Choosing the right MCP gateway matters. Bifrost stands out as the best option for teams running production AI agents that need governance, security, and speed without compromise.
When you're connecting AI agents to multiple MCP servers, things get messy fast. Your first agent uses two servers. Your second adds three more. By the time you have five agents across your org, you're staring at dozens of connections, no visibility into which agent can call which tool, and token costs that grow with every new server you add. An MCP gateway sits in the middle, creating a single checkpoint where you control access, authenticate, audit, and manage costs across everything.
Bifrost is an open-source AI gateway built in Go that solves this by acting as both an MCP client and server at once. But it's not the only option out there. This post walks through five gateways you can use to govern MCP access in production, comparing them on ease of setup, governance depth, audit capabilities, and how they handle costs at scale.
What Does an MCP Gateway Actually Need to Do?
MCP server access governance means controlling who can do what with your tools, logging everything, and keeping costs predictable. Here's what a serious gateway needs to handle:
- Tool-level access control: lock down which teams, customers, or services can touch which tools. When a key has no permission for a tool, it shouldn't see it at all (deny-by-default).
- Centralized identity: manage credentials to your MCP servers in one place, not scattered across agent configs.
- An audit trail: record every tool call with who made it, what happened, and why. Non-negotiable for compliance.
- Cost boundaries: set budgets and rate limits so one runaway agent doesn't tank your bill.
- Deployment options: the ability to run this in your own VPC, on-prem, or air-gapped if your industry or data residency requires it.
The MCP gateway resource goes deeper on what these controls look like in practice. The five tools below are compared against those criteria.
1. Bifrost: The All-in-One Approach
Bifrost combines MCP governance with LLM routing in one system. It routes requests to your models and governs access to your tools, so you're not stitching together separate infrastructure.
The architecture is straightforward: Bifrost sits between your agents and your MCP servers. It aggregates connections from multiple servers and exposes them through a single /mcp endpoint, with access enforced per request. At 5,000 requests per second, it adds only 11 microseconds of overhead, so governance doesn't become a bottleneck.
Governance happens through virtual keys. Each key is a bundle of permissions: which providers you can talk to, your budget, your rate limit, and which tools you can call. Crucially, the default is deny (a key with no MCP configuration sees zero tools until you explicitly add them).
Authentication to protected servers uses OAuth 2.0 with automatic token refresh and PKCE. On the cost side, Bifrost's Code Mode lets models orchestrate tools through code rather than loading massive tool definitions into the prompt, cutting tokens by up to 92% at scale.
For enterprise teams, Bifrost adds MCP tool groups so you define tool sets once and attach them to keys, teams, or customers. Federated auth turns your existing APIs into MCP tools from OpenAPI specs or Postman collections (no code required). Audit logs export to any SIEM for SOC 2, HIPAA, GDPR, and ISO 27001 audits. It runs anywhere: VPC, on-prem, air-gapped, with SSO via Okta or Entra.
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. Docker MCP Gateway: Container-Native Isolation
If your team lives in Docker, Docker MCP Gateway is worth a look. It runs each MCP server in its own container with signed images and built-in secrets handling.
The appeal is isolation at the container boundary. Each server runs independently, image signatures verify provenance, and secrets come through Docker's native systems, not hardcoded configs. For teams already shipping everything via containers, it's a natural fit.
The catch: Docker MCP Gateway is more of a toolkit than a turnkey solution. You get the building blocks (containers and signed images), but you layer on identity management, per-consumer access lists, and audit logging yourself. It also governs tools at the container level, not per-request, and doesn't handle model routing, so you'd run LLM traffic separately.
Best for: Container teams with Docker expertise who want full control over their setup and are willing to build governance layers on top.
3. Kong AI Gateway: For API-First Teams
Kong added MCP support through an AI MCP Proxy plugin. It translates between MCP and HTTP, letting MCP clients talk to REST APIs without rewriting them as MCP servers.
If you already use Kong for API management, extending it to MCP traffic keeps everything under one roof. Your existing rate-limiting, authentication, and observability policies apply to agent tool access too. It's familiar territory for platform teams that already standardize on Kong.
But MCP is bolted onto Kong rather than native to it. Tool-level access control, deny-by-default filtering, and token-optimization features like Code Mode aren't Kong's focus. LLM routing stays separate. For a deeper comparison, check the LLM gateway buyer's guide.
Best for: Teams with existing Kong deployments who want to manage agent tools through the same gateway and policies they use for REST APIs.
4. Azure API Management for MCP: Microsoft Ecosystem
Microsoft's approach: extend existing Azure services rather than shipping a new product. Azure API Management provides policy enforcement and OAuth 2.0, Entra ID handles identity and RBAC, and Azure Container Apps hosts MCP servers with Kubernetes-native scaling.
For Azure-centric orgs, it's appealing. You're using identity and policy infrastructure you already operate, reducing the number of new systems your platform team has to maintain and secure. Microsoft maintains an open-source MCP gateway for Kubernetes alongside the managed services.
The downside: Azure-first architecture locks you in. If you're multi-cloud, management complexity and vendor concerns become real. MCP capabilities are spread across multiple Azure services rather than unified in one layer, and portable deployments across AWS, GCP, and on-prem get harder.
Best for: Large enterprises committed to the Microsoft stack that want identity and policy controls integrated with Azure services.
5. Cloudflare AI Gateway and MCP Server Portals: Edge-First
Cloudflare's model: use Cloudflare AI Gateway, MCP Server Portals, and Cloudflare Gateway to create a security layer at the network edge. It includes shadow MCP detection to catch unauthorized server connections.
If you're already on Cloudflare's edge network and use Cloudflare One and Workers, this integrates naturally. MCP control happens at the network edge alongside your existing Zero Trust policies.
The dependency: you need Cloudflare infrastructure to make this work. If you're not on that platform, it's harder to justify. Model routing across LLM providers still happens separately from MCP access governance.
Best for: Orgs already invested in Cloudflare One and Workers that want edge-based MCP control with built-in shadow server detection.
Making the Choice
The right gateway depends on two things: do you need tool governance and model routing in one system (you probably do), and what infrastructure does your team already run?
Should governance and routing be unified?
In production? Almost always yes. Having tool access, model routing, audit logs, and identity in one control plane means simpler audits, smaller attack surface, and fewer integration points to debug. Bifrost is the only gateway here that unifies both by design.
What matters most for compliance?
Immutable audit logs, deny-by-default filtering, and the ability to run in isolated environments. Regulated industries (healthcare, finance, government) need proof of every tool call and the option to run in a VPC or offline. Bifrost's audit and access controls map directly to SOC 2, HIPAA, GDPR, and ISO 27001.
How do gateways cut token costs?
Most use budgets and rate limits. But there's a structural problem: if every request loads full tool definitions into the model context, costs climb with each new server. Bifrost's Code Mode flips this (the model writes orchestration code instead of carrying tool schemas, cutting tokens significantly).
Start Governing MCP Access
Controlling MCP server access at scale requires per-consumer tool filtering, centralized credentials, immutable audit trails, and cost controls that hold up as you add servers. Bifrost delivers all of these in one high-performance control plane, with deny-by-default access, tool groups for teams and customers, federated auth for enterprise APIs, and audit logs ready for SIEM export.
Ready to see it in action? Book a demo with the Bifrost team, or check out the Bifrost resources hub for step-by-step guides.
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