Best Enterprise MCP Gateways (2026)
TL;DR
An enterprise AI gateway sits between your company's AI tools and the apps and data they connect to. It gives each employee access to the right apps through MCP, tracks cost and usage, and lets teams share workflows packaged as Skills.
When choosing an AI gateway for your company, the choice comes down to Arcade.dev, Runlayer, Barndoor, MintMCP, and Metorial. The first four handle access and cost well, then stop. Only Metorial does the third job, letting non-technical teammates share Skills and the MCP access behind them. That matters if your rollout is stuck on:
- creating GitHub accounts for non-engineers just to publish a Skill,
- shared workflows breaking because each teammate is connected to a different account, or
- Skills staying private because updating them means editing files in a code repository.
Best Enterprise MCP Gateways in 2026
Most companies run into this at around 50 employees. Engineering adopts Claude, Cursor, or Codex, and then someone tries to connect those tools to the apps everyone actually works in: Slack, Gmail, GitHub, Linear.
That connection is now standardized. MCP is a protocol that lets an AI tool plug into an app the same way every time, so the app only has to expose its data once instead of building a custom integration per AI client. Every major AI client either ships its own connectors or lets you point it at an existing MCP server.
Without those connections, a capable model is only a chat window. With them, it can do the work end to end: pull your meeting notes out of Granola, figure out what was agreed, and draft the follow-up to every participant.
Rolling this out across a few hundred employees runs into four problems.
Built-in connectors tie you to one AI client. If you set everything up through Claude's own connectors, that configuration doesn't come with you when a cheaper or better model ships somewhere else. You rebuild it.
People on the same team use different clients. One engineer prefers Cursor, another prefers Claude, and support lives in a browser. Same tools, three separate setups, and no way to give the team one shared configuration.
Credentials spread out. Someone pastes a Salesforce API key into a config file on their laptop to get an agent working. It succeeds, so a teammate copies it. Six months later nobody knows who created the key, when it expires, or how much it can reach.
You can't tell who an agent acted as. When an agent deletes the wrong record or emails the wrong customer, the first question is whose access it used. If the answer is a shared key on someone's laptop, there is no answer. Security needs a record before that happens, not after.
Each of these has a partial fix you can build yourself. Together they are the reason "enterprise AI gateway" turned into a budget line this year, and why five well funded companies are now selling one. The rest of this post compares them on the dimensions that change what your rollout actually looks like, and says plainly which one fits which problem.
What should you actually compare when picking a gateway?
Vendor homepages all claim the same things. These five dimensions are where they actually differ:
- Setup time. Can your team have it running this week, or does the vendor need to put their own engineers on it? A rollout that depends on their staff is a rollout that slips a quarter.
- Who can use it. Engineers only, or can someone in ops or support build a workflow themselves? This is the difference between AI reaching twenty people and reaching two hundred.
- Where it runs. Hosted by the vendor, or inside your own infrastructure? If you have data residency rules or a regulated industry, the second option isn't optional.
- Access control and records. Can you grant tools by role through the login system you already use, so a new salesperson gets the right access on day one and loses it on their last? And can you see afterward which employee's access an agent used?
- Cost model. Flat price per seat, priced by usage, or a layer that cuts what you spend on the model itself? The third only matters once your AI bill is big enough to notice.
Most vendors here are strong on one or two. None is strong on all five, which is what makes the comparison below more useful than any homepage.
The 2026 comparison
Sources for funding and compliance claims are listed at the end of this post.
Arcade.dev: best for agent authorization
Arcade.dev builds what it calls a secure action layer. The idea: an agent should only ever have the access of the person it's acting for, narrowed to the specific action it's taking. An agent looking up a deal reads that one Salesforce record and nothing more, and the attempt is logged either way. For companies whose main requirement is being able to prove an agent couldn't exceed its user's permissions, this is one of the strongest implementations available.
Its total backing is $72M, a $12M seed plus a $60M Series A led by SYN Ventures, with Morgan Stanley and Wipro participating.
Where it falls short. Arcade doesn't compete on cost control and won't find MCP servers your teams have set up without approval. Its defense against prompt injection is structural, limiting what an agent can reach, rather than actively inspecting requests for attacks. And there's nothing here for non-engineers writing workflows, because it isn't built for that.
Runlayer: best for externally verified security
Runlayer sits between your AI clients and your MCP servers and inspects every tool call passing through, producing logs that can be shown not to have been altered afterward. Its credibility comes from outside validation rather than its own claims: it meets AARM Extended Conformance R1 through R9, a third-party specification for agent security, and David Soria Parra, the lead creator of MCP, advises the company.
It has raised $41M, a $11M seed and a $30M Series A, both backed by Felicis and Khosla Ventures.
Where it falls short. If your security lead wants the most independently audited option on the market, Runlayer has earned that. It isn't built to get the rest of the company using AI, and it doesn't try to be.
Barndoor: best for cutting AI cost
Barndoor is the clearest cost-focused option here. Its ToolIQ router addresses a specific waste problem: a model connected to two hundred tools has to read all two hundred descriptions on every single request, and you pay for those tokens each time. ToolIQ sends only the tools a request actually needs. Barndoor reports this cuts token use and processing cost by up to 95% per query.
It raised a $13.6M Series Seed from Crosslink Capital.
Where it falls short. No shared workflow layer and nothing aimed at non-engineering teams. If finance is driving the conversation, this is the one to look at; if enablement is, it isn't.
MintMCP: best for the fastest setup
MintMCP is the newest and smallest of the five, and the quickest to get running. Each role gets its own endpoint URL carrying only the tools that role should reach, so bringing a department online is closer to one configuration step than a project. If someone needs to show a working, governed setup to a security reviewer next week, this is the shortest path there.
It's backed by Coatue, Hustle Fund, and Maven Ventures, with angels including Andrej Karpathy, Jeff Dean, and Scott Belsky.
Where it falls short. No shared Skills layer, no cost optimization, and less depth on security inspection than Runlayer or Arcade.
Metorial: the enablement-first AI gateway
Metorial does something none of the others attempt: it gets people outside engineering using AI directly, and treats access control as the baseline rather than the product.
Each employee logs in through the identity system you already use and gets the tools the company has approved: no GitHub account, no API key to copy by hand. Access appears when someone joins and disappears on their last day. Every integration sits behind one URL that works the same in Claude, Cursor, Codex, or Copilot, so switching clients doesn't mean setting access up again. And one person can write a workflow once and have it appear for everyone who should have it. A support lead can build refund triage in the morning and have the team using it that afternoon, without touching a code repository.
The product pieces are Metorial Portals for employee access, Magic MCP for one URL across AI clients, Agent Skills for shared workflows, Protoguard for prompt-injection checks, and Tracing for searchable session records across every agent interaction.
None of that trades away control. Every tool call is checked for prompt injection, every session is recorded and searchable, and the platform is SOC 2 Type II and GDPR compliant. It's open core under the FSL license and offers on-prem deployment.
Where it falls short. For a security-only rollout that stays inside engineering, Arcade.dev's authorization model is more specialized. If AI spend is your main concern, Barndoor's numbers are hard to match. If you need audit certification beyond SOC 2, Runlayer is further along.
Who should pick whom?
- Arcade.dev - your rollout stays inside engineering and you need to prove agents can't exceed their user's permissions.
- Runlayer - security or compliance is driving the decision and you want the most independently audited option.
- Barndoor - AI spend is your main constraint, or you need deployment with no internet connection.
- MintMCP - you need a compliant gateway running this month and don't yet need non-engineers contributing.
- Metorial - the blocker is getting AI past engineering: one URL across clients, access by role, and a shared Skills library your ops and support teams can build themselves.
FAQ
What's the difference between an AI gateway and an MCP server?
An MCP server exposes a specific tool or data source to an AI client. A gateway sits above many MCP servers (and other integrations) and handles the cross-cutting concerns (auth, access control, logging) so you're not reimplementing them for every tool and every team.
Do I need an enterprise AI gateway if only engineering uses AI tools today?
Not urgently, but the moment a second department wants access, ad hoc credential sharing becomes the actual bottleneck. Most companies in this comparison's ICP (50 to 1000 employees) hit that point faster than they expect.
Which AI gateway is best for non-technical teams?
Of the five compared here, Metorial is the only one built around non-engineers contributing directly: creating and editing skills without a GitHub account or a PR workflow.
Can I self-host an AI gateway instead of using a hosted SaaS?
Yes, though the depth varies by vendor. If deployment model is a hard requirement, compare hosted, VPC, on-prem, and air-gapped options before picking a gateway.
How much do these gateways cost?
Pricing models vary widely and most (MintMCP, Barndoor, Runlayer) require a custom quote. Compare on total cost of rollout, including the engineering time needed to configure and maintain each option, not just the sticker price.
Sources
- Arcade Raises $60M Series A: Businesswire
- Runlayer Raises $30M Series A: SecurityWeek
- MCP Security Startup Runlayer Raises $11M as Protocol Creator Joins Advisory Board: byteiota
- Barndoor AI Raises $13.6M in Series Seed: PR Newswire
- Introducing ToolIQ: Barndoor AI
- Metorial GitHub repository (stars, license): github.com/metorial/metorial
- Metorial: metorial.com