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MCP gateways have emerged as critical infrastructure for production-grade AI agents, consolidating tool access, routing, and governance within a unified control plane. This article evaluates five leading MCP gateways with strong built-in routing and control features: Bifrost, Docker MCP Gateway, Kong AI Gateway, TrueFoundry, and Lasso Security. Each platform addresses a distinct architectural requirement, ranging from unified LLM and tool governance to container-based isolation and API-centric management.
As agentic AI systems transition from experimentation to production, the distinction between simply connecting to a tool and governing tool access at scale becomes increasingly significant. A single AI agent may interact with databases, issue trackers, file systems, and external APIs within a single workflow. Without a centralized control layer, teams encounter fragmented authentication, limited observability into tool usage, and inconsistent enforcement of access policies.
MCP gateways address this challenge by acting as an intermediary between AI agents and the tools they access. They provide a governed interface that centralizes routing, authentication, rate limiting, and observability. In effect, they function as API gateways tailored specifically for the Model Context Protocol.
However, MCP gateways differ significantly in how they implement routing and control. Some extend traditional API management platforms, while others are designed specifically for MCP-native workflows. Selecting the appropriate solution depends on existing infrastructure, security requirements, and whether tool governance must be integrated with LLM routing or deployed independently.
Below are five MCP gateways that stand out for their routing and control capabilities.
1. Bifrost
Bifrost is an open-source, high-performance AI gateway written in Go that operates as both an LLM gateway and an MCP gateway within a unified platform. This dual capability distinguishes Bifrost, as production AI systems typically require both model routing and tool governance. Bifrost consolidates these functions into a single control plane, eliminating the need to manage separate infrastructure layers.
How Bifrost handles routing and control
Bifrost’s MCP functionality is built on a dual server-client architecture. It operates simultaneously as an MCP server, exposing tools to agents, and as an MCP client, connecting to upstream MCP servers. This design enables advanced routing, caching, and access control patterns that are not possible with single-role gateways.
When an agent issues a request, Bifrost manages tool discovery automatically. It connects to configured MCP servers, identifies available tools, and injects them into model requests without requiring changes to application code. Routing decisions are handled at the infrastructure layer rather than within agent logic.
Tool-level RBAC, implemented through virtual keys, provides fine-grained control over tool access. Organizations can define access boundaries for agents, teams, or customers. For example, a customer-facing agent may be restricted to read-only database operations, while an internal DevOps agent can access CI/CD tools. This enforcement occurs at the gateway level, ensuring consistency across applications.
Rate limiting mitigates risks associated with uncontrolled agent behavior, such as cascading tool calls in autonomous workflows. Bifrost allows limits to be configured per virtual key, team, or project, preventing excessive API usage and cost overruns.
Bifrost also includes Code Mode, which reduces token consumption by more than 50-90% in multi-tool orchestration scenarios. Instead of loading extensive tool schemas into the context window, Code Mode enables models to generate TypeScript orchestration code using four meta-tools: listToolFiles, readToolFile, getToolDocs ****and executeToolCode. This approach improves execution speed, reduces cost, and enhances determinism.
From a performance perspective, Bifrost introduces approximately 11 microseconds of overhead at 5,000 requests per second in sustained benchmarks. In workflows where a single user action triggers multiple model calls and tool interactions, this low latency provides a meaningful advantage over higher-overhead alternatives.
Bifrost’s built-in observability captures and stores detailed information about all the AI requests and responses flowing through the system. In addition to that, Bifrost integrates natively with Maxim AI’s observability platform, enabling end-to-end tracing across both model and tool interactions. Every MCP tool invocation is recorded within a unified audit trail, simplifying debugging for complex multi-step agent workflows. Organizations such as Clinc, Thoughtful, and Atomicwork use Bifrost to support production AI systems where both model routing and tool access are governed centrally.
Best for: Engineering teams that require unified MCP tool governance and LLM routing, ultra-low latency, and production-grade observability within a single gateway.
2. Docker MCP Gateway
Platform overview
Docker MCP Gateway applies established container orchestration principles to MCP server management. Instead of building a dedicated governance layer, it relies on container isolation as the primary mechanism for security and routing. This makes it well-suited for teams already operating containerized environments.
Features
Docker MCP Gateway offers container-level isolation for each MCP server, with CPU and memory constraints that prevent resource exhaustion. It provides access to the Docker MCP Catalog, which includes a large set of pre-built servers, along with cryptographically signed images to ensure supply chain integrity. Integration with Docker Compose and Kubernetes allows teams to incorporate MCP management into existing workflows. Routing is handled through container orchestration primitives rather than a centralized policy engine. This approach introduces an additional latency overhead of approximately 50 to 200 milliseconds, which may impact latency-sensitive workloads.
3. Kong AI Gateway
Platform overview
Kong is a widely adopted enterprise API gateway platform. Its AI Gateway release in late 2025 extends its capabilities to MCP through features such as an MCP Proxy plugin, OAuth 2.1 support, and LLM-as-a-Judge validation.
Features
Kong’s MCP support includes centralized OAuth for securing all MCP servers, along with rate limiting and policy enforcement inherited from its mature API management ecosystem. The MCP Proxy plugin routes tool interactions through Kong’s existing traffic management layer. For organizations already managing large-scale API ecosystems with Kong, this extension integrates seamlessly. However, as Kong is not MCP-native, teams without prior experience may face a steeper learning curve.
4. TrueFoundry
Platform overview
TrueFoundry provides a unified control plane for managing both LLM interactions and MCP tool access. Recognized in the 2025 Gartner Market Guide for AI Gateways, it emphasizes operational simplicity for teams managing AI infrastructure at scale.
Features
TrueFoundry offers a combined LLM and MCP gateway built on an optimized Node.js backend with in-memory policy enforcement, achieving less than 5 milliseconds of p95 latency overhead. It includes real-time observability with logs, metrics, and traces across all traffic, along with enterprise governance capabilities such as RBAC and centralized credential management. While it provides strong infrastructure and routing capabilities, teams are responsible for building or integrating most MCP servers.
5. Lasso Security
Platform overview
Lasso MCP Gateway is an open-source proxy and orchestration layer introduced in April 2025. It operates between AI agents and multiple MCP servers, serving as a centralized coordination layer with a strong emphasis on security.
Features
Lasso implements a plugin-based guardrail framework that enables detailed inspection and filtering of MCP traffic at both request and response levels. Developers can integrate tools such as Presidio for PII detection to enforce data protection policies. The platform logs all tool calls, prompt executions, and resource access in structured JSON format, supporting auditability and compliance. It also includes centralized routing with session tracking across multiple MCP servers.
Choosing the right MCP gateway
Selecting an MCP gateway depends largely on your architectural priorities.
For teams that require both LLM routing and MCP tool governance within a single platform and with minimal latency overhead, Bifrost provides a consolidated solution. Its unified control plane, Code Mode for efficient orchestration, and integration with Maxim AI’s evaluation and observability stack make it particularly well-suited for production-grade agentic systems.
While Docker MCP Gateway is an option for teams strictly focused on container-bound isolation, it often functions best as a tactical addition to existing Docker-centric workflows rather than a holistic solution. Organizations already deeply committed to Kong or TrueFoundry may find value in extending those specific infrastructures, though this often requires managing the complexity of their existing ecosystems. For specialized use cases requiring exhaustive request-level inspection, Lasso’s plugin architecture provides granular control.
Ultimately, the gateway layer should be treated as a foundational component rather than an afterthought. As AI agents scale in production, the routing and control layer between agents and tools determines whether systems remain governable, debuggable, and reliable or become a source of operational risk.
