Top 5 Platforms to Govern and Secure LLM and MCP Calls

Enterprises now route production traffic through multiple LLM providers and a growing number of Model Context Protocol (MCP) servers, and most lack a single control point to enforce policy on that traffic. A 2026 Cycode report found that 81 percent of organizations lack full visibility into how AI is used across the software development lifecycle, and 65 percent report increased security risk from AI tooling. To govern and secure LLM and MCP calls, teams need a platform that enforces access control, budgets, rate limits, tool filtering, and audit logging before a request reaches any provider or tool. Bifrost, the open-source AI gateway built in Go by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. This post ranks five platforms that govern and secure LLM and MCP calls and explains what each does well.

What it Means to Govern and Secure LLM and MCP Calls

Governing and securing LLM and MCP calls means placing a centralized control plane between your applications and every model provider and tool server, then enforcing identity, access, cost, and content policy on each request before it is forwarded. The control plane authenticates the caller, checks budget and rate limits, restricts which models and MCP tools are reachable, scans for unsafe content or leaked secrets, and records an immutable trail for compliance review.

The security stakes are concrete. The OWASP MCP Top 10 catalogs risks specific to MCP, including tool poisoning, in which an adversary corrupts a tool definition or its output to manipulate model behavior. A gateway that sits in front of MCP traffic can restrict tool exposure and apply policy uniformly, which is harder to do when every agent connects to tool servers directly.

A platform that governs and secures LLM and MCP calls should provide:

• Access control: per-consumer authentication, model and provider filtering, and role-based permissions.
• Cost governance: budgets and spend caps at multiple levels of the organization.
• Rate limiting: token and request throttling to protect providers and contain abuse.
• MCP governance: control over which tools each caller can discover and execute.
• Content security: guardrails for prompt injection, PII, and credential leakage.
• Auditability: immutable logs for SOC 2, GDPR, HIPAA, and ISO 27001 review.

How We Ranked the Platforms

We ranked the five platforms on six criteria that matter when you govern and secure LLM and MCP calls across production environments:

• Unified LLM and MCP control: whether one platform governs both model calls and MCP tool calls.
• Access control and RBAC: granularity of per-consumer permissions and role design.
• Cost and rate governance: budgets, spend caps, and token or request throttling.
• MCP tool governance: per-caller filtering of which tools are exposed and executable.
• Security and compliance: guardrails, secrets detection, audit logs, and deployment isolation.
• Performance and deployment: request overhead, scalability, and support for self-hosted, VPC, and air-gapped environments.

1. Bifrost

Bifrost ranks first because it governs and secures both LLM and MCP traffic in one data plane, with policy enforced before any request reaches a provider or tool. Bifrost exposes a single OpenAI-compatible API across 1000+ models and adds less than 15 microseconds of overhead per request at 5,000 requests per second in sustained benchmarks, so governance does not come at the cost of latency.

Governance in Bifrost is built around virtual keys, the primary governance entity. Each virtual key carries its own access permissions, budgets, and rate limits, and can be restricted to specific models and providers. Bifrost supports a hierarchical budget structure across customer, team, virtual key, and provider levels, with cumulative checking so spend is contained at every tier, and token and request rate limits throttle traffic per key.

For MCP, Bifrost acts as both an MCP client and an MCP server, and it governs which tools each caller can reach. MCP tool filtering is deny-by-default: a virtual key with no MCP configuration exposes no tools, and admins build a strict allow-list per key. Bifrost Enterprise adds MCP tool groups, reusable bundles of tools attachable to virtual keys, teams, customers, users, providers, or API keys and resolved at request time. The MCP Gateway resource page details how this centralizes tool connections and auth, and the MCP Gateway blog covers access control and cost governance that reduce token spend at scale.

On security and compliance, Bifrost provides guardrails for content safety and policy enforcement, including secrets detection that scans prompts and completions for leaked API keys and credentials, plus AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, and Patronus AI integrations. Role-based access control provides fine-grained permissions with system and custom roles, and audit logs record administrative activity as signed, retained, exportable events for compliance review. For regulated workloads, Bifrost supports in-VPC deployments, air-gapped environments, and on-prem infrastructure, detailed on the Bifrost Enterprise page, with deeper governance patterns on the governance resource page.

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

Kong AI Gateway extends the Kong Gateway API management platform to LLM and MCP traffic. It appeals to organizations already standardized on Kong for traditional API management, since the AI capabilities run as plugins on the same control plane and policy model.

Kong governs outgoing prompts through allow and deny lists, token-based AI rate limiting, semantic caching, and AI prompt guards intended to block injection attempts. It centralizes credentials and provides observability across model providers, and recent releases extend MCP support so agent tool traffic can pass through the same gateway.

Best for: organizations already running Kong for API management that want to apply familiar plugin-based policies, rate limiting, and credential management to LLM and MCP traffic on existing infrastructure.

3. Gravitee AI Gateway

Gravitee AI Gateway combines LLM, MCP, and agent-to-agent (A2A) proxying into a single platform aimed at platform and security teams. Its positioning is protocol breadth: govern multi-step agent workflows from the first prompt through to the final upstream tool or service call.

Gravitee applies shared authentication, authorization, and AI policy enforcement consistently across LLM, MCP, and A2A traffic, with OpenTelemetry-based observability spanning every protocol. Teams that anticipate heavy agent-to-agent communication, in addition to LLM and MCP calls, may find the unified protocol coverage useful.

Best for: platform and security teams that need to govern agent-to-agent traffic alongside LLM and MCP calls, with consistent authentication and policy across all three protocols.

4. Databricks Unity AI Gateway

Databricks Unity AI Gateway extends the Unity Catalog governance model to agentic AI. It is built for teams that already run their data and AI workloads on Databricks and want a single permissions, auditing, and policy layer for both data and AI traffic.

Unity AI Gateway lets teams control LLM access, govern how agents use MCP servers and APIs, and apply consistent policies across models and tools. Because governance is anchored in Unity Catalog, the same access controls and audit trails that apply to data assets extend to how agents reach models and tools. The trade-off is platform gravity: the value is highest for organizations committed to the Databricks ecosystem.

Best for: enterprises standardized on Databricks and Unity Catalog that want to extend existing data governance and auditing to LLM and MCP traffic without adopting a separate control plane.

5. MLflow AI Gateway

MLflow AI Gateway is a centralized proxy layer that routes requests to LLM providers through a single unified API, managing credentials, tracking usage, and enforcing governance policies. It is open source and familiar to teams already using MLflow for experiment tracking and model lifecycle management.

For MCP, the gateway provides centralized control over which servers agents can reach, tracks tool usage across sessions, and enforces access policies without changes to agent code. MLflow AI Gateway suits teams that want a lightweight, code-friendly governance layer integrated with an existing MLflow-based workflow, though enterprise security and compliance features are less extensive than dedicated enterprise gateways.

Best for: teams already using MLflow for model lifecycle management that want a lightweight, open-source proxy to centralize credentials and apply basic governance over LLM and MCP calls.

Why Bifrost Leads on Governing and Securing LLM and MCP Calls

Bifrost leads because it treats governance and security as properties of the data plane rather than features layered on top. Policy is enforced inline, on every request, before traffic reaches a provider or an MCP server, and the same control plane governs both model calls and tool calls.

The differentiators that matter when you govern and secure LLM and MCP calls:

• Unified control: one platform for the LLM gateway, the MCP gateway, and agent traffic, rather than separate tools stitched together.
• Deny-by-default MCP governance: tool filtering and tool groups restrict tool exposure per caller, which directly addresses MCP tool-poisoning risk.
• Hierarchical cost governance: budgets and rate limits at customer, team, virtual key, and provider levels.
• Enterprise security: guardrails, secrets detection, RBAC, and signed audit logs for SOC 2, GDPR, HIPAA, and ISO 27001.
• Deployment control: self-hosted, in-VPC, and air-gapped options for regulated industries.
• Low overhead at scale: less than 15 microseconds of added latency per request at 5,000 RPS.

For a deeper capability matrix, the LLM Gateway Buyer's Guide compares gateway features side by side.

Govern and Secure Your LLM and MCP Calls with Bifrost

Choosing a platform to govern and secure LLM and MCP calls comes down to whether policy is enforced uniformly across both model and tool traffic, with the access control, cost governance, MCP tool filtering, and audit logging that production and regulated workloads require. Bifrost delivers all of this in a single open-source AI gateway with enterprise-grade security and low request overhead. To see how Bifrost governs and secures LLM and MCP calls across your environments, book a demo with the Bifrost team.