Should You Use a Gateway Vendor or Build Directly on MCP?

MCP is the protocol. Fastn UCL is what makes it production-ready.

As AI agents move from novelty to utility, many teams are asking a core infrastructure question: Should we build directly on top of the Model Context Protocol (MCP), or use a gateway vendor to handle integrations and auth?

This post breaks down the trade-offs and introduces a third path that balances flexibility, scalability, and speed: Fastn UCL.

The Options

1. Build Directly on MCP

MCP is an open protocol that standardizes how AI agents describe actions. It defines the structure, not the infrastructure.

If you go this route, you’ll need to handle:

• Authentication and token management for every integration

• Routing logic for commands

• Tenant isolation and context detection

• Retries, failures, and permissions

• Scalable infrastructure for production

This approach gives you control, but it’s complex and resource-intensive, especially for multi-tenant SaaS or enterprise apps.

2. Use a Gateway Vendor

Vendors like Composio, Smithery, and Pipedream offer prebuilt connectors and simplified OAuth flows. These are great for prototypes or early-stage tools.

But most gateway vendors are:

• Focused mainly on authentication

• Limited in tenant or context awareness

• Opinionated in how data and actions flow

• Harder to extend or deeply customize

You’ll still be stitching things together and risk vendor lock-in when your needs grow.

3. The Fastn UCL Advantage

Fastn UCL (Unified Command Layer) is a fully managed backend that turns MCP into a scalable, secure, production-ready system.

It’s not just a gateway, it’s the infrastructure layer that teams would have to build themselves to make MCP work reliably in real-world environments.

With Fastn UCL, you get:

• Full MCP command compatibility

• Secure multitenant execution with context detection

• Deep integration with tools like Slack, Jira, Notion, Gmail, and more

• Built-in retries, logging, and monitoring

• Role-based permissions and isolated workspaces

Real-World Scenario

Imagine you’re building a reminder feature powered by AI agents in your product. Different customers want reminders delivered through different tools:

• Customer A prefers Slack

• Customer B uses Microsoft Teams

• Customer C wants Gmail notifications

Here’s how this plays out depending on your approach:

If you use MCP directly: You need to build and maintain everything yourself, from OAuth flows for each app to custom routing logic for each tenant. You’re also responsible for managing context and securing user data per customer.

If you use a gateway vendor: You save time on authentication, but the heavy lifting remains. You still have to implement tenant-aware routing, manage permissions, and stitch together context handling, often using brittle workarounds.

If you use Fastn UCL: You send a single MCP-formatted command. Fastn automatically detects the tenant and user context, selects the right connector, handles authentication, and securely executes the action, whether it’s Slack, Teams, or Gmail. No custom logic. No manual routing. No duplication across tenants.

Why It Matters

AI agents are only useful when they can act securely and contextually. That means you need:

• Multitenancy with isolation

• Role- and workspace-aware permissions

• Centralized observability and control

MCP provides the language. Fastn UCL provides the infrastructure.

Final Take

If you're a SaaS or enterprise team building with AI, you don’t have to choose between DIY complexity and limited gateway vendors.

Fastn UCL is the third option. It makes MCP usable at scale, without the overhead.

Build agents that take real-world actions. Let us handle the hard stuff.