Stow.Best MCP Gateways in 2026: An Honest Comparison
ProductApril 19, 20266 min read

Best MCP Gateways in 2026: An Honest Comparison

The MCP gateway category didn't exist two years ago. Now there are a dozen options and the differences matter — especially if your AI agent is touching production data.

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MCP (Model Context Protocol) shipped in late 2024 and the tooling ecosystem has been catching up ever since. In 2026, there are real options for connecting AI agents to external services — with different trade-offs on security, coverage, and control. Here's what to evaluate and how the leading options compare.

What to Look For in an MCP Gateway

Before comparing products, it helps to know what the category is actually solving. A raw MCP server gives your AI agent a connection to a service — but nothing else. No permissions, no logging, no approval flows. A gateway adds the infrastructure between the agent and the service.

Permission granularity

Can you configure which specific operations are allowed? Or is it all-or-nothing at the service level?

Audit logging

Is every tool call logged? Can you see what the agent did, when, and with what parameters?

Approval flows

Can you require human sign-off before high-stakes actions execute? Or does everything run automatically?

Auth mechanism

Does the agent hold the actual token, or does the gateway handle credentials server-side?

Retention policy

What happens to payload content — email bodies, document text — that passes through the gateway?

Agent support

Which AI agents are supported? Claude Desktop (OAuth), Cursor (SSE), ChatGPT (plugins), custom?

The Comparison

Stow

Security-first MCP gateway. Granular permissions, approval queue, zero-retention, full audit log.

This product

Strengths

  • +Operation-level permissions per agent
  • +Approval queue for high-stakes actions
  • +Zero-Retention: payload content never stored
  • +Full activity audit log
  • +Security Baseline for session scoping
  • +Cursor, Claude Desktop, ChatGPT support

Trade-offs

  • Fewer integrations than Composio (25+ vs 250+)
  • More setup than a raw MCP server
Best for: Teams where production data, compliance, or human oversight matter

Composio

Broad integration coverage with developer-first API. 250+ connectors.

Strengths

  • +250+ service integrations
  • +Fast to set up
  • +Good SDK coverage
  • +Solid agent support

Trade-offs

  • Limited operation-level permission control
  • No approval queue
  • No zero-retention policy
  • Basic audit logging
Best for: AI product builders who need breadth and fast shipping

Raw MCP Servers

Self-hosted, open-source MCP servers for individual services.

Strengths

  • +Free and open source
  • +Full control over implementation
  • +No third-party dependency
  • +Large community (GitHub, filesystem, etc.)

Trade-offs

  • No permissions beyond what you build yourself
  • No audit logging
  • No approval flows
  • Agent holds credentials directly
  • Each service requires separate setup
Best for: Personal use, local experiments, and development environments

TrueFoundry / Portkey

LLM gateway with some tool-call routing capabilities.

Strengths

  • +Strong LLM gateway features (model routing, fallbacks)
  • +Cost tracking across models
  • +Enterprise-ready infrastructure

Trade-offs

  • Not purpose-built for MCP tool calls
  • Limited permission granularity for external services
  • Best suited for LLM traffic, not service connections
Best for: Teams whose primary concern is LLM cost/reliability, not service permissions

The Honest Recommendation

If you're running personal experiments or building a dev tool that connects one AI agent to one non-sensitive service, raw MCP servers are completely appropriate. They're free, well-documented, and get out of your way.

The moment your AI agent touches production data — a shared Slack workspace, a GitHub repo with real code, a Gmail inbox with real customer emails, a database with real user data — you need a gateway. The question is which one.

Composio wins on breadth. If you need 100 integrations and permissions aren't a concern, it's the pragmatic choice. Stow wins on control. If every action needs to be auditable, if some actions need human approval, if your credentials can't pass through AI context windows, if payload content can't be stored — Stow is the only gateway built for that set of requirements.

What to Ask Any Gateway Before Committing

1.

Does payload content get stored on your servers? What's your data retention policy?

2.

Can I configure which specific operations each agent is allowed to perform?

3.

Is there a way to require human approval before a specific action executes?

4.

How are credentials stored — and does the agent ever have direct access to the token?

5.

What does the audit log capture — just metadata, or operation parameters too?

6.

What happens to my data if I close my account?

The Gateway Built for Control.

Granular permissions. Approval queue. Zero-Retention. Full audit log. One gateway for every AI agent you run.

S

Stow Security Team

April 19, 2026