which tools agents invoke, what data they access, or where failures occur. Without structured logging and tracing, debugging agent behavior is guesswork
• Operational overhead: Each server needs its own deployment, monitoring, versioning, and maintenance, repeated across development, staging, and production environments

An MCP gateway closes these gaps by routing every tool invocation through one control plane with consistent security, logging, and policy. The same argument applies to agents running in regulated environments, covered in this guide for production AI agents.

1. Bifrost by Maxim AI

Bifrost, the high-performance open-source AI gateway, takes a different approach to MCP gateway architecture. Rather than treating MCP as an isolated capability requiring separate infrastructure, Bifrost integrates it as a native feature of a high-performance AI gateway, giving teams unified control over both model access and tool invocations through a single platform.

MCP capabilities:

• Centralized tool connections: Connect all MCP servers (filesystem, databases, web search, custom tools) through a single gateway endpoint, so agents hold one connection instead of many
• Tool filtering per virtual key: Control exactly which MCP tools each agent, team, or customer can access through virtual key configurations, preventing unauthorized tool invocations at the infrastructure layer
• Six authentication types: Bifrost supports none, static headers, admin OAuth 2.0, per-user OAuth, per-user headers, and token exchange. Per-user modes authenticate each end user lazily against the upstream service, and token exchange carries the caller's identity-provider token without persisting a credential per user
• Federated authentication: Enterprise deployments turn existing internal APIs into MCP tools without glue code through MCP with federated auth, and MCP tool groups attach curated tool collections to virtual keys, teams, customers, and users
• Governance and audit trails: Every tool call is captured in request logs with full metadata, while audit logs record administrative activity (who changed which policy, and when) as signed, retention-controlled events for compliance review
• Code Mode for large tool catalogs: With Code Mode, the model writes Python against tool stubs inside a sandbox instead of loading every tool definition into context. In a benchmark spanning 508 tools across 16 servers, this cut input tokens from 75.1M to 5.4M and estimated cost from $377 to $29 while preserving a 100% pass rate
• Zero-config tool setup: Define MCP clients via Web UI or JSON config, Bifrost automatically injects available tools into model requests, extending agent capabilities without application code changes

What sets Bifrost apart is the unified gateway architecture. Because Bifrost handles both LLM routing and MCP tool access, teams get a single control plane for model providers, tool servers, budgets, guardrails, and request logs. There is no separate MCP proxy to deploy, secure, and upgrade alongside the AI gateway, which is the operational cost every standalone option in this list carries. Bifrost also runs in both directions: it acts as an MCP client connecting out to external tool servers, and as an MCP server exposing those tools to clients such as Claude Desktop and Cursor.

Performance: Built in Go, Bifrost adds 11 microseconds of overhead per request in sustained benchmarks at 5,000 requests per second, with a 100% request success rate. Gateway overhead compounds with every tool call, so a single agent turn that fires twenty tool calls pays that overhead twenty times.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform.

Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

2. Kong AI Gateway

Kong AI Gateway extends an established API management platform to MCP traffic, so MCP endpoints are governed by the same policy engine, plugins, and control plane already handling REST APIs. It suits organizations whose constraint is consolidating on one gateway vendor rather than optimizing for agent-specific tool governance, which is a real constraint and a real trade-off. Teams weighing that trade-off against a purpose-built option can compare it with MCP governance and how it works.

MCP capabilities:

• MCP traffic governance: Route and manage MCP server connections through Kong's existing policy engine with rate limiting, authentication, and access controls
• Plugin-based security: Apply Kong's ecosystem of plugins for request transformation, logging, and security enforcement on MCP traffic
• PII sanitization: Automatically redact sensitive information before tool invocations reach MCP servers
• Unified API and AI management: Manage traditional REST APIs and MCP endpoints through a single Kong control plane

Best for: Enterprises already running Kong for API management that want to extend existing governance infrastructure to AI agent tool access without adopting a new platform.

3. ContextForge (IBM)

ContextForge is an open-source MCP gateway, proxy, and registry that federates multiple MCP servers, REST APIs, and agent-to-agent services behind one MCP-compliant endpoint. Its distinguishing feature is multi-tenancy: separate teams get isolated tool catalogs and independent policy, which suits large organizations running many tool owners. It governs MCP traffic only, so it sits beside an AI gateway for model routing rather than replacing one.

MCP capabilities:

• Multi-server federation: Aggregate multiple MCP servers, REST APIs, and agent-to-agent services into a single MCP-compliant endpoint that agents interact with
• Multi-tenant workspaces: Provide different teams with isolated tool catalogs, role-based access boundaries, and independent policy configurations
• Safety plugins: A built-in plugin set for PII detection, content filtering, rate limiting, and policy enforcement, applied as pre- and post-hooks on every MCP request
• REST-to-MCP conversion: Automatically expose existing REST APIs as MCP-compatible tools behind the gateway with authentication and rate limiting

Best for: Large organizations with complex, multi-team environments that need sophisticated tool federation and are comfortable managing infrastructure.

4. Docker MCP Gateway

Docker MCP Gateway treats each MCP server as a container workload, applying container-native isolation, resource limits, and network policy to tool execution. It answers the question of how to run MCP servers safely rather than who may call which tool, so most teams pair it with a governance layer. Docker MCP Gateway is also one of the more searched options in this category, largely on the strength of that isolation model.

MCP capabilities:

• Container-based isolation: Each MCP server runs in its own container with strict resource limits and network policies for security isolation
• Unified endpoint: Aggregates multiple containerized MCP servers behind a single endpoint for simplified agent connectivity
• Secrets management: Built-in credential handling for MCP servers using Docker's native secrets infrastructure
• Observability hooks: Enterprise-ready logging and monitoring integrated with container orchestration tools

Considerations: Docker MCP Gateway is focused on server orchestration and isolation rather than comprehensive governance. It lacks virtual key management, budget controls, and the granular tool filtering available in a full MCP control plane.

Best for: DevOps teams already using Docker for infrastructure that want container-native MCP server management with strong isolation guarantees.

5. LiteLLM

LiteLLM adds MCP capabilities to its open-source LLM proxy, so teams already running it for model routing get team-scoped and key-scoped tool access without a second component. The trade-off is depth: MCP is handled as a request-layer tool type rather than a dedicated gateway. Teams hitting that ceiling often evaluate Bifrost as a LiteLLM alternative.

MCP capabilities:

• MCP gateway support: Route MCP tool requests through LiteLLM's proxy with team-based and key-based access controls
• Tool access by team and key: Define which MCP tools are available to specific teams or API keys with granular permissions
• Budget integration: Apply existing LiteLLM budget and rate limit controls to MCP tool usage
• Multi-provider compatibility: Manage MCP tools alongside a broad list of LLM provider connections through a single proxy

Considerations: LiteLLM runs on the Python runtime, so per-request overhead and concurrency limits under sustained load are materially different from a compiled gateway. MCP support is a tool-type integration at the request layer rather than a dedicated gateway, so it does not host MCP servers, filter tools per virtual key, or offer per-user OAuth in the same way.

Best for: Python-first teams that need basic MCP tool management alongside LLM proxy capabilities and are comfortable with performance trade-offs.

MCP Gateway Comparison at a Glance

The five gateways divide cleanly by what they were built to govern. Bifrost and LiteLLM govern model traffic and tool traffic together; Kong governs API traffic and extends to MCP; ContextForge and Docker govern MCP servers specifically, one by federation and one by containerization.

Cells marked "Not published" mean the capability was not documented on a page reviewed for this comparison, not that the product lacks it. For a deeper capability matrix across gateway categories, see the LLM gateway buyer's guide.

Open Source MCP Gateway Options

Four of the five gateways here are open source, which matters for MCP specifically: the gateway sees every tool call an agent makes, including arguments, so teams in regulated environments generally need to read the code and run it inside their own boundary rather than route tool traffic through a vendor.

Bifrost, ContextForge, Docker MCP Gateway, and LiteLLM can all be self-hosted at no license cost. They differ in what the open-source tier includes. Bifrost ships multi-provider routing, failover, semantic caching, virtual keys, budgets, and the full MCP gateway in the open-source build, with clustering, SSO and OIDC, RBAC, guardrails, audit logs, and in-VPC support in Bifrost Enterprise. Teams comparing self-hosted options in more depth can review the open-source MCP gateways for self-hosted AI infrastructure roundup, or the control guide for MCP gateways in regulated industries.

Running Bifrost locally takes one command:

npx -y @maximhq/bifrost

The gateway starts with zero configuration and MCP servers can be added from the web UI or a JSON config file.

How to Choose the Right MCP Gateway

The choice comes down to what you already run and what you need governed. Teams that already need an AI gateway should prefer one that governs MCP too, because a second control plane doubles the policy surface. Teams that only need MCP should choose on federation model and isolation guarantees. The criteria below separate the options:

• Unified vs. standalone: If you already need an LLM gateway for model routing and failover, a unified platform like the Bifrost AI gateway that handles both model access and MCP tools removes a second control plane. Standalone MCP proxies require managing separate infrastructure
• Tool-level governance: Production deployments need granular control over which agents access which tools. Look for virtual key-based tool filtering that enforces access policies at the infrastructure layer
• Observability depth: Understanding agent behavior requires visibility into every tool invocation. Look for gateways that emit OpenTelemetry traces and Prometheus metrics natively, so tool calls land in the monitoring stack the team already runs
• Performance at scale: Agents executing multi-step workflows may trigger dozens of tool calls per conversation. Gateway overhead compounds with each call, making low-latency architectures critical for responsive agent experiences
• Authentication model: Enterprise environments need federated auth with per-user OAuth, SSO, and centralized credential management, not just shared API keys

Frequently Asked Questions

What is an MCP gateway?

An MCP gateway is a control layer between AI agents and MCP servers that provides one governed entry point for every tool call. It aggregates multiple tool servers behind a single endpoint, authenticates upstream on the caller's behalf, filters which tools each caller can see, and records every invocation for debugging and compliance.

What is the difference between an MCP server and an MCP gateway?

An MCP server exposes one set of tools over the Model Context Protocol, such as a filesystem, a database, or a SaaS API. An MCP gateway sits in front of many servers and adds the control plane: authentication, tool filtering, policy enforcement, rate limits, and audit. A server provides capability; a gateway decides who may use it.

Is MCP an API gateway?

No. MCP is a protocol for tool discovery and invocation, not a gateway. An API gateway governs HTTP traffic to services; an MCP gateway governs protocol-level tool access for AI agents, including which tools appear in a model's context, how upstream credentials are resolved per user, and how many tokens tool definitions consume.

Are there open source MCP gateways?

Yes. Bifrost, ContextForge, Docker MCP Gateway, and LiteLLM are all open source and self-hostable. They differ in scope: Bifrost and LiteLLM govern model traffic and tool traffic together, while ContextForge and Docker MCP Gateway focus on federating and isolating MCP servers respectively.

How does an MCP gateway reduce token costs?

Every MCP tool definition loaded into a model's context consumes input tokens on every turn, so a large tool catalog is expensive before any tool is called. Bifrost's Code Mode addresses this by exposing tools as code stubs the model reads on demand, which cut input tokens by up to 92.8% in benchmarks with 508 tools across 16 servers.

Can an MCP gateway enforce per-user permissions?

Yes, when it supports per-user authentication. Bifrost offers per-user OAuth and per-user headers, where each end user authenticates against the upstream service themselves, and token exchange, where the caller's identity-provider token is exchanged per call with no stored credential. Combined with tool filtering, this scopes tool access to the person, not just the application.

Do coding agents work through an MCP gateway?

Yes. Because Bifrost also runs as an MCP server, clients such as Claude Desktop, Claude Code, and Cursor can point at it and receive the filtered tool set for their virtual key. The practical guide to using an MCP gateway with Claude Code walks through the configuration.

Conclusion

As AI agents move from answering questions to executing actions against real systems, the MCP gateway becomes the place where tool access is authorized, scoped, and recorded. Among the options compared here, Bifrost as a unified MCP and LLM gateway is the one that governs model providers and tool servers through a single control plane, with per-virtual-key access control, six upstream authentication modes, request-level logging, and a token-reduction mode built for catalogs too large to fit in context.

The practical test is small: run the gateway locally, point one agent at it, and check whether every tool call it makes is visible and attributable. Teams evaluating a gateway for production agents can start from the MCP gateway explainer, read the MCP gateway resource page for the architecture, or book a demo with the Bifrost team to see MCP tool governance running against an existing agent stack.