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

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

As AI agents shift from novelty to utility, a common infrastructure question arises for product and engineering teams:

Should we build directly on top of the Model Context Protocol (MCP), or use a gateway vendor to handle integrations and authentication?

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

Approaches to Operationalizing MCP

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 take this route, you’ll need to handle:

• Authentication and token management for every integration

• Routing logic for incoming commands

• Tenant isolation and context detection

• Retries, error handling, and permissions

• Scalable infrastructure for production environments

Although this approach offers full control and customization. But it is complex and resource intensive, especially when dealing with multitenant SaaS or enterprise-grade applications.

2. Use a Gateway Vendor

Gateway vendors are ideal for early-stage prototyping or validating integration concepts.

Like Composio, Smithery, and Pipedream offer platforms with prebuilt connectors and simplified OAuth flows to help you quickly integrate third-party APIs.

But they’re not designed for production-scale, AI-native use cases.

• They focus on authentication, not full action execution.

• They lack multi-tenant context detection, making user-level routing complex.

• They impose opinionated workflows, limiting control over how actions are processed.

• They’re hard to extend or deeply customize for advanced needs.

• They introduce vendor lock-in, which can slow you down as your product evolves.

You’ll still be writing glue code and workarounds to reach production standards.

3. The Fastn UCL Advantage

Fastn UCL (Unified Command Layer) is a managed platform that turns MCP into a secure, scalable system ready for production. It does everything a gateway can, and much more:

• Full compatibility with MCP commands

• Secure, tenant-aware execution with automatic context detection

• Deep integration with tools like Slack, Jira, Notion, Gmail, Microsoft Teams, and others

• Built-in retries, logging, monitoring, and error handling

• Tenant-based permissions and workspace-level access control

• Real-time observability and tenant-specific dashboards

Rather than being a connector library or an auth wrapper, Fastn UCL is the infrastructure layer you would otherwise have to build from scratch to make MCP work in real environments.

Real-World Scenario

Imagine you’re building an AI-powered reminder feature inside your product.

Different customers want reminders delivered through different channels:

• Customer A prefers Slack

• Customer B uses Microsoft Teams

• Customer C wants email via Gmail

Let’s look at what this requires in each scenario.

Why This Matters?

AI agents only create value when they can take secure, contextual, real-world actions. This requires:

• Multitenancy with strong tenant isolation

• Fine-grained permissions across users and workspaces

• Unified monitoring and observability

• Robust handling of failures and retries

• Scalable infrastructure for real-time execution

  

MCP gives you the standard. Fastn UCL gives you the engine that runs it safely, reliably, and at scale.

No extra logic. No routing code. No duplication across tenants.

Moving Forward

If you're a SaaS or enterprise team building AI-powered products, you don't have to choose between DIY infrastructure and limited gateway platforms.

Fastn UCL is the third path. It makes MCP production ready, giving you the flexibility to build powerful agent features, without getting bogged down in infrastructure.

Focus on your product. Let Fastn UCL handle the hard parts.

Learn more at ucl.dev, or explore the documentation to get started.