The Model Context Protocol (MCP), introduced in late 2024, has quickly emerged as a standard for connecting AI agents to tools, data sources, and external workflows. With adoption spreading across major AI ecosystems, MCP provides a consistent interface for how applications discover and invoke capabilities beyond the model itself.
However, adopting the protocol alone does not address the realities of running agents in production. Every tool integration introduces security considerations, compliance obligations, and operational complexity. Without centralized controls, organizations often lack visibility into how agents interact with systems or how sensitive data flows across tool calls. MCP gateways fill this gap by providing a control layer that makes tool connectivity manageable, observable, and secure.
As enterprise adoption of AI agents accelerates, MCP gateways are becoming foundational infrastructure. This guide reviews leading MCP gateways in 2026, comparing them across performance, security posture, observability depth, and readiness for real-world deployments.
What an MCP Gateway Provides
An MCP gateway acts as a centralized control plane between agents and MCP servers. Instead of configuring each agent to communicate directly with multiple tools, organizations route interactions through a managed layer that enforces policies and provides visibility.
Core responsibilities typically include:
- Centralized authentication and authorization: Managing which agents or users can access specific tools
- Security controls: Enforcing guardrails, detecting suspicious activity, and reducing the risk of data leakage
- Audit logging: Capturing detailed records of tool interactions for compliance and investigation
- Operational visibility: Providing dashboards, metrics, and tracing across tool usage
- Routing and resilience: Handling load balancing, failover, and health monitoring for tool endpoints
Without this layer, teams face growing operational overhead as the number of agents and tools increases.
1. Bifrost
Bifrost is a high-performance, open-source gateway that combines MCP tool connectivity with LLM routing in a single platform. Designed as infrastructure rather than a point solution, it provides a unified interface for managing both model interactions and tool access.
Key strengths:
- Native MCP support enabling agents to interact with filesystems, APIs, databases, and custom services
- Unified management of model calls and tool usage through a single API layer
- Built-in mechanisms for routing, caching, and failover across providers and services
- Enterprise controls such as budget enforcement, secure credential management, and audit logging
- Deep observability through metrics, tracing, and real-time analytics
Operational advantages:
- Low overhead suitable for high-throughput agent workloads
- Flexible deployment with containerized and drop-in integration options
- Extensible architecture supporting custom middleware and policies
Best for: Teams that want a single gateway to manage both LLM traffic and MCP tool interactions with strong governance and performance.
2. IBM ContextForge
IBM ContextForge is an open-source gateway and registry designed to federate tools, agents, and models across multiple protocols. It supports complex environments where different communication standards must coexist.
Key strengths:
- Support for MCP alongside REST, gRPC, and additional agent protocols
- Plugin framework enabling extensibility across transports and integrations
- Observability through OpenTelemetry-compatible tracing systems
- Administrative interfaces for configuration and monitoring
- Scalable deployment options across clustered environments
Limitations: Configuration can be complex, and operating it effectively often requires strong platform engineering expertise.
Best for: Organizations that need protocol federation and full control over deployment in large-scale environments.
3. Kong AI Gateway
Kong AI Gateway brings MCP support into the broader Kong ecosystem, allowing organizations to apply established API governance practices to AI workloads.
Key strengths:
- Protocol bridging between MCP and HTTP-based services
- Centralized authentication and policy enforcement
- Integration with Kong’s plugin ecosystem for logging and analytics
- Strong security controls including role-based access and certificate management
Limitations: Operational complexity can be high, particularly for teams not already using Kong.
Best for: Enterprises with existing Kong deployments looking to extend governance to MCP traffic.
4. Traefik Hub
Traefik Hub introduces MCP capabilities through a middleware approach, enabling teams to layer tool governance onto existing cloud-native networking infrastructure.
Key strengths:
- Security-focused architecture protecting multiple layers of AI interactions
- Support for advanced authentication flows including token exchange
- Context-aware authorization mechanisms
- Integration with cloud-native observability tooling
Limitations: Adoption may require aligning with Traefik’s ecosystem and operational model.
Best for: Platform teams already using Traefik who want to extend their networking layer to support MCP securely.
5. Azure API Management MCP Gateway
Azure API Management provides MCP capabilities integrated with Microsoft’s identity and monitoring stack, allowing organizations to manage agent tool access within existing cloud governance frameworks.
Key strengths:
- Native integration with Azure identity and access management
- Built-in monitoring through Azure observability services
- Managed deployment options alongside self-hosted configurations
- Tool exposure workflows that align with Azure service patterns
Limitations: Best suited for organizations operating primarily within Azure.
Best for: Teams deeply invested in Microsoft cloud infrastructure seeking integrated MCP governance.
Choosing the Right MCP Gateway
Selecting an MCP gateway depends on how you plan to operate agents in production:
- Unified control: Platforms that combine model and tool governance simplify operations and provide holistic visibility
- Infrastructure alignment: Leveraging existing platforms can reduce operational overhead
- Security posture: Evaluate how each gateway handles authentication, auditing, and policy enforcement
- Scalability requirements: Consider performance under real-world traffic patterns
A key strategic decision is whether to separate LLM routing and tool connectivity or manage both through a unified control plane.
Final Thoughts
As AI agents become more capable and interact with increasingly sensitive systems, MCP gateways play a critical role in ensuring that tool connectivity remains secure, observable, and manageable. The right gateway provides not just connectivity, but confidence — enabling teams to scale agent deployments while maintaining control over risk and operations.
Investing in robust gateway infrastructure today helps organizations build reliable foundations for the next generation of AI-powered workflows.
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