Yes, you can deploy Model Context Protocol (MCP) servers on serverless infrastructure, but there are important considerations to address. Serverless platforms like AWS Lambda, Google Cloud Functions, or Azure Functions allow you to run code without managing servers, which aligns with the event-driven, scalable nature of many MCP use cases. For example, if your MCP server handles API requests for model inference or context management, serverless can automatically scale to handle traffic spikes. However, serverless environments impose constraints, such as execution time limits (e.g., 15 minutes for AWS Lambda) and statelessness, which may require adjustments to how MCP servers operate.
A key challenge is managing state. MCP servers often need to maintain context—such as session data or model parameters—across multiple requests. Serverless functions are stateless by design, so you’d need external storage solutions like Redis, DynamoDB, or cloud-native key-value stores to persist this information. For instance, if your MCP server tracks conversational context for a chatbot, each function invocation would retrieve the latest state from a database before processing a request and update it afterward. This adds latency but ensures consistency. Additionally, cold starts—delays when a function initializes after inactivity—can impact real-time performance, making asynchronous processing or pre-warming techniques necessary for latency-sensitive applications.
Another factor is resource limitations. Serverless platforms restrict memory, CPU, and payload sizes, which could affect MCP servers handling large models or complex computations. For example, deploying a GPU-accelerated model inference endpoint on serverless is impractical today, as most providers don’t offer GPU support. However, lightweight MCP tasks like routing requests, preprocessing data, or managing API gateways are well-suited to serverless. A hybrid approach—using serverless for stateless components and dedicated instances or containers for compute-heavy tasks—can balance cost and performance. Tools like AWS Step Functions or Azure Durable Functions also help orchestrate multi-step workflows, enabling more complex MCP logic within serverless constraints.