4 posts tagged with "architecture"

AI assistants are most useful when they can access internal data and tools via MCP. MCP gateways help to make that process easier by managing connections and authentication for your organization. This article compares LiteLLM's MCP offering as part of their LLM proxy to the MintMCP - a gateway built specifically for enterprises using MCP internally.

Key Takeaways​

• Fundamental difference: LiteLLM is an LLM proxy with MCP added, MintMCP is purpose-built for MCP
• Custom MCP servers: With LiteLLM you deploy and manage them yourself, MintMCP runs them in their cloud
• Authentication: LiteLLM uses pass-through auth (you manage credentials), MintMCP provides managed OAuth
• Best fit: LiteLLM for developer teams building products, MintMCP for internal enterprise deployments

Deploying MCP servers in production requires choosing the right approach for your use case. This guide covers the four main deployment patterns and helps you select between Cloudflare Workers, Vercel Functions, and MCP gateways based on your specific requirements.

AI agents need access to tools, data sources, and APIs. The Model Context Protocol (MCP) standardizes these interactions, but managing multiple MCP servers introduces complexity. MCP gateways address the technical problems that emerge in production deployments.

When you first discover the Model Context Protocol (MCP), it can feel a bit like magic: suddenly your AI assistant can read from a database, update a CRM record, or spin up cloud resources - all through a single, standard interface. But as soon as you try to move beyond a demo, you'll run into practical questions: How do you secure these tool calls? Who keeps track of rate limits and audit logs? Where do you plug in observability? That's where an MCP gateway comes in. Think of it as the operations and security layer that makes MCP usable in production - similar to how an API gateway fronts traditional REST or gRPC services.