Enterprise LLM and MCP Gateway: Route, Govern, Secure

TL;DR

• An enterprise LLM and MCP gateway is one control plane for both model requests and MCP tool calls, applying authentication, routing, budgets, rate limits, and security policy to every request.
• Bifrost routes traffic to 25+ providers and 10,000+ models through one OpenAI-compatible API and governs MCP tools with per-virtual-key filtering and six MCP auth types.
• Guardrails for secrets, PII, and content safety apply to both LLM traffic and MCP tool executions, and audit logs record every administrative change.
• In-VPC, air-gapped, and clustered deployment keep AI traffic inside infrastructure the organization controls.

Enterprise AI traffic now flows through two distinct planes: requests to LLM providers, and tool calls routed through Model Context Protocol (MCP) servers. According to IBM's 2025 Cost of a Data Breach Report, 97% of organizations that reported a breach of AI models or applications lacked proper AI access controls, and a Cloud Security Alliance survey found that 82% of organizations discovered an AI agent or workflow in the past year that security or IT did not previously know about.

An enterprise LLM and MCP gateway addresses both planes from a single control point. Bifrost, the open-source AI gateway built in Go by Maxim AI, is the centralized layer enterprises use to route, govern, and secure all AI traffic across models, tools, and environments. This post covers how an enterprise LLM and MCP gateway works, why both planes need governance, and how Bifrost enforces access, budgets, and security policy across them.

What is an enterprise LLM and MCP gateway?

An enterprise LLM and MCP gateway is a single control plane that sits between applications and both LLM providers and MCP tool servers, applying authentication, routing, budgets, rate limits, and security policy to every request in either direction. It unifies model access and tool access behind one governed entry point instead of scattering provider keys and MCP connections across application code.

The two planes it governs are distinct:

• The LLM plane: chat completions, embeddings, and other inference requests sent to providers such as OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI.
• The MCP plane: tool calls that AI models make to external MCP servers for filesystem access, web search, database queries, and custom business logic.

Bifrost handles both. It exposes a single OpenAI-compatible API for model traffic and acts as an MCP gateway that aggregates connected tool servers, applies the same governance to tool calls, and exposes those tools to clients through one endpoint. The model side is covered in more depth in what an AI gateway is as the control plane for LLM traffic, and the tool side in the production guide to MCP gateways.

Why both planes need governance

Both planes need governance because an ungoverned model plane creates cost and compliance exposure, while an ungoverned tool plane lets a model read files, query databases, or call internal APIs without a policy check. Governance programs often start at the model plane and leave tool calls ungoverned. That gap matters because the two planes carry different risks, and an incident on either plane can affect production.

On the LLM plane, the operational signals are familiar to platform teams:

• Provider outages and rate-limit errors that interrupt production traffic.
• Untracked spend when every team holds its own provider keys.
• No central record of which application called which model with what data.

On the MCP plane, the risks are newer and less visible. A 2025 arXiv paper on securing the Model Context Protocol notes that the protocol prioritized interoperability over security, with OAuth support added to the specification only in March 2025 and authentication still frequently neglected in practice. Research by Knostic, cited in the paper, found over 1,800 MCP servers exposed on the public internet without authentication. Because an agent can reach multiple MCP servers, a single request can trigger cascading actions across several connected systems.

These risks compound. An enterprise LLM and MCP gateway closes both by making every model request and every tool call pass through one enforcement point.

The same gap shows up as shadow agents and unmanaged MCP servers, as covered in the enterprise MCP security checklist. For a deeper view of the access-control model, the governance resource page details how this consolidation works at scale.

How Bifrost governs LLM traffic

Bifrost governs LLM traffic through virtual keys, the primary governance entity in the system. Instead of distributing raw provider keys, platform teams issue virtual keys that carry their own access permissions, budgets, and rate limits, and applications authenticate against those rather than against the providers directly.

Each virtual key supports:

• Access control: model and provider filtering, so a key can be restricted to specific models or providers.
• Cost management: independent budgets enforced through a hierarchical structure that spans customers, teams, and individual keys.
• Rate limiting: token-based and request-based throttling per period.
• Key restrictions: limiting a virtual key to specific provider API keys.

How these controls combine across teams is covered in AI governance with virtual keys for LLM and MCP traffic.

Underneath the governance layer, Bifrost routes the actual model traffic. It unifies access to 25+ providers and 10,000+ models through a single OpenAI-compatible API and supports automatic failover between providers and models, so a provider outage redirects traffic to a configured fallback rather than failing the request. Load balancing distributes requests across multiple API keys with weighted selection. Using Bifrost as a drop-in replacement for an existing SDK requires changing only the base URL, which keeps the governance layer from disrupting developer workflows.

How Bifrost governs MCP traffic

Bifrost acts as an MCP gateway by serving as both an MCP client and an MCP server. It connects to external tool servers over STDIO, HTTP, or SSE, aggregates their tools into a single registry, and exposes that registry to clients such as Claude Desktop or Cursor through one MCP endpoint. This consolidation gives security teams a single place to control tool access instead of managing scattered MCP connections per application.

The governance controls on the MCP plane mirror those on the model plane:

• Tool filtering per virtual key: control which MCP tools a given key can call, so a key scoped to one team cannot reach tools meant for another.
• Authentication per server: configure one of six MCP auth types for each connected MCP server (None, Headers, Per-User Headers, OAuth 2.0, Per-User OAuth, and enterprise Token Exchange), with automatic token refresh for OAuth.
• Explicit execution by default: tool calls returned by a model are treated as suggestions, and execution requires a separate API call unless Agent Mode is explicitly configured with an auto-approval list.

For agentic workflows that orchestrate many tools, Code Mode lets the model write and execute Starlark code against four meta-tools rather than issuing one tool call per step, which cuts input tokens by up to 92.8% at around 500 tools, as explained in what Code Mode is and how it works. The MCP Gateway blog covers how this pattern delivers access control, cost governance, and lower token costs at scale. Routing tool calls through the same gateway that governs model calls means one MCP gateway configuration covers both planes, rather than two separate governance systems.

Securing AI traffic at the gateway

Security policy at an enterprise LLM and MCP gateway operates on the content of requests and responses, not just on access, through AI guardrails that inspect prompts, completions, and tool executions before they reach a model, a user, or a downstream system. Bifrost applies guardrails that validate LLM traffic and MCP tool executions in real time against configured policies, protecting against harmful content, prompt injection, PII leakage, and credential leakage.

Available guardrail mechanisms include:

• Secrets detection: Gitleaks-backed detection that catches leaked API keys, tokens, and credentials in prompts and completions.
• Custom regex and PII detection: in-process pattern rules for organization-specific redaction or rejection.
• Prompt guardrails: LLM-as-judge enforcement of organization-specific natural-language policies.
• Third-party providers: integrations with Microsoft Presidio, Azure AI Language PII, AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, CrowdStrike AIDR, Gray Swan Cygnal, and Patronus AI, among others.

For compliance, Bifrost records audit logs of administrative activity, capturing who changed what, when, and which resource was affected. Audit entries can be signed with an HMAC key, retained for a configurable period, and exported as JSON, JSON Lines, or Syslog for downstream review.

These trails support compliance reviews that require a configuration-change record, while request logs record the LLM and MCP calls themselves.

Access to the gateway itself is controlled through role-based access control, which provides Admin, Developer, and Viewer system roles plus custom roles, and integrates with OIDC identity providers for group-based role assignment.

Enterprise deployment and data control

For regulated industries and strict data-residency requirements, where the gateway runs is as important as what it governs. Bifrost supports in-VPC deployments across AWS, Google Cloud, Microsoft Azure, Cloudflare, and Vercel, so all AI traffic is processed within infrastructure the organization controls. This keeps data inside the network boundary and helps meet HIPAA, SOC 2, and GDPR obligations. The Bifrost Enterprise tier adds high-availability clustering, adaptive load balancing, and identity federation on top of the open-source gateway, as a strict superset where every open-source provider, integration, and SDK works identically.

Bifrost enterprise deployment supports:

• VPC isolation, on-prem, and air-gapped: run the gateway inside your own network, and in air-gapped environments load the pricing and model datasheets from local files.
• Clustering: high availability with automatic service discovery and zero-downtime deployments.
• Secret management: keep provider keys, virtual key values, and MCP auth headers in HashiCorp Vault, AWS Secrets Manager, or GCP Secret Manager instead of the Bifrost database.
• Log exports: offload request and response payloads to S3 or GCS object storage for long-term retention and queries from your own data lake.

Because Bifrost is open source at its core, teams can self-host and inspect the full request path before committing to an enterprise rollout. For a structured comparison of capabilities when evaluating options, the LLM Gateway Buyer's Guide lays out the criteria that matter for production deployments.

Key considerations for implementation

Teams adopting an enterprise LLM and MCP gateway should plan for both planes from the start rather than retrofitting tool governance later. The five steps below cover identity, tool scope, content security, audit, and deployment in the order most teams need them.

• Issue virtual keys per team or application, not raw provider keys, so budgets and rate limits are enforced from day one.
• Apply tool filtering before connecting MCP servers broadly, so each virtual key reaches only the tools its workload requires.
• Configure guardrails for secrets and PII on both inbound prompts and outbound completions, since leakage can occur in either direction.
• Enable audit logs early to build the compliance record before it is needed for an audit.
• Choose a deployment model that matches data-residency requirements, using in-VPC or on-prem for regulated workloads. For AI that runs on employee machines rather than in services, see governing enterprise LLM and MCP usage with Bifrost Edge and the gateway.

Each control compounds the others. Virtual keys define who can call what; guardrails define what content is allowed; audit logs record what happened; and deployment choice defines where the data lives.

Frequently asked questions

What is an AI gateway used for?

An AI gateway is used to route, govern, and secure traffic between applications and AI model providers through one API. It handles failover between providers, load balancing across keys, budgets and rate limits per team, guardrails on content, and logging for audit. An enterprise LLM and MCP gateway extends the same controls to the tool calls agents make through MCP servers.

What is the difference between an LLM gateway and an MCP gateway?

An LLM gateway governs inference requests to model providers, such as chat completions and embeddings. An MCP gateway governs the tool calls models make to MCP servers, such as file access, database queries, or internal APIs. Bifrost does both in one deployment, so the same virtual key controls which models a team can call and which tools it can execute.

Is there a self-hosted AI gateway available?

Yes. Bifrost is an open-source AI gateway that can be self-hosted, including self-hosted in-VPC installs on AWS, Google Cloud, Microsoft Azure, Cloudflare, and Vercel. Air-gapped installs are supported by loading datasheets from local files, and Bifrost Enterprise adds clustering, secret management, and identity federation for organizations with strict data-residency requirements.

Do guardrails apply to MCP tool calls?

Yes. Bifrost guardrails validate both LLM traffic and MCP tool executions, so secrets detection, PII redaction, and content safety policies apply to tool inputs and outputs as well as to prompts and completions. This matters because a tool call can move sensitive data just as easily as a model response.

How does an enterprise gateway reduce AI security risk?

An enterprise gateway reduces AI security risk by forcing every model request and tool call through one enforcement point with identity, scoped permissions, content inspection, and logging. That addresses the gap IBM's 2025 report identified, where 97% of organizations that reported a breach of AI models or applications lacked proper AI access controls.

Getting started with Bifrost

An enterprise LLM and MCP gateway gives platform and security teams a single point to route, govern, and secure AI traffic across both model providers and MCP tool servers. Bifrost unifies model access, MCP tool governance, guardrails, audit logging, and in-VPC deployment into one open-source gateway built for enterprise scale. Teams can start from the Bifrost resources hub or review the enterprise deployment options before rolling out.

To see how Bifrost can route, govern, and secure your enterprise LLM and MCP traffic, book a demo with the Bifrost team.