5 Best MCP Gateways in 2026

If you’re building AI agents in 2026, the difficult part is no longer making a model call.

It’s making the model safely use real systems.

GitHub access. Databases. CRMs. Internal APIs. Cloud infrastructure. File systems. SaaS applications. Every MCP server introduces another transport, credential, tool catalog, session, permission model, and operational surface.

Among the options below, Bifrost by Maxim AI stands out for pairing an Apache 2.0 open-source core with a production-focused control plane. Its GitHub repository is available for teams that want to inspect, run, and extend the gateway themselves.

At small scale, connecting an agent directly to a few MCP servers works fine.

At production scale, teams start dealing with:

• Credentials scattered across agent configurations
• Hundreds of tools bloating the model context
• Destructive tools exposed to the wrong users
• No unified audit trail for tool calls
• MCP servers using different transports
• OAuth tokens expiring during long-running workflows
• No reliable way to attribute tool usage to a user, team, or customer

That’s where MCP gateways become core infrastructure.

An MCP gateway sits between AI clients and MCP servers, giving teams one place to manage discovery, authentication, authorization, routing, observability, and policy enforcement.

This article compares the 5 best MCP gateways in 2026 based on real production concerns: security, transport support, governance, reliability, deployment flexibility, developer experience, and cost control.

We’ll start with a quick comparison table, then examine how each gateway behaves in production.

What Is an MCP Gateway (And Why Does It Matter in Production)?

MCP is an open protocol for connecting AI applications with external data sources and tools.

An MCP server can expose:

• Tools that an AI model can call
• Resources that provide context or data
• Prompts and reusable workflows
• Authentication and session handling

An MCP gateway sits in front of one or more MCP servers and presents a controlled interface to AI clients such as Claude, Cursor, ChatGPT, VS Code, or custom agents.

A production MCP gateway should provide:

• One endpoint for multiple MCP servers
• Centralized authentication and credential management
• Tool-level filtering and access control
• User, team, agent, or tenant-level permissions
• Session-aware routing
• Rate limits and usage budgets
• Per-call logs, metrics, and traces
• Support for remote and local MCP transports
• Safe handling of OAuth tokens and secrets

Without a gateway, every agent becomes its own integration hub.

That means each agent must independently manage server URLs, tool schemas, credentials, retries, permissions, and logs. The result is an N×M integration problem: N agents connected directly to M tools.

An MCP gateway reduces that complexity to a governed control point.

How We Evaluated These MCP Gateways

This comparison focuses on production readiness rather than feature-count marketing.

The main criteria were:

• Security and identity: OAuth, SSO, RBAC, delegated access, and secret handling
• Tool governance: allowlists, virtual servers, approvals, scopes, and policy enforcement
• Transport compatibility: STDIO, HTTP, SSE, and Streamable HTTP
• Observability: audit logs, latency, errors, traces, and usage attribution
• Reliability: session handling, retries, scaling, and high availability
• Deployment: SaaS, VPC, on-premises, Kubernetes, and air-gapped options
• Context efficiency: discovery, schema loading, and tool-token reduction
• Pricing and operating model

The ranking is based on production breadth and architectural fit, not on one synthetic cross-vendor benchmark.

Quick Comparison: Top 5 MCP Gateways

Gateway	Delivery	Open Source	Strengths	Tradeoffs	Pricing Model	Best For
Bifrost	Self-hosted single binary and Enterprise	✓ Apache 2.0 core	Unified MCP, LLM, and agent control plane; governance; air-gapped deployment; high performance	Enterprise features require a paid tier	Free self-hosted; Enterprise custom	Production AI systems that need one control plane
IBM ContextForge	Self-hosted Python, Docker, and Kubernetes	✓ Apache 2.0	MCP, A2A, REST/gRPC federation; registry; plugins; observability	Broad architecture requires more operational ownership	Free software; infrastructure costs apply	Platform teams that want open-source federation
Kong AI Gateway	Konnect-managed or self-hosted	Varies	API-to-MCP mapping; aggregation; authentication; policies; metrics and audit logs	Heavier API-management setup	Commercial and enterprise pricing	Existing Kong and API-platform teams
Azure API Management AI Gateway	Managed Azure AI gateway and API Management	✗	One governed endpoint for models and MCP; OpenAPI-to-MCP; connectors; runtime keys; policies	Public preview; regions, limits, and pricing may change	Preview pricing to be announced; Azure costs apply	Azure-native platform teams
Arcade MCP Gateways	Managed Cloud, VPC, and air-gapped Enterprise	✗	Tool curation; user-scoped OAuth; remote MCP federation; action-focused integrations	Remote MCP support is Streamable HTTP and tools-focused	Free tier; Team from $25/month plus usage; Enterprise custom	Applications needing user-authorized actions

These gateways overlap, but they solve slightly different problems. Some are full AI control planes. Others focus on tool federation, API management, managed connectors, or user-level authorization.

1. Bifrost (by Maxim AI)

Many MCP gateways focus on aggregating tools.

Bifrost takes a broader approach: MCP traffic is part of the same AI infrastructure as model calls and agent activity.

According to Maxim AI’s official Bifrost reference, Bifrost is an enterprise AI gateway and control plane for LLMs, MCP tools, and agents. The open-source core is licensed under Apache 2.0 and can run inside a company’s own infrastructure.

That broader scope is what puts Bifrost at number one.

One control plane for MCP, models, and agents

Bifrost can connect to upstream MCP servers over:

• STDIO
• HTTP
• SSE

It can also expose connected tools through a single MCP endpoint for clients such as Claude Desktop, Cursor, Claude Code, and custom agents.

The result is one place to:

• Register MCP servers
• Discover and synchronize tools
• Filter tools by client, request, or virtual key
• Create curated virtual MCP servers
• Apply user, team, customer, or business-unit policies
• Authenticate upstream servers
• Record every tool call

Bifrost supports server-level and per-user OAuth 2.0, automatic token refresh, shared headers, and token exchange. Credentials remain in the gateway instead of being copied into every agent configuration.

Governance is built into the request path

Bifrost combines tool governance with broader AI governance controls.

Teams can use:

• Role-based access control
• Data access policies
• Virtual keys
• Budgets and rate limits
• Tool groups and access profiles
• Guardrails for requests and responses
• Personal-data and secret detection
• Signed audit logs
• Prometheus metrics and OpenTelemetry tracing

This matters because MCP tools are not just read-only information sources. They can create tickets, modify infrastructure, send messages, move money, or delete data.

The gateway needs to understand which tool is being called, who is calling it, and whether that action is allowed.

MCP Code Mode reduces context pressure

Large MCP installations can expose hundreds of tools. Sending every tool definition to the model on every request increases token usage and makes tool selection less reliable.

Bifrost’s MCP Code Mode lets the model orchestrate tools through sandboxed code and load schemas only when needed.

The official reference reports input-token reductions of up to 92.8% across large tool sets. Maxim AI also reports a published example where a 508-tool workflow reduced token cost from $377 to $29 while maintaining a 100% task pass rate.

This is one of Bifrost’s most interesting advantages for agent-heavy systems: the gateway is not only routing tool calls; it is also reducing the cost of deciding which tools to call.

Performance and deployment

Bifrost is written in Go and is designed for the request path. Its architecture keeps configuration in memory, reuses pooled memory, and maintains provider connections ahead of time.

Maxim AI’s official reference reports approximately 11 microseconds of gateway overhead at 5,000 requests per second on a single t3.xlarge instance. That is a vendor-published benchmark, so teams should validate results against their own MCP server latency and workload patterns.

Deployment is flexible:

• Virtual machines
• Kubernetes
• Private cloud
• In-VPC environments
• On-premises infrastructure
• Fully disconnected or air-gapped networks

Bifrost runs inside the customer’s infrastructure, and the official reference states that prompts, responses, files, and telemetry do not leave that environment.

Teams can also add AI guardrails and route operational data into existing monitoring systems through AI observability integrations.

Pricing

Bifrost’s open-source version is free forever for self-hosted use under Apache 2.0.

The Enterprise tier is custom priced and adds features such as:

• High availability
• Cluster mode
• Identity-based governance
• Advanced security
• Compliance controls
• Enterprise support

Bifrost Enterprise includes a 14-day trial. See current Bifrost pricing before making a purchasing decision.

Best for

Teams running high-traffic, customer-facing, regulated, or multi-tenant AI systems where MCP tools need to follow the same policies as LLM and agent traffic.

If you are evaluating it, start with the Bifrost MCP Gateway documentation and the open-source repository.

2. IBM ContextForge

IBM ContextForge is one of the broadest open-source options in this category.

It acts as an MCP registry, proxy, and federation layer, but also supports A2A, REST, and gRPC services.

That makes it useful for organizations whose internal tool ecosystem is not uniformly MCP-based yet.

What stands out

ContextForge supports:

• Federation across multiple MCP servers
• REST-to-MCP adaptation
• gRPC-to-MCP translation
• HTTP, JSON-RPC, WebSocket, SSE, and Streamable HTTP
• Virtual servers containing selected tools
• JWT, Basic Auth, and custom authentication
• Rate limiting
• OAuth and user-scoped access
• Admin UI and real-time logs
• Prometheus metrics
• OpenTelemetry tracing
• Redis-backed caching and session storage
• Multi-cluster Kubernetes deployments
• Plugin extensibility

It can wrap legacy REST or gRPC services as MCP-compatible tools, which can reduce the need to rewrite existing internal APIs.

Tradeoffs

ContextForge’s breadth is also its main tradeoff.

This is a platform to operate, not just a lightweight reverse proxy. Production deployments may require PostgreSQL, Redis, Kubernetes configuration, secret management, backups, upgrades, and monitoring.

Teams also remain responsible for securing the gateway and validating downstream tool behavior. The software provides the control surface, but operating a reliable self-hosted control plane still requires platform engineering effort.

Pricing

ContextForge is open source under Apache 2.0. There is no gateway license fee for self-hosting, but teams pay for infrastructure, operations, and any commercial support they choose to add.

Best for

Platform teams that want an open-source, self-hosted gateway capable of federating MCP with REST, gRPC, and A2A services.

3. Kong AI Gateway

Kong AI Gateway brings MCP into an established API-management platform.

It supports three traffic types through a unified control plane:

• LLM traffic
• MCP traffic
• Agent-to-agent traffic

Kong’s MCP layer can map REST API endpoints into MCP-compatible tools, aggregate multiple MCP servers, and expose those tools to clients such as Claude Desktop and Cursor.

What stands out

Kong provides:

• AI MCP Server entities
• REST-to-MCP mapping
• Multiple MCP server aggregation
• Key-based and OpenID Connect authentication
• OAuth protected-resource metadata
• ACLs and rate-limiting policies
• MCP audit logs
• Latency, response-size, and error metrics
• OpenTelemetry integrations
• Konnect-based management
• Self-hosted deployment options

For companies already operating Kong, this can be a natural extension of an existing API governance model.

MCP tools can be managed alongside other APIs rather than becoming a completely separate infrastructure category.

Tradeoffs

Kong is powerful, but it assumes familiarity with API management, control planes, data planes, policies, and Kong configuration.

It is usually more infrastructure than a small team needs for a handful of MCP servers. The MCP registry functionality is also marked as a technology preview in the current documentation.

Pricing

Kong AI Gateway is commercially positioned through Kong Konnect and enterprise deployments. Pricing depends on the selected control-plane, data-plane, support, and deployment model.

Best for

Enterprises already standardized on Kong or teams that want MCP governance integrated into an existing API platform.

4. Azure API Management AI Gateway

Azure API Management AI Gateway is Microsoft’s managed gateway for AI models and MCP tools. The AI Gateway tier is currently in public preview.

It gives platform teams one place to publish, secure, govern, and observe AI traffic.

A single MCP server can federate backends from:

• Remote MCP servers
• OpenAPI specifications
• Built-in SaaS connectors

That makes it possible to bring existing APIs and MCP services behind one governed endpoint without rewriting every backend.

What stands out

Azure AI Gateway provides:

• Runtime access keys for client applications
• Backend authentication through API keys, OAuth 2.0, or managed identity
• Content-safety policies
• IP filters
• Request and token rate limits
• Azure Monitor and Application Insights integration
• OpenTelemetry support
• A self-service model and tool catalog
• Private networking options

The gateway keeps backend credentials away from client applications. Agents authenticate to the gateway, while the gateway handles authentication to the underlying model or tool service.

Azure API Management can also expose REST API operations as MCP tools, which makes it attractive for enterprises with a large existing API estate.

Tradeoffs

The main limitation is preview maturity.

Microsoft currently lists the AI Gateway tier in East US 2 and Sweden Central, with availability, quotas, telemetry, and APIs subject to change. The preview has no service-level agreement, so teams should use controlled pilots and maintain a rollback plan for critical workloads.

Azure API Management’s MCP capabilities currently focus on tools rather than MCP resources or prompts.

Pricing

Microsoft states that pricing and the business model for the AI Gateway tier will be announced later. Related Azure services, observability resources, networking, and backend providers may still generate costs.

Best for

Azure-native organizations that want managed governance for models, APIs, and MCP tools through Microsoft identity, networking, and monitoring.

5. Arcade MCP Gateways

Arcade MCP Gateways are designed around managed tool access and user-authorized actions.

A gateway can combine tools from multiple MCP servers into one endpoint. Administrators choose exactly which tools are exposed, and different gateways can provide different tool bundles for different projects or workflows.

What stands out

Arcade supports:

• Tool federation from multiple MCP servers
• Per-gateway tool selection
• Server instructions for agents
• Remote MCP server registration
• OIDC-based user identity
• OAuth-based authentication
• Project and organization scopes
• Dashboard-based gateway creation
• AI-assisted gateway configuration
• Streamable HTTP connections
• Managed tool and connector catalogs

The user identity model is particularly useful for applications where an agent must act on behalf of an end user rather than using one shared service account.

Arcade supports Arcade Auth, an external OIDC User Source, or API-key-style headers for clients that cannot complete a browser-based OAuth flow.

Tradeoffs

Arcade’s remote MCP support currently focuses on Streamable HTTP servers that are reachable from Arcade.

Its documentation also states that remote MCP gateways expose tools, while prompts, resources, and sampling are not supported for remote servers today.

That makes Arcade a strong fit for action-oriented integrations, but it is not the same type of full AI infrastructure control plane as Bifrost or Kong.

Pricing

Arcade publishes:

• Free: $0 per month, including 2,000 auth events and 2,000 tool calls per month
• Team: $25 per month plus usage
• Auth events: $0.10 each
• Tool calls: $0.01 each
• Enterprise: Custom pricing with VPC, air-gapped deployment, SSO, RBAC, audit logs, and private registry access

See the Arcade pricing page for current limits and terms.

Best for

Product teams that need user-scoped authorization, managed integrations, and a simple way to expose carefully selected tools to agents or IDE clients.

How to Choose the Right MCP Gateway

There is no universal winner for every MCP architecture.

Ask yourself:

Do MCP calls need to follow the same policy as model calls?

If your organization is managing LLMs, agents, and MCP tools as one production system, a unified AI gateway is usually the cleanest model.

Bifrost is especially strong here because its control plane covers LLM, MCP, and agent traffic, with self-hosted and air-gapped deployment options.

Kong is a strong alternative for teams that already manage APIs through its platform.

Do you need open-source federation?

If you need to connect MCP servers with REST, gRPC, or A2A services while keeping the gateway in your own infrastructure, IBM ContextForge is worth serious consideration.

Bifrost is also a strong option when performance, unified AI governance, and a simpler deployment model matter more than broad protocol federation.

Are you primarily trying to roll out approved connectors?

Azure API Management AI Gateway is a strong fit when your connectors and APIs already live in Azure. It can federate remote MCP servers, expose operations from OpenAPI definitions as tools, and connect to built-in SaaS integrations through one governed endpoint.

Arcade is compelling when the main requirement is exposing user-authorized actions through curated gateways.

Which MCP transports do your servers use?

Transport compatibility matters more than many comparison tables suggest.

If you depend on local STDIO servers, verify support before choosing a managed gateway. If your servers are already exposed through Streamable HTTP, options such as Arcade and Kong become easier to evaluate.

Can you enforce tool-level permissions?

Server-level access is not enough.

A gateway should let you expose read-only tools while hiding destructive operations. For example, an agent may be allowed to read a GitHub repository but not merge a pull request, query a database but not modify rows, or inspect cloud resources but not delete them.

Can you explain every tool call later?

For production systems, logs should answer:

• Which user or agent initiated the call?
• Which MCP server handled it?
• Which tool was executed?
• What authorization policy applied?
• How long did it take?
• Did it fail, retry, or time out?
• What did it cost?
• What model request caused it?

If the gateway only shows server uptime, it is not providing enough operational visibility.

How large will your tool catalog become?

A gateway should help agents discover tools without forcing every schema into every prompt.

For large MCP installations, evaluate lazy discovery, virtual servers, tool groups, schema loading, and approaches such as Bifrost MCP Code Mode.

Final Thoughts

MCP is becoming the standard interface between AI agents and the systems they need to use.

That also means MCP gateways are becoming more than connection managers. They are becoming policy boundaries.

The right gateway determines:

• Which tools an agent can see
• Which actions it can execute
• Which identity is attached to the call
• Where credentials are stored
• How sessions are routed
• How failures are handled
• What gets logged
• How tool usage is controlled and optimized

If you want open-source federation across MCP, REST, gRPC, and A2A, IBM ContextForge is a strong choice.

If you want MCP governance integrated with an established API-management platform, Kong is compelling.

If you want managed Azure governance for models, APIs, and MCP tools, Azure API Management AI Gateway is worth evaluating, subject to its preview limitations.

If your priority is user-authorized actions through curated tool bundles, Arcade is a good fit.

But for teams building high-traffic production AI systems, Bifrost is the strongest overall option because it combines MCP, LLM, and agent governance in one control plane. Its Apache 2.0 core, self-hosted deployment, air-gapped support, tool-level policies, Code Mode, observability, and performance focus give it a particularly strong foundation for serious agent infrastructure.

Start with the Bifrost MCP Gateway, review the documentation, explore the Bifrost resources, and validate the architecture against your own tools and security requirements.

Thanks for reading! 🙏