Build vs Buy: The Hidden Costs of DIY MCP Servers

Building a Model Context Protocol (MCP) server can be deceptively simple for proof-of-concept demonstrations, but the transition to a production environment reveals significant hidden costs related to security, governance, observability, and maintenance. As companies increasingly adopt MCP, which standardizes connections for AI agents to discover and invoke tools, the challenges of managing authentication complexities, ensuring governance to avoid server sprawl, and maintaining observability for debugging become apparent. The DIY approach often leads to technical debt, with high security risks and governance without a control plane, resulting in server sprawl and shadow AI issues, which can incur substantial costs. Additionally, maintaining a DIY setup requires constant updates to align with evolving MCP standards. These issues suggest that while building an MCP server might seem appealing initially, leveraging enterprise-grade infrastructure, like those offered by Kong, could provide a more robust solution by offering seamless API integration, centralized governance, and comprehensive observability, allowing teams to focus on innovative AI features rather than infrastructure maintenance.