The Best MCP Gateway Options for Platform Engineering Teams
Platform engineering teams build the internal infrastructure that other teams consume. When AI agents enter the picture, the platform team inherits a new responsibility: providing a governed, self-service way for engineering, product, and business teams to connect agents to tools — without creating the kind of ungoverned sprawl that undermines everything a platform team exists to prevent.
An MCP gateway is the control plane for that responsibility. It centralizes tool access, credential management, observability, and policy enforcement into infrastructure that the platform team operates and other teams consume.
This guide covers the best MCP gateway options for platform engineering teams.
Why Platform Teams Need an MCP Gateway
The N×M Integration Problem Is a Platform Problem
Without a gateway, every team configures every agent’s connection to every tool independently. Ten agents connecting to ten tools is 100 separate configurations to manage. The platform team’s job is to collapse that to a manageable control point — and a gateway does exactly that.
Internal Developer Platforms Need an MCP Layer
Platform teams are building IDPs (Internal Developer Platforms) that abstract infrastructure complexity for other teams. MCP gateways extend that abstraction to AI tooling: approved servers in a registry, self-service access with appropriate permissions, and centralized observability for the platform team.
Tool Sprawl Is the Enemy of Platform Stability
Community MCP servers vary wildly in quality, security, and maintenance. Without curation, different teams adopt different servers for the same tool, with different configurations and different risk profiles. A gateway with a curated registry gives the platform team a single source of truth for approved MCP infrastructure.
MCP Manager by Usercentrics
Best MCP Gateway for Platform Teams That Need to Ship Governance Quickly
MCP Manager provides the governance layer that platform teams need to offer self-service MCP access to other teams without building that infrastructure internally. The private MCP registry with one-click deployment lets the platform team maintain an approved catalog and push it across clients organization-wide.
RBAC and ABAC scope access at the team, agent, tool, and operation level — enabling the platform team to define what each consuming team can see and do. Tool and team provisioning keeps manifests lean per consumer, reducing token waste. PII detection, runtime guardrails, and SIEM integration via OpenTelemetry give the platform team security controls that work without constant manual oversight.
Org-wide dashboards provide the centralized visibility platform teams need to understand how MCP infrastructure is being consumed across the organization. Pricing scales with capabilities used.
You can try MCP Manager for free by booking an onboarding call.
Obot
Best for Platform Teams Building a Self-Service MCP Platform on Kubernetes
Obot is the closest thing to a turnkey internal MCP platform available in open source. It combines server hosting, a searchable registry, a gateway routing layer, and a built-in chat client — all Kubernetes-native and manageable through the admin UI or GitOps workflows.
For platform teams, the registry and catalog are the key differentiators. You curate approved MCP servers, tag them with metadata and documentation, and publish them for internal teams to discover and connect to. Employees browse the catalog, select tools, authenticate through the enterprise IdP, and start using them — without filing infrastructure tickets or configuring anything manually.
MCP servers run as containers in your Kubernetes cluster with per-user isolation options for sensitive workloads. The shim architecture keeps credentials isolated from server processes. Configuration can be managed declaratively through GitOps, fitting into existing platform engineering workflows.
The open-source edition supports GitHub and Google for identity; the Enterprise Edition adds Okta and Microsoft Entra. Obot is backed by $35 million in seed funding.
The tradeoff is that you own the full operational lifecycle. For platform teams, that’s usually a feature — it’s infrastructure you operate, not a service you depend on.
IBM ContextForge
Best for Platform Teams Managing Multi-Cluster, Multi-Protocol Environments
Platform teams at large organizations often manage infrastructure across multiple Kubernetes clusters, cloud regions, and business units — each with its own tools, governance requirements, and risk tolerance. ContextForge is designed for that level of complexity.
Multi-cluster federation via Redis enables independent ContextForge instances across organizational boundaries to share tool discovery while maintaining separate governance. The gateway doesn’t just handle MCP — it federates MCP servers, A2A agents, and REST/gRPC APIs into a single endpoint, which matters for platform teams managing heterogeneous AI architectures.
RBAC/ABAC with multi-tenancy supports private, team, and global catalogs — enabling the platform team to offer different tool sets to different business units with appropriate access controls. Forty-plus plugins extend the platform for additional transports, protocols, and integrations. OpenTelemetry observability connects to Phoenix, Jaeger, Zipkin, and other OTLP backends.
ContextForge deploys via PyPI, Docker, or Helm charts and supports multi-architecture containers (amd64, arm64, s390x). SSO integration covers GitHub, Google, Microsoft Entra, Okta, Keycloak, IBM Security Verify, and generic OIDC.
The tradeoffs: beta-stage software (1.0.0-BETA) with no commercial support. Platform teams comfortable running complex open-source infrastructure will find ContextForge’s federation capabilities unmatched. Teams that need production SLAs should weigh the operational risk.
Bifrost by Maxim AI
Best for Platform Teams Prioritizing Performance and Unified AI Infrastructure
Bifrost operates as both an LLM gateway and an MCP gateway in a single binary, which simplifies what the platform team needs to deploy, scale, and monitor. One infrastructure component handles both model routing and tool governance.
For platform teams managing internal consumers with different needs, Bifrost’s virtual key system provides the abstraction layer: each consuming team or service gets a virtual key with its own budget, rate limits, and tool-level access controls. Tool filtering ensures consumers only see the tools they’re authorized to use, and Code Mode — originally pioneered by Cloudflare — reduces token consumption by 50% or more for multi-server workflows.
Bifrost adds 11 microseconds of overhead at sustained 5,000 RPS, deploys via NPX, Docker, or Helm, and provides built-in observability through Prometheus metrics and OpenTelemetry. The open-source core is Apache 2.0; enterprise features (clustering, vault integration, federated auth, guardrails) require a commercial agreement.
The tradeoff: Bifrost is a gateway, not a platform. It doesn’t include a curated server registry, a self-service catalog, or the kind of internal developer platform features that Obot provides. Platform teams that need more than routing and governance will need to build those layers themselves.
Kong AI Gateway
Best for Platform Teams Already Operating Kong
If the platform team already manages Kong for API gateway infrastructure, extending Kong to handle MCP traffic is the consolidation play. MCP governance sits alongside API governance in the same platform, using the same operational patterns, monitoring pipelines, and security policies.
Kong’s MCP capabilities include the MCP Proxy plugin for protocol bridging, OAuth 2.1 via a dedicated MCP OAuth2 plugin, MCP-specific Prometheus metrics, and the MCP Registry in Kong Konnect for centralized tool discovery. Kong can also auto-generate MCP servers from existing Kong-managed REST APIs — letting the platform team expose internal services to agents without building custom MCP server implementations.
The full Kong plugin ecosystem (OIDC, mTLS, rate limiting, OpenTelemetry) applies to MCP traffic, and for platform teams already managing those configurations, the learning curve is minimal.
The tradeoff: Kong’s enterprise pricing can exceed $50,000 annually, and the platform’s scope extends well beyond MCP. Platform teams not already running Kong are adopting a significant infrastructure commitment for the MCP use case alone.
Choosing the Right MCP Gateway for Your Platform Engineering Team
Ship governance to other teams quickly: MCP Manager. Purpose-built registry, RBAC, and self-service provisioning without building it internally. You can learn more about MCP Manager and book a free trial.
Build a self-service MCP platform on Kubernetes: Obot. Registry, catalog, hosting, and GitOps workflows — the full internal platform stack in open source.
Multi-cluster, multi-protocol federation: IBM ContextForge. Federated governance across business units, regions, and protocols.
Unified LLM + MCP gateway with performance focus: Bifrost. One binary, virtual keys per consumer, sub-microsecond overhead.
Extending existing Kong infrastructure: Kong AI Gateway. Same platform, same policies, same team.
Platform teams that get MCP right give the rest of the organization a safe, governed path to AI adoption. The gateway is the foundation of that path.
