As enterprises adopt the Model Context Protocol (MCP), choosing the right gateway becomes critical for security, scalability, and operational control. This article breaks down how to evaluate enterprise MCP gateways by comparing leading options across key criteria like deployment, governance, integrations, and observability. Read on to learn what matters most—and how to select a gateway that supports safe, scalable AI adoption.
As artificial intelligence adoption accelerates, enterprises are increasingly relying on AI tools and agents to interact with internal applications, databases, and third-party services. The Model Context Protocol (MCP) has emerged as a standard way for these tools to connect with systems safely and predictably. However, the rapid proliferation of MCP servers within organizations raises a critical question: how can IT teams ensure secure, manageable, and scalable access to these resources?
Enter the Enterprise MCP Gateway. These platforms act as a control layer between AI clients and MCP servers, providing discoverability, governance, and operational visibility. With multiple open-source projects now available—including Obot MCP Gateway, Microsoft MCP Gateway, and IBM ContextForge MCP Gateway—enterprises have options, but choosing the right gateway requires careful consideration of several key factors.
1. Security and Governance
The first priority for any enterprise-grade MCP gateway is security, especially when the goal is securing AI agents in production. Organizations must ensure that sensitive systems and data are protected while providing appropriate access to users and AI agents. AI agents can expose sensitive data or API keys through prompt injection or other unsafe prompts. Essential security features include:
Unapproved agent actions and unsafe tool calls can cause significant damage, so the MCP gateway should govern tool access before execution.
Centralized authentication and access control: Integration with enterprise identity providers (e.g., Okta, Entra, Google) to enforce role based access control (RBAC), support SSO integration, and enable enterprise identity integration with providers such as Google Workspace.
Audit logging and monitoring: Full visibility into who accessed which MCPs and what actions were performed, with monitoring for every tool call and tool responses to review MCP usage and support compliance.
Proxy functionality: Gateways should act as intermediaries for all MCP traffic, enabling inspection, filtering, and policy enforcement before requests reach the underlying servers, with security controls such as validating tool schemas to reduce tool poisoning risk.
Gateways like Obot, Microsoft, and IBM all provide mechanisms for access control, logging, and proxying traffic, though each implements it differently. Evaluating how a gateway handles these core security functions is critical to maintaining compliance and risk mitigation. Credential isolation should also prevent token reuse across different MCP servers, especially when agents reach external tools.
👉 Obot helps enterprises operationalize MCP governance with centralized control, observability, and secure connector management. Try Obot today.
2. Discoverability and Catalog Management
MCP adoption often results in a fragmented landscape of internal and external servers. Without clear discoverability, employees may waste time searching for the right services or inadvertently bypass governance. Gateways should provide a unified interface or MCP registry for discovering approved MCP servers across multiple MCP servers:
Role-based catalogs: Users can see curated access to the MCP servers relevant to their team or permissions, rather than a raw directory of every server.
Documentation and metadata: Clear guidance on server capabilities, usage patterns, and supported clients.
Search and filtering: Easy navigation to quickly find the appropriate services.
Each of the three open source MCP Gateways handle catalogs and discovery in different ways. Obot emphasizes user-facing catalogs with detailed documentation, Microsoft uses adapters under a unified platform, and IBM provides a federated registry model. Discovery matters even more when teams operate local MCP servers, remote MCP servers, and other MCP servers at the same time. Some platforms also expose virtual MCP servers or pre built MCP servers to simplify governed discovery and MCP server access. Enterprises should evaluate how each approach balances usability with governance.
3. Deployment and Hosting Flexibility
MCP Gateways can also act as platforms to deploy and run MCP servers within the enterprise, serving as the layer where an MCP client connects to different MCP servers across local and external environments. Because organizations vary in their infrastructure preferences, a gateway’s deployment flexibility is important. Consider whether the platform is just a proxy and registry, or if it can take on the operational overhead required to run MCP Servers. Specifically, can it support:
Hosting MCP servers: directly, or simply routing requests to remote, external, or an upstream MCP server endpoint
On-premise, cloud, or hybrid environments: Where does the software run? Is it deployable on your existing infrastructure.
Containerized deployments: If the platform deploys MCP servers on demand, are they deployed using containers on Kubernetes?
Single-tenant and Multi-tenant MCP server support: MCP servers today come in many formats, how flexible are the deployment capabilities.
Stronger MCP gateway solutions also support dynamic routing and load balancing across multiple MCP servers.
Enterprises may choose an open source MCP gateway or a managed MCP gateway, including self-hosted commercial software, depending on infrastructure control requirements and how the gateway fits into broader AI infrastructure and gateway infrastructure decisions.
IBM ContextForge supports federated multi-cluster deployments, Obot allows internal hosting or proxying of remote servers, and Microsoft emphasizes enterprise routing and session-aware traffic management. The right gateway should fit your organization’s operational model and growth plans. Microsoft’s approach also highlights why MCP gateways work differently from traditional API gateways or an API gateway built for stateless traffic. remote servers, and Microsoft emphasizes enterprise routing and session-aware traffic management. The right gateway should fit your organization’s operational model and growth plans.
4. Integration with AI Clients and Workflow Automation
A gateway’s value extends beyond IT administration; it should enable productivity for AI users. The MCP gateway sits between AI clients and external tools, and MCP gateways solve workflow coordination and governance problems for AI systems. Key considerations include:
Compatibility with multiple MCP clients: Support for developer tools (VS Code, Cursor, Goose, Claude Desktop) and web-based clients, with the gateway acting as the protocol-aware layer for the MCP protocol between the MCP client and MCP tools.
Workflow orchestration: The ability to chain MCP servers into repeatable tasks or automated pipelines, including coordinating MCPtool calls across multiple servers while maintaining centralized control.
Chat or conversational interfaces: Optional interfaces for end users to interact naturally with data, tools, and agents.
Some gateways, like Obot, provide built-in chat interfaces for interacting with LLMs and MCP servers, while Microsoft and IBM focus more on adapters and infrastructure. Enterprises should consider how each platform supports practical workflows for their teams, especially as AI gateway capabilities become more important when AI workloads and autonomous AI systems scale.
Meet with an Obot Architect
Get expert guidance on deploying Obot as your enterprise MCP gateway and aligning it with your infrastructure.
👉 Click here to book a time that works with your schedule.
5. Observability and Metrics
To manage an enterprise-scale MCP ecosystem, IT teams need visibility into usage, performance, and adoption. Effective gateways provide:
Telemetry and monitoring: Track request volumes, error rates, latency, MCP server usage, and cost management.
Usage analytics: Understand which MCP servers are popular, who is using them, and how.
Troubleshooting tools: Access logs and live instances to quickly resolve operational issues.
A slow gateway can lead to poor AI application performance because every tool call passes through it.
Microsoft emphasizes session-aware routing with telemetry, IBM leverages OpenTelemetry with Phoenix, Jaeger, and Zipkin, and Obot provides analytics and logging through its admin console. As one benchmark example, the truefoundry MCP gateway reports sub-3ms latency under load and 350+ requests per second on 1 vCPU, which shows why throughput matters in real deployments. Enterprises should evaluate both the breadth and depth of observability features when selecting a gateway. Observability should also include centralized control plane visibility into MCP usage across tools and servers..
6. Extensibility and Open Standards
Finally, the choice of a gateway should align with open standards and future flexibility. Key factors include:
Support for the MCP standard: Ensures compatibility with current and future tools.
Open-source vs. proprietary: Open platforms allow customization and internal integration without vendor lock-in, however enterprises may also compare open source options with commercial offerings such as MCP Manager when evaluating MCP gateway solutions.
Extensibility: Ability to add new MCP servers, support approved and remote integrations exposed through consistent MCP tools, or extend workflow capabilities across multiple MCP servers.
This matters when connecting external tools while still preserving centralized control and governance.
All three projects—Obot, Microsoft, and IBM—are open-source and built on MCP, though they differ in maturity and focus. Enterprises should consider how each platform can support long-term integration, growth, and evolving AI workflows, since the best MCP gateway is ultimately the one that fits long-term standards alignment, customization needs, and the enterprise operating model.nd built on MCP, though they differ in maturity and focus. Enterprises should consider how each platform can support long-term integration, growth, and evolving AI workflows.
Conclusion
Enterprise MCP Gateways are emerging as a strategic layer for enabling AI adoption safely and effectively. While the MCP standard facilitates connections between AI tools and organizational data, gateways provide the governance, security, and discoverability needed to scale across an enterprise.
When evaluating MCP gateways, enterprises should focus on:
Security and proxy capabilities
Catalog management and discoverability
Deployment and hosting flexibility
Integration with AI clients and workflow automation
Observability and operational metrics
Extensibility and adherence to open standards
Projects like Obot MCP Gateway, Microsoft MCP Gateway, and IBM ContextForge MCP Gateway demonstrate different approaches to these challenges. By assessing gateways against these core characteristics, IT leaders can select a solution that both accelerates AI adoption and maintains enterprise control—unlocking the full potential of MCP-driven AI workflows.
