Tech Radar 27/07: Scaling MCP Servers in Production Kubernetes
Answer-first: Scaling MCP servers in Kubernetes requires decoupling the JSON-RPC state from persistent connections using websocket gateways, deploying stateless MCP worker replicas with HPA, and utilizing Redis for distributed context caching. This architecture prevents connection exhaustion when hundreds of AI agents query context simultaneously. The Model Context Protocol (MCP) has become the de facto standard for exposing enterprise data to AI agents. However, running a single local MCP server is vastly different from serving thousands of concurrent LLM requests in a distributed microservices environment. ...