What is serverless architecture?

Serverless architecture is a cloud computing model where developers build and run applications without managing the underlying servers. Instead of provisioning or maintaining physical or virtual machines, the cloud provider (like AWS, Azure, or Google Cloud) dynamically allocates resources to execute code in response to events or requests. The term “serverless” can be misleading—servers are still involved, but their management is abstracted away. Developers deploy functions or services, and the provider handles scaling, availability, and infrastructure maintenance automatically. This model is often used for event-driven workloads, such as APIs, file processing, or scheduled tasks.

A common example is a serverless function triggered by an HTTP request or a file upload to cloud storage. For instance, when a user uploads an image to an S3 bucket, AWS Lambda can automatically resize it without requiring a dedicated server. The function runs only when needed, and you pay only for the compute time consumed during execution. This contrasts with traditional setups where servers run continuously, incurring costs even during idle periods. Serverless platforms also integrate with other managed services, such as databases or message queues, simplifying backend development. However, functions are stateless by design, so persistent data must be stored externally.

Serverless is best suited for short-lived, scalable tasks with unpredictable traffic. For example, a weather API that sees sporadic use could run efficiently on serverless infrastructure, scaling to zero when unused. However, it’s less ideal for long-running processes or applications requiring persistent connections, like real-time gaming. Cold starts—delays when a function initializes after inactivity—can also impact performance. Developers must design around these constraints, such as using provisioned concurrency or optimizing code for faster startup. While serverless reduces operational overhead, it introduces dependency on provider-specific tools and limits low-level control, so evaluate trade-offs before adoption.