The Best MCP Gateways for Engineering & IT Teams in 2026

Model Context Protocol (MCP) has moved fast. What started as a developer experiment is now being wired into production systems at companies across financial services, healthcare, and enterprise tech. AI agents can now connect to CRMs, ticketing systems, and internal APIs, accessing sensitive data in the process. And engineering and IT teams are scrambling to keep up.

That’s created a new category of tooling: the MCP gateway. But not all gateways are built for the same job. Some are built for individual developers running servers locally. Some are API gateways that added MCP support as an afterthought. And some are purpose-built for the governance and security challenges that come with deploying MCP at enterprise scale.

This article breaks down the most common MCP gateway options engineering and IT teams will encounter, showing you what they’re built for and where the fall short.

What is an MCP Gateway?

MCP gateways act as a central layer to control, secure, and govern the data flowing between AI systems or agents and MCP servers.

Without an MCP gateway, there is a messy web of connections. This makes it challenging (and nerve wracking) for IT and engineering teams to ensure that data stays secure. There are many types of MCP security vulnerabilities and attacks (e.g., tool poisoning, prompt injection, rug pull attacks). MCP gatways help prevent them.

What to Look for in an MCP Gateway

Before comparing options, it helps to be clear about what enterprise engineering and IT teams actually need from an MCP gateway. The list tends to look something like this:

• Centralized visibility: a single place to see which AI agents are accessing which systems, and when
• Access controls:role-based permissions that determine what tools different users or agents can invoke
• PII and data detection: the ability to spot and block sensitive data (e.g., social security numbers) moving through the gateway
• Audit logs: a tamper-resistant record of all MCP activity for compliance and incident response
• Runtime guardrails: the ability to enforce policies on tool calls in real time, not just at configuration time
• Enterprise integrations: SSO, SIEM compatibility, and support for existing security infrastructure

With that framework in mind, here’s how the main options stack up.

MCP Manager by Usercentrics

Best for: Mid-sized companies that need production-ready MCP governance and easy deployment

MCP Manager‘s gateway is built specifically for teams that need an intuitive interface and easy deployment. Where other gateways focus on connectivity or developer experience, MCP Manager focuses on the governance layer and ease of use, giving IT teams centralized visibility and control over every AI agent interacting with business systems. In addition, they offer workflows that make requesting MCP server approvals easier within an org.

Key capabilities include:

• Private MCP registry with one-click installation across clients like Claude and Cursor
• Workflows for requesting and approving MCP servers
• PII detection that identifies and flags sensitive data moving between agents and MCP servers
• Runtime guardrails that enforce policy in real time, not just at setup
• Dashboards, alerts, and audit logs built for compliance and incident response
• Enterprise-grade RBAC with tool-level provisioning
• Integrations with SSO, SIEM via OpenTelemetry, and AWS Bedrock

The core insight behind MCP Manager is that MCP at scale shouldn’t be hard. They specifically have features that make it easy for IT and engineering leaders to decide what teams and employees can access what servers in what ways. They also help prevent MCP rug pull attacks, data exfiltration, and other security vulnerabilities.

MCP Manager is offered by Usercentrics, a company with deep expertise in data governance and consent management, which gives it a unique foundation for the compliance-heavy use cases where MCP governance matters most, such as financial services, insurance, healthcare, and other regulated industries.

Docker MCP Gateway

Best for: Containerizing MCP servers

Docker’s MCP Gateway is open source and ships as part of Docker Desktop’s MCP Toolkit. It solves a real problem: local MCP servers must run directly on a developer’s machine means dealing with installation, dependencies, updates, and security risks.

Docker’s solution is elegant, allowing engineers to run MCP servers as isolated containers, managed by a local gateway. The gateway handles server lifecycle, credential injection, and routing. It includes basic logging and call-tracing, and servers run with restricted privileges and network access by default.

For a developer getting started with MCP locally, Docker’s gateway is a reasonable choice. It’s free, it’s containerized, and it integrates naturally into a Docker-native workflow.

Where it falls short for IT teams is significant, though. There’s no centralized dashboard, no RBAC, no PII detection, no compliance-grade audit logs, and no way for an IT team to govern what’s happening across an organization. The gateway runs on an individual developer’s machine — by design. It’s a local dev tool, not an enterprise control plane.

If someone on your engineering team asks “how do I run MCP servers more safely on my laptop,” Docker is a fine answer. If the question is “how does IT govern AI agent access to production systems across the organization,” Docker doesn’t have one.

Kong AI Gateway

Best for: Enterprises already deeply invested in Kong’s API infrastructure

Kong is a well-established name in API gateway infrastructure, and their AI Gateway product extends that foundation to support MCP. For organizations already running Kong to manage their API layer, the MCP support is a natural extension.

Kong’s strengths are its maturity, its enterprise feature set, and its breadth; it’s built to handle enormous scale and complex routing across a large organization’s API surface. If your engineers are already fluent in Kong and your security team is comfortable with it, adding MCP governance through Kong creates less net-new infrastructure to manage.

The limitations are also inherent to its infrastructure. Because Kong is fundamentally an API gateway that added MCP support (and not an MCP-native governance platform), the governance features available for MCP traffic are less purpose-built than what you’d get from a dedicated MCP governance tool. For teams not already in the Kong ecosystem, adopting it specifically for MCP is significant overhead. Kong is a powerful platform, but it’s also a large one to onboard.

For organizations evaluating MCP governance as a standalone need, Kong is likely overkill. For Kong shops looking to extend existing infrastructure, it’s worth evaluating how mature the MCP-specific features actually are.

Portkey

Best for: Teams primarily managing LLM routing and observability

Portkey started as an LLM gateway, a tool for routing requests across different model providers, managing fallbacks, and tracking LLM usage and cost. It’s good at that job, and it has a developer-friendly experience with pricing starting at $50/month.

MCP gateway functionality is a more recent addition to Portkey’s feature set, which is important context. The platform was designed around the LLM call as the unit of work (e.g., model, prompt, response). MCP introduces a different model, where agents are invoking tools, accessing external systems, and taking actions in the world. Those are meaningfully different governance challenges.

Portkey works well if your primary concern is observability and cost management across LLM providers, and MCP governance is a secondary consideration. If MCP governance is the primary need — especially in a regulated environment — you’re working against the grain of what Portkey was built to do.

TrueFoundry

Best for: Engineering teams building AI agents in code

TrueFoundry is a full AI and agent platform with MCP gateway functionality built in. It’s well-suited for engineering teams that are writing agents from scratch and want a unified platform for model access, deployment, and tooling.

The key caveat is that MCP support is still maturing in TrueFoundry. For teams that need robust, production-grade MCP governance today — not in a future roadmap — this is a meaningful limitation. TrueFoundry also skews toward engineering builders rather than IT governance teams, so the feature set reflects that audience.

Pricing ranges from a free trial to $499 or $2,999/month for higher tiers, with enterprise pricing available on request.

Bifrost by Maxim AI

Best for: Engineering teams prioritizing fast performance

Bifrost is a high-performance MCP gateway built in Go and it is designed for developer velocity. It’s fast. Like, really fast at sub-3ms-latency. It also offers built-in observability tooling, such as Prometheus metrics and OpenTelemetry distributed tracing. They offer decent observability tool, with cost tracking per tool call and a real-time dashboard.

For engineering teams where gateway latency is a real constraint (such as high-throughput agent workflows, latency-sensitive applications) Bifrost’s performance profile is genuinely differentiated.

The tradeoff is governance depth. There are other MCP gateways that focus more on security. It is also open source. Therefore, it is better for more technical teams. Other MCP gateways are better if you need to deploy MCP servers to non-technical teams.

Amazon Bedrock AgentCore Gateway

Best for: Teams already in the AWS ecosystem

Amazon Bedrock AgentCore Gateway is AWS’s managed MCP gateway, built to turn existing APIs, Lambda functions, and services into MCP-compatible tools with minimal custom code. It handles credential injection and authentication on both ends (inbound from agents and outbound to backend systems) and includes semantic tool discovery; this means that agents can find the right tool across large catalogs without bloating prompts.

For teams already running workloads on AWS, the integration story is compelling. IAM permissions, familiar AWS console workflows, and native support for Bedrock models mean less net-new infrastructure to manage. One-click connectors for tools like Salesforce, Slack, Jira, and Asana lower the setup bar further.

The limitation is the same one baked into any AWS-native solution: it’s designed to work within the AWS ecosystem, not across it. Teams with multi-cloud infrastructure or those who need purpose-built governance features like PII detection and compliance-grade audit trails will find AgentCore Gateway doesn’t cover that ground. It’s a strong choice if AWS is where you live. It’s a harder sell if it isn’t.

How to Choose

The right MCP gateway depends on what problem you’re actually trying to solve:

The pattern that emerges is fairly consistent: the further you move from individual developer tooling toward organizational governance, the more purpose-built your MCP gateway needs to be. Tools built for developers make developer tradeoffs. Tools built for governance make governance tradeoffs.

For IT and security teams trying to give engineering the access they need without accepting ungoverned risk, the question isn’t just which gateway supports MCP. Rather, it’s which gateway was built to solve your actual problem.

Moira West

Moira West

Moira West is a former journalist and educator who has spent the last nine years writing about technology, data management, and more. She is excited to now bring her expertise to Visor. When she's not working, she spends time with her family, paints, bakes, and writes more -- just for fun.