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Gram

In Depth: Gram vs FastMCP Cloud

Nolan Sullivan

September 27, 2025 - 9 min read

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Since Anthropic launched the Model Context Protocol in November 2024, over 16,000 MCP servers have been created. But there’s a problem: most teams struggle to move from quick prototypes to production deployments. We built Gram  to help close this gap.

This post compares Gram with FastMCP Cloud , another platform for deploying hosted MCP servers.

In short: How are Gram & FastMCP Cloud different?

Like most MCP frameworks, FastMCP Cloud just focuses on abstracting the details of the server mechanics. But when it comes to MCP servers, the real challenge is tool design. How do you structure and organize capabilities so LLMs can use them effectively. That is what Gram focuses on (in addition to abstracting the details of the server mechanics).

We’ll dive into these in more detail below. But here is an overview of the two platforms’ capabilities:

Framework vs Platform

FastMCP Cloud is built on the FastMCP framework , a Python-based framework for building MCP applications. Like any good framework, it provides the building blocks and flexibility to create exactly what you need. You have unlimited control over your implementation—you can build sophisticated authentication flows, complex tool logic, and integrate with any system you want. This flexibility is powerful when you need to build something highly customized or when your requirements don’t fit standard patterns.

Gram takes a platform approach that trades some of that unlimited flexibility for significantly improved quality and ease of implementation. Rather than starting with raw building blocks, Gram provides opinionated, production-ready patterns:

• Guided tool creation that automatically generates well-structured tools from your APIs
• Built-in best practices for tool curation that prevents common LLM performance issues
• Zero-configuration deployment that gets you from idea to production-ready MCP server in minutes
• Quality guardrails that ensure your tools follow proven patterns for AI agent success

The platform approach means you get a faster time-to-market with tools that are more likely to work well with LLMs out of the box. You trade some flexibility for the assurance that what you build will be high-quality and performant. For most teams, this trade-off accelerates their AI initiatives rather than limiting them.

Python specific vs Language agnostic

FastMCP Cloud is Python-specific, requiring you to write your MCP server code in Python using the FastMCP framework. This gives you the full power of Python’s ecosystem and complete control over your server logic. However, this can limit enterprises with diverse internal ecosystems where different teams use different programming languages and may not have Python expertise readily available.

With FastMCP Cloud:

You need to write Python code to configure your MCP server.

Gram takes a language-agnostic approach. For API-based tools, you work directly with your existing APIs through OpenAPI specifications without needing to write code in any specific language.

For custom logic, Gram Functions supports both Python and TypeScript (with more languages in the future), allowing you to write serverless functions that run securely in the cloud. This means you can choose the language that best fits your team’s expertise while still leveraging Gram’s platform capabilities.

With Gram:

1. Upload your OpenAPI spec to the Gram dashboard.
2. Create a toolset and add the tools you want to expose.
3. Your MCP server is ready to use at https://app.getgram.ai/mcp/your-server-slug with the tools you selected.

API proxy support

Gram provides true API proxy capabilities that automatically keep your MCP tools synchronized with your existing APIs. When you upload an OpenAPI specification, Gram generates production-ready tools that proxy requests to your API endpoints. As your API evolves, simply upload an updated OpenAPI spec and your MCP tools automatically stay in sync—no code changes required.

FastMCP Cloud offers OpenAPI integration as a development starting point. While FastMCP can generate MCP server code from your OpenAPI specification, this is primarily a bootstrapping approach. The generated code requires significant customization—you’ll need to configure route mappings, handle authentication, set up parameter filtering, and manage the ongoing synchronization between your API and MCP server manually.

Tool curation vs. customization

This framework vs platform distinction becomes most apparent in how each approach handles tool management and AI performance optimization.

As you build on Gram’s platform, you will be guided through the core challenge of building performant AI tools through systematic curation. Here are some key steps:

1. Cut down on tools: When you upload your API to Gram, it converts your endpoints into simple tools, and then helps you curate which capabilities actually matter for your use cases. A 600-endpoint API becomes a focused 5-30 tool collection that AI agents can navigate confidently. Tools are organized into toolsets that can remix capabilities across different APIs for specific use cases.

2. Add Business Context: APIs designed for developers often lack the rich context AI agents need. Gram helps you enhance tool descriptions, add business logic, and provide examples that help LLMs understand not just what each tool does, but when and why to use it.

3. Define Custom Tools: This is where Gram truly differentiates itself. Instead of forcing AI agents to orchestrate multiple tool calls manually, you can define “custom tools” that chain together smaller atomic tools into complete business operations. For example, rather than an AI figuring out five separate steps to “summarize ACME Corp’s health,” you create one targeted tool that handles the entire workflow and provides consistent results.

FastMCP Cloud’s framework approach focuses on the mechanics of MCP server creation: decorators, custom functions, and infrastructure. While these are important foundational elements, you implement your own curation logic in Python code, giving you unlimited flexibility to build sophisticated tool architectures. You can create complex routing logic, implement custom filtering systems, and build exactly the tool interactions your specific use case demands. However, you’re responsible for solving the critical factor for agent success—tool design—yourself through code. LLMs don’t interact with your Python code; they interact with your tool definitions, descriptions, and prompts, which you must craft and optimize manually.

Authentication and Enterprise Security

Gram provides two flexible authentication methods to fit different organizational needs. With Managed Authentication, Gram handles OAuth flows and provides users with API keys, taking care of the complex authentication work behind the scenes. With Passthrough Authentication, users bring their own API credentials, which Gram securely passes through to your API without storing or managing them directly.

Gram also supports full OAuth 2.1 with Dynamic Client Registration (DCR) and offers an OAuth proxy for providers that don’t support DCR.

FastMCP Cloud includes OAuth options and lets you bring your own identity provider; however, you still need to wire authentication into your MCP server code and manage credentials yourself. This can get complicated .

Company backgrounds

Speakeasy has been building API tools since 2022, including SDK generation across multiple languages. We released Gram publicly in September 2025.

Prefect is the company behind FastMCP Cloud. They focus on ETL and Data Orchestration and became known in the MCP world for releasing FastMCP, which became part of the official Python implementation for MCP in 2024. They released FastMCP Cloud in August 2025 as a managed hosting solution for FastMCP servers.

Real-World Comparison: Building AI Tools for E-commerce

Let’s walk through a hypothetical example that shows how each platform works in practice. Imagine you’re building AI-powered tools for Acme Corp, an e-commerce company with an existing REST API.

Acme Corp has a typical e-commerce API with endpoints for products, orders, customers, and inventory. They want to give AI agents the ability to help with customer service, order management, and inventory queries.

Starting Point: Your Existing API

You have an e-commerce API with 50+ endpoints for products, orders, customers, inventory, returns, and analytics. AI agents only need about 8-10 of them for customer service.

The Gram Way: Transform Your Existing API

With Gram, you upload your OpenAPI spec and the platform generates MCP tools automatically:

The platform lets you pick the 8 endpoints that matter for customer service, automatically handles authentication, and creates an MCP server. When your API changes, you just upload a new OpenAPI spec, and the MCP tools stay in sync.

Timeline: Most teams can get a production-ready MCP server running in about 20 minutes, including time to review and curate which tools to expose.

The FastMCP Cloud Way: Build Custom MCP Tools

With FastMCP Cloud, Acme Corp’s dev team will need to write some Python code to create MCP tools that call their API:

Then you deploy this code to FastMCP Cloud, which handles the hosting and scaling:

You get complete control over the tools, but you’re responsible for keeping them in sync with your API manually.

Timeline: Development teams typically need 1-2 weeks for initial implementation, plus ongoing maintenance time whenever the underlying API changes. This includes writing the MCP server code, configuring authentication, testing the integration, and setting up deployment pipelines.

What this means in practice

Gram’s platform approach works well when you have existing APIs and want to get MCP tools running quickly. The guided experience eliminates much of the development work, letting your team focus on tool curation rather than MCP protocol details. This approach excels when you want to expose existing API functionality to AI agents with minimal development overhead.

FastMCP Cloud’s framework approach shines when you need to build custom logic that goes beyond what your existing APIs provide. You can combine multiple API calls into sophisticated tools, implement complex business rules that span different systems, or integrate with proprietary data sources that don’t have REST APIs. The code-first approach gives you complete control when your MCP tools need to do much more than act as a simple API interface.

Both approaches are valid—the choice depends on your team’s preferences, existing infrastructure, and whether you prioritize speed-to-market (platform) or unlimited customization possibilities (framework).

Conclusion

Gram and FastMCP Cloud solve the same problem: getting MCP servers into production, but with fundamentally different approaches that cater to different needs.

Gram’s platform approach works well for teams with existing APIs who want MCP servers with minimal development overhead. The guided experience and built-in tool curation help you get from API specification to production-ready MCP server quickly.

FastMCP Cloud’s framework approach excels when you need unlimited flexibility to build custom MCP applications. The code-first approach gives you access to all the raw primitives, allowing you to implement exactly the logic and integrations your use case requires.

Neither approach is inherently better—they serve different needs and preferences. The choice depends on whether you prioritize speed and guidance (platform) or flexibility and control (framework).

Try Gram for free today at getgram.ai .