Custom MCP Servers vs Managed Solutions: When to Build vs Buy
The build vs buy decision for AI integration
According to Menlo Ventures' 2025 State of Generative AI report, 76% of enterprise AI use cases are now purchased rather than built internally—up from 53% in 2024. This article helps you decide whether to build a custom MCP server or use a managed solution.
Key Statistics (Primary Sources)
76% of enterprise AI use cases are purchased (not built internally) in 2025 (Menlo Ventures)
53% were purchased in 2024—showing a significant shift toward buying (Menlo Ventures)
$37 billion spent on enterprise AI in 2025, up from $11.5B in 2024 (3.2x growth) (Menlo Ventures)
47% of AI deals reach production, vs 25% for traditional SaaS (Menlo Ventures)
Survey of ~500 U.S. enterprise decision-makers, November 7-25, 2025
What Is a Custom MCP Server?
A custom MCP server is an implementation of the Model Context Protocol that you build and maintain yourself. It connects your specific business systems to AI assistants like ChatGPT and Claude.
MCP is an open standard, so anyone can build an MCP server using the official SDKs (Python, TypeScript, Java, Kotlin, C#, Swift, Go). The server exposes tools, resources, and prompts that AI assistants can use.
A managed solution handles MCP server creation, hosting, and maintenance for you. Instead of building from scratch, you configure the integration through a platform interface.
Examples include:
AWS MCP Server: Managed remote MCP server for Amazon Bedrock (preview since November 2025) (source)
Noodle Seed: Creates and manages MCP servers for businesses connecting to ChatGPT and Claude
ChatGPT Connectors: Pre-built connectors for common services (Google Drive, Slack, GitHub)
When to Build Custom
Consider building a custom MCP server when:
Unique Business Logic: Your integration requires complex, proprietary business rules that can't be configured in a managed platform
Specialized Data Sources: You need to connect to internal systems with custom APIs that managed solutions don't support
Full Control Required: Security, compliance, or regulatory requirements mandate that you control the entire stack
Development Capacity: You have an engineering team with the capacity to build and maintain the integration
Building Custom Requires:
Developers familiar with MCP protocol and chosen SDK
Infrastructure for hosting the MCP server
Ongoing maintenance as MCP specification evolves
Security hardening and monitoring
Testing across different AI clients (ChatGPT, Claude, etc.)
When to Use Managed Solutions
Consider a managed solution when:
Faster Time-to-Market: You want to launch an AI integration quickly without building infrastructure
Standard Use Cases: Your needs align with common patterns (e-commerce, CRM, knowledge base)
Limited Engineering Resources: Your team should focus on core product, not AI infrastructure
Reduced Maintenance: You want the platform to handle updates as MCP evolves
Why 76% are buying:
The Menlo Ventures data shows enterprises increasingly prefer buying AI solutions. The 47% production rate for AI (vs 25% for traditional SaaS) suggests that bought solutions actually ship faster than built ones.
Build vs Buy: Decision Framework
Factor
Build Custom
Use Managed
Time to Launch
Weeks to months
Days to weeks
Engineering Required
Dedicated team
Configuration only
Customization
Unlimited
Within platform limits
Ongoing Maintenance
Your responsibility
Platform handles
Protocol Updates
You implement
Platform updates
Enterprise Share
24% of use cases
76% of use cases
Enterprise share based on Menlo Ventures 2025 survey data
Hybrid Approach
Many organizations use a hybrid approach: managed solutions for standard integrations, custom builds for unique requirements.
For example, you might use Noodle Seed's managed Shopify integration for e-commerce data, while building a custom MCP server for your proprietary analytics system.
This approach captures the speed benefits of managed solutions (the 76%) while retaining flexibility for specialized needs.
Noodle Seed's Approach
Noodle Seed offers managed MCP server creation for businesses. We handle the technical implementation while you configure the integration:
Pre-built integrations: Shopify, knowledge bases, and common data sources
Custom MCP development: For unique business requirements
Multi-platform deployment: Your data available in ChatGPT, Claude, and other AI assistants
Managed hosting: We handle infrastructure and protocol updates
Frequently Asked Questions
How hard is it to build a custom MCP server?
MCP provides official SDKs in Python, TypeScript, Java, Kotlin, C#, Swift, and Go. A simple MCP server can be built in hours, but production-ready servers with proper error handling, security, and monitoring take longer.
Can I switch from custom to managed (or vice versa)?
Yes. MCP is a standard protocol, so the AI clients don't care whether your server is custom-built or managed. You can migrate between approaches as your needs evolve.
What's the production success rate for AI solutions?
According to Menlo Ventures, 47% of AI deals reach production, compared to 25% for traditional SaaS. This suggests AI solutions (whether built or bought) have higher deployment success rates.
Does Noodle Seed build custom MCP servers?
Yes. In addition to our managed platform, Noodle Seed offers custom MCP server development for businesses with unique requirements. Contact us to discuss your needs.
Get Started with Managed MCP
Let Noodle Seed handle your MCP server creation and maintenance. Connect your business data to ChatGPT, Claude, and other AI platforms.