What is the difference between stateful and stateless serverless applications?

Stateful and stateless serverless applications differ in how they handle data and context between requests or function invocations. In a stateless serverless application, each function or service call operates independently without retaining data from previous interactions. The function processes a request using only the input provided and external storage (like a database), then discards any temporary data once the task is complete. In contrast, a stateful serverless application maintains some form of context or data between invocations, often requiring coordination to track changes or progress across multiple requests. This distinction impacts how developers design scalability, fault tolerance, and data management.

Stateless serverless applications prioritize simplicity and scalability. For example, an AWS Lambda function that resizes images uploaded to an S3 bucket is stateless: it reads the input file, processes it, saves the result back to storage, and terminates. No data about the operation is retained in the function’s runtime after execution. Stateless designs rely entirely on external systems (like databases, caches, or object storage) to persist data. This approach allows cloud providers to scale instances horizontally—since any function instance can handle any request—and simplifies recovery from failures, as there’s no local state to lose. APIs that fetch user profiles from a database or validate authentication tokens are common stateless use cases.

Stateful serverless applications, while less common, manage ongoing workflows or user sessions. For instance, an e-commerce checkout process spanning multiple steps (cart updates, payment, confirmation) requires tracking order state. Serverless platforms like AWS Step Functions or Azure Durable Functions enable this by externalizing state into managed services. The functions themselves remain stateless, but the orchestration layer (e.g., Step Functions) maintains progress and passes context between steps. Another example is a real-time game backend that tracks player positions; here, state is stored in a fast, external service like Redis. While stateful patterns add complexity, they allow serverless architectures to handle multi-step processes without violating the “stateless function” principle. Developers must carefully manage consistency and latency when relying on external state.