Connecting one MCP server to one agent is a five minute job. Connecting fifteen of them across four teams, each with its own credentials, its own tool catalogue and its own idea of what an agent should be allowed to touch, is an infrastructure problem. Tool definitions start to fill up the context window before the model has read your actual prompt, nobody can say which agent called which tool last, and the security team starts asking questions that nobody on the platform team can answer. That is the point where an MCP gateway stops being optional.
An MCP gateway sits between every AI client and every upstream tool server, exposing them all through a single endpoint and applying authentication, authorisation, logging and routing before any tool call reaches a real system. Bifrost, built by Maxim AI, is one of the clearest examples of the category. It is an open source AI gateway written in Go that handles LLM routing and MCP tool execution in the same deployment, and you can find it on the Bifrost website or pull the source from the GitHub repo. It competes with a mix of API platform veterans and model routers like Kong AI Gateway, Cloudflare MCP Server Portals, LiteLLM and OpenRouter. This article compares all five on the things that actually matter once you are past the prototype stage.
TL;DR
- Bifrost is the strongest overall choice for enterprise scale. Native MCP gateway, 11µs of internal overhead, Code Mode for token reduction, virtual keys scoped at the tool level, Apache 2.0 and self hostable
- Kong AI Gateway is the right call if Kong already governs your API estate and you want MCP traffic under the same plugin and policy model
- Cloudflare MCP Server Portals suits teams standardised on Cloudflare One who want Zero Trust identity checks applied to MCP traffic at the edge
- LiteLLM is a solid open source option for teams already running the Python proxy, best suited to smaller deployments
- OpenRouter is a model router instead of an MCP gateway. Useful alongside one, not instead of one
What Enterprise Scale Actually Demands From an MCP Gateway
Before the list, here is the rundown. Every gateway below is measured against the same six things.
- Token efficiency: Classic MCP injects every tool definition from every connected server into context on every request. At 500 tools that is the majority of your spend, and it gets worse as you connect more servers
- Access control granularity: Per server permissions are not enough. You need per tool, per team and per key control, so a customer facing agent cannot reach internal admin tooling
- Transport coverage: stdio for local processes, HTTP for remote servers, SSE for streaming. Missing one of the three limits which clients can connect
- Authentication: OAuth 2.0 or 2.1 with token refresh, SSO into your existing identity provider, and secrets that live somewhere better than an environment variable
- Observability and audit: Per tool logs, cost attribution, OpenTelemetry export, and something your SIEM can ingest when compliance comes asking
- Deployment boundary: Self hosted, inside a VPC, or air gapped. If your configuration cannot stay inside your own network, a lot of enterprise conversations end early
Enterprise deployment and governance
| Criterion | Bifrost | Kong | Cloudflare | LiteLLM | OpenRouter |
|---|---|---|---|---|---|
| Self hosted, VPC or air gapped | Yes | Self hosted plus Konnect | No | Yes | No |
| SSO, RBAC and audit logs | Yes | Yes | Yes | Partial | No |
| Per tool cost attribution | Yes | Metrics only | Logs only | Yes | Not applicable |
| Secrets management | Vault, AWS, GCP, Azure | Enterprise tier | Workers secrets | Environment or database | Not applicable |
| Licence | Apache 2.0 plus enterprise | Enterprise | Commercial | MIT plus enterprise | Commercial |
1. Bifrost: Best Overall for Enterprise Scale
Bifrost acts as both an MCP client and an MCP server in a single deployment. It connects out to your filesystem, database, search and custom API servers, discovers their tools automatically, and exposes everything back through one gateway URL that Claude Desktop, Claude Code, Cursor or any other MCP client can point at. Add a new server to Bifrost and it appears in every connected client with no client side configuration changes.
Key capabilities
- Performance: Maxim's published benchmarks show roughly 11µs of overhead at 5,000 requests per second with a 100% success rate, running on a single t3.xlarge instance. It is written in Go and tuned for the request path, with pooled memory, configuration held in memory and provider connections opened ahead of time, so memory stays flat as traffic grows. In a head to head test at 500 concurrent users, Bifrost delivered 9.5x higher throughput than LiteLLM, 54x faster P99 latency and 68% lower memory usage
- Code Mode: Instead of carrying every tool definition in context, Bifrost exposes connected servers as lightweight stub files. The model reads only what it needs, writes a short Python script to orchestrate the tools, and that script runs in a sandboxed Starlark interpreter with only the final result returning to context. Across large tool sets, input token use drops by up to 92.8%. In one 508 tool run, Code Mode cut the cost from $377 to $29 while holding a 100% task pass rate
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Governance at the tool level: Tool filtering works at the client, request and virtual key level, so a key can be granted
filesystem_readwithout ever receivingfilesystem_write. MCP Tool Groups bundle curated sets of tools from any connected server, and access profiles let platform teams define reusable MCP, model, budget and rate limit policies once, then hand out virtual keys automatically - Security first defaults: Tool calls returned by a model are treated as suggestions. Execution requires a separate call from your application. Agent Mode with automatic execution exists, but it is opt in and you configure exactly which tools can be approved automatically
- Enterprise tier: Identity based governance running off Okta, Microsoft Entra, Keycloak, Zitadel, Auth0, Google Workspace or any OIDC provider, with SCIM 2.0 directory sync. Fine grained role based and data access control, peer to peer cluster mode with no leader node, adaptive load balancing with circuit breakers, zero downtime deployments, guardrails, signed audit logs that cannot be altered, and secret management through HashiCorp Vault, AWS Secrets Manager or GCP Secret Manager. Deployment runs in VPC, on premises or fully air gapped, backed by SOC 2 Type II, ISO 27001, HIPAA and GDPR compliance
- Transports and auth: stdio, HTTP and SSE, with upstream authentication covering shared headers, OAuth 2.0, per user OAuth and token exchange, so each person can connect to tools under their own account
Setup is genuinely fast. npx @maximhq/bifrost has a gateway running in about 30 seconds, and the Docker image is roughly 80MB.
Where it falls short: It is a younger project than Kong, and the deepest governance features sit behind the enterprise tier instead of the open source build
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. Kong AI Gateway: Best for Existing Kong Estates
Kong made MCP a first class traffic type in Gateway 3.12, and the 3.14 release split the AI Gateway into three modes: LLM, MCP and Agent. All three are governed through the same Konnect control plane, which means the authentication, rate limiting and observability policies you already run against REST traffic extend to agent traffic.
Key capabilities
- An MCP proxy plugin that bridges existing REST APIs into MCP, so your internal services become agent callable without anyone rewriting them as MCP servers. This is Kong's real differentiator and nothing else on this list matches it
- An MCP OAuth2 plugin that centralises OAuth 2.1 across every connected server, with token exchange under RFC 8693
- MCP Registry inside Konnect, announced in February 2026, which catalogues and versions MCP servers alongside the APIs they depend on, complete with ownership and inherited policy
- MCP specific Prometheus metrics, plus the hundreds of existing Kong plugins covering mTLS, OIDC, rate limiting and OpenTelemetry
- Unauthorised tools are hidden at discovery time rather than merely blocked at execution, which shrinks what a compromised agent can even reason about
Where it falls short: MCP features are enterprise licensed with no free tier. Configuration flows through the Konnect control plane, which is a problem if your policy has to stay entirely inside your network. There is also no token efficiency layer, so context bloat remains your problem to solve.
Best for: organisations already running Kong for API management who want one governance story across APIs and agents.
3. Cloudflare MCP Server Portals: Best for Zero Trust at the Edge
One note worth making, because the naming causes real confusion. Cloudflare AI Gateway handles the LLM side of the equation: caching, retries, model fallback, rate limits and analytics. The MCP gateway capability lives somewhere else, in Cloudflare One under Zero Trust MCP Server Portals. AI Gateway is adjacent to MCP authorisation, not a replacement for it, and Cloudflare's own documentation is clear on that.
Key capabilities
- A single portal URL aggregates every registered MCP server. Users configure one endpoint, and newly onboarded servers appear automatically
- Access policies run through your existing identity provider, so multi factor authentication, device posture checks and geographic restrictions all apply to MCP traffic
- Administrators curate which servers and which specific tools appear in each portal, which keeps least privilege intact and reduces context exposure at the same time
- Every request, prompt invocation and tool execution lands in Cloudflare's unified Access logs
- Available in open beta across Cloudflare One plans, with the Workers free tier covering 100,000 requests per day
Where it falls short: There is no self hosted option, so this is cloud only by definition. Logpush for portal logs depends on your plan tier. AI prompt DLP profiles do not apply to portal traffic, which surprises people. And like Kong, there is no token reduction layer.
Best for: teams already standardised on Cloudflare One who want the tightest identity model around MCP traffic.
4. LiteLLM: Best Open Source Starting Point
LiteLLM is the open source proxy that a lot of teams reach for first, and its MCP Gateway extends the same idea to tools. The proxy exposes a fixed /mcp endpoint that any MCP aware client can connect to.
Key capabilities
- Full MCP operation coverage including listing tools, calling tools, prompts and resources
- All three transports: streamable HTTP, SSE and stdio
- Permission management by key, team and organisation, with tool level access control added in v1.78.0. Engineering can get the repository tools while sales only sees search
- OAuth 2.0 authorisation server discovery by default, with dynamic client registration and PKCE
- Per tool cost tracking, a single audit trail, and an admin UI for onboarding servers
Where it falls short: It is a Python proxy, and the performance profile reflects that under sustained load. The Docker image runs well past 700MB against Bifrost's 80MB. There is no clustering or adaptive load balancing, and no code execution layer to keep tool schemas out of context.
Best for: teams already running LiteLLM who want MCP governance without introducing a second system.
5. OpenRouter: Best for Model Access, Not Tool Governance
OpenRouter belongs in this comparison because it comes up constantly in gateway conversations, but it is worth being precise about what it does. It is a model router: one endpoint compatible with the OpenAI API covering 500+ models across 80+ providers, with automatic fallback when a provider fails.
Key capabilities
- Typed SDKs in TypeScript, Python and Go, plus an Agent SDK with multi turn tool loops and stop conditions
- An MCP server at
mcp.openrouter.ai/mcpthat gives any MCP client access to the full model catalogue - A Terraform provider for managing configuration as code
- Unified billing across every provider
Where it falls short: Notice the direction of travel. OpenRouter exposes models to MCP clients instead of governing your MCP servers, which is the reverse of what an MCP gateway does. There is no self hosting, no tool level policy and no per tool audit trail.
Best for: teams that want a lot of model access, running alongside a genuine MCP gateway instead of in place of one.
MCP Gateway Comparison
Core MCP capabilities
| Capability | Bifrost | Kong | Cloudflare | LiteLLM | OpenRouter |
|---|---|---|---|---|---|
| Native MCP gateway | Yes | Yes | Yes, via Portals | Yes | No |
| Transports supported | stdio, HTTP, SSE | HTTP | HTTP | stdio, HTTP, SSE | Not applicable |
| Tool level access control | Yes | Via plugins | Per portal curation | Yes | No |
| Token reduction layer | Code Mode | No | No | No | No |
| Acts as MCP client and server | Both | Both | Server side | Both | Model access only |
| Explicit approval by default | Yes | Policy driven | Policy driven | No | Not applicable |
The Verdict
If you are choosing a gateway to carry production agent traffic across multiple teams, Bifrost is the one to beat, for four specific reasons.
One gateway, one policy model: LLM calls and MCP tool executions flow through the same virtual keys, the same hierarchical budgets and the same audit log. When something goes wrong at 2am, you can trace a full agent run end to end with model token costs and tool costs sitting side by side, rather than stitching together two systems that disagree about what happened.
Cost control that scales in the right direction: Every other option on this list gets more expensive per request as you connect more servers, because every tool definition rides along in context. Code Mode inverts that relationship. Cost tracks what the model actually reads, not how many tools happen to exist, and the published benchmarks show the pass rate holding at 100% while token usage falls by up to 92.8%.
Governance that stops at the tool, not the server: Virtual keys and MCP Tool Groups let you hand a customer facing agent exactly the four tools it needs from three different servers, and nothing else. The model never receives definitions for anything outside its scope, so the failure mode most teams worry about simply cannot occur.
Your deployment boundary, your decision: Self hosted, inside a VPC or fully air gapped, with Vault backed secrets, signed audit logs and a logging pipeline designed around SOC 2, GDPR, ISO 27001 and HIPAA requirements. Content logging can be switched off per environment while still capturing tool name, server, latency and status.
Conclusion
The honest summary is that your choice comes down to two questions. Where is your configuration allowed to live, and do you need the token layer? If you are deep in Kong or Cloudflare already, extending what you have is a reasonable call. If you are starting fresh, running agents against a growing set of MCP servers, and you need governance and cost control that hold up as that set grows, Bifrost is the one built for exactly that shape of problem.
It takes about 30 seconds to try. Spin it up with npx @maximhq/bifrost, point a client at the gateway URL, and see what your token usage looks like with Code Mode switched on.
Start here: Go to the Bifrost MCP Gateway page, if you want to talk through an enterprise deployment. There is a 14 day enterprise trial available too and you can always book a demo.
Top comments (1)
Magic Cloud and Hyperlambda runs in circles around all of the above. Don't believe me, run it through Claude and ask her!