Serverless applications handle state by relying on external storage services instead of maintaining it within the runtime environment. Since serverless functions are ephemeral and stateless by design—meaning each invocation starts fresh—any data needed between requests or function calls must be stored outside the function itself. This is typically done using databases, caching systems, or object storage. For example, a serverless API might use Amazon DynamoDB to store user profiles or Azure Cosmos DB for session data. These services provide durable, scalable storage that persists beyond the lifespan of individual function executions.
Developers often use a combination of databases and caching layers to balance performance and durability. For frequently accessed data, like user preferences or temporary session tokens, a distributed cache such as Redis (via AWS ElastiCache or Azure Cache) can reduce latency. For larger files, object storage services like AWS S3 or Azure Blob Storage are common. A practical example is a file-processing app where a serverless function uploads a file to S3, triggers a database entry with metadata, and uses a cache to track processing status. This separation ensures state survives function restarts or scaling events.
To manage state across multiple functions or workflows, event-driven patterns and orchestration tools are used. Services like AWS Step Functions or Azure Durable Functions allow chaining functions while maintaining context. For instance, an e-commerce checkout process might use Step Functions to track an order through payment, inventory checks, and shipping updates—storing each step’s outcome in a shared state object. Client-side storage (like cookies or tokens) can also offload state management, such as storing a session ID in a JSON Web Token (JWT) that the server validates against a database. This approach keeps functions lightweight while ensuring consistent state handling.