Top MCP Gateways Optimized for Speed and Scale

A side-by-side look at four MCP gateways, evaluated on latency, throughput, governance, and deployment fit for production AI workloads.

TLDR: MCP adoption is climbing, and so is the operational overhead of wiring tool connections together across teams and agents. This piece walks through four MCP gateway options worth evaluating, Bifrost, Lunar.dev MCPX, Kong AI Gateway, and Docker MCP Gateway, with a focus on performance, scale, and production readiness.

Running one or two MCP servers is easy. Coordinating dozens of them across environments, teams, and agent workflows is a different problem entirely. Without a central layer, every agent ends up owning its own credentials, retry logic, and connection handling, and that approach falls apart quickly. An MCP gateway fixes this by sitting between AI agents and the tools they call, acting as a single control plane for the whole estate. Teams comparing options in this category should also work through a structured AI gateway buyer's guide before committing.

Four gateways stand out if speed and scale are the constraints driving your decision.

Bifrost

Bifrost is a Go-based, open-source AI gateway from Maxim AI. It was engineered from day one for production traffic rather than retrofitted onto a general-purpose API management layer.

Platform overview

Sitting between AI agents and LLM providers, Bifrost routes every request through a single OpenAI-compatible endpoint. The gateway works with 15+ providers, including OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, and Google Vertex AI, and runs either self-hosted or via Docker. MCP support is built in, not added as an afterthought, and the MCP gateway architecture is designed for centralized tool discovery and governance from the start.

Performance and scale

Performance is where Bifrost pulls ahead. On MCP operations, overhead stays under 3ms, and a single vCPU sustains 350+ requests per second with no tuning required. That figure is not just a marketing benchmark; it reflects deliberate architectural choices, including asynchronous execution, zero-copy message passing, and in-memory processing that sidesteps trips to external state stores. Independent performance benchmarks document the overhead profile under sustained production load.

For conversational agents that fire dozens or hundreds of tool calls per session, those milliseconds compound. A gateway adding 3ms of overhead behaves very differently in production than one adding 100ms.

Features

Out of the box, Bifrost ships with semantic caching, automatic failover, multi-provider load balancing, RBAC, and rate limiting. Enterprise governance features, including virtual keys, audit logs, and granular budgets, are first-class rather than bolted on. Policy-based guardrails cover PII detection, prompt injection protection, and output filtering at the gateway layer.

On the observability side, Prometheus metrics are exposed natively, and OpenTelemetry tracing slots into whatever monitoring stack you already run. An MCP tool registry is also included, so agents can discover available tools dynamically rather than relying on hardcoded configuration. Teams using CLI coding agents such as Claude Code, Codex CLI, Gemini CLI, and Cursor can route their MCP tool calls through Bifrost for unified governance and cost tracking.

Getting started takes almost no effort. A single npx command runs Bifrost locally, or you can pull the Docker image. There is no mandatory cloud signup and no upfront configuration before you can stress-test it. Teams migrating from Python-based gateways can also follow the LiteLLM migration guide for a drop-in replacement path.

Best for

Teams running many MCP servers across multiple agents, tools, and environments who need one secure entry point with full governance coverage. Bifrost works well for enterprises that need OAuth, federated authentication, and sandboxed tool execution without slowing down agents. It also fits regulated organizations that need to keep tool calls and request data inside their own infrastructure perimeter. For teams currently on Python-based gateways and weighing a switch, the LiteLLM alternatives comparison covers the feature-by-feature differences.

Lunar.dev MCPX

Platform overview

MCPX is Lunar.dev's MCP gateway, designed around enterprise governance and security monitoring rather than raw throughput. It plugs into Lunar's broader AI Gateway for end-to-end traffic inspection.

Features

Tool-level RBAC, detailed audit logs, tool scoping with parameter overrides, prompt sanitization, and private deployment or VPC options for sensitive workloads. A plugin-based architecture lets organizations layer in additional security controls incrementally as their needs grow. Teams comparing this approach against alternative open-source AI gateway options will find different trade-offs across the category.

Best for

Teams that want flexible monitoring and control over how agents interact with MCP tools, with room to extend policy and security coverage over time.

Kong AI Gateway

Platform overview

Kong's MCP capabilities shipped with the AI Gateway 3.12 release in October 2025, layering an MCP Proxy plugin, OAuth 2.1, and MCP-specific Prometheus metrics onto an already-mature API management platform.

Features

Centralized policy enforcement, Prometheus metrics, OAuth 2.1, and integration with Kong Konnect's existing control plane. The fit is strongest for teams already routing API traffic through Kong who want to fold MCP governance into the same setup rather than standing up something separate. Teams without a Kong footprint may find purpose-built MCP gateway tooling a better starting point.

Best for

Organizations already invested in the Kong ecosystem. Greenfield teams will likely pay for capabilities they do not need, since Kong's design is not MCP-native. Enterprise licensing can run past $50k per year.

Docker MCP Gateway

Platform overview

Docker's MCP Gateway brings container orchestration thinking to MCP server management. It runs through Docker Desktop, the Docker CLI, and Docker Compose, which makes it immediately familiar to most engineering teams.

Features

Container-native lifecycle management, request and response filtering, destination allow-lists, secret masking, and horizontal scaling through Docker's native orchestration. Federation across multiple nodes is also supported for distributed deployments.

Best for

Docker-native teams that want self-hosted MCP infrastructure with strong container isolation and would rather manage MCP servers the same way they already manage everything else, with tooling they know.

How to choose

The right gateway maps to your constraints:

• Latency-sensitive production workloads → Bifrost (benchmarks)
• Governance-first enterprise rollouts → Lunar.dev MCPX (or Bifrost's governance layer for tighter MCP integration)
• Already standardized on Kong → Kong AI Gateway
• Docker-native teams that want self-hosted control → Docker MCP Gateway

Most of these platforms either launched or significantly upgraded their MCP capabilities in 2025, so the category is still settling. For teams formally evaluating vendors, the AI gateway buyer's guide is worth working through end to end. Run your own staging benchmarks against the workloads you actually care about before committing to any infrastructure decision.

Try Bifrost

If sub-3ms MCP overhead and 350+ RPS on a single vCPU map to what your agents need, book a Bifrost demo and walk through MCP gateway setup, governance, and performance benchmarks for your stack.