Serverless Computing definition
Serverless computing is a cloud model in which developers run code and use managed services without provisioning or managing servers. The provider allocates resources automatically for each request or event, scales from zero to large volumes and bills only for actual usage. AWS Lambda, Azure Functions and Google Cloud Run are typical examples.
How does serverless computing work?
Developers write small functions and connect them to triggers: an HTTP request through an API gateway, a message on a queue, a file uploaded to storage, a database change or a schedule. When an event arrives, the platform starts an execution environment, runs the function and returns the result, reusing warm environments for later requests when it can. Functions are stateless, have time limits and store data in external services.
Serverless is broader than functions. Managed services that scale automatically and bill per use, such as Amazon DynamoDB, S3, SQS, Aurora Serverless, Google Cloud Run and Azure Container Apps, are also called serverless. A typical serverless application combines functions with these services and contains no servers the team has to patch or size.
Common serverless use cases
Serverless fits best when work arrives in bursts or in response to events, and when each task finishes in seconds or minutes. Long-running jobs, persistent connections and workloads with very steady high traffic are often better served by containers. Many systems mix both styles.
- REST and GraphQL APIs with variable traffic.
- Processing uploaded images, documents and videos.
- Webhooks from payment providers and SaaS tools.
- Scheduled jobs such as reports, cleanups and data syncs.
- Event-driven pipelines that react to queue messages or database changes.
- Chatbot and AI inference backends that call model APIs.
Serverless vs containers
Containers package an application with its dependencies and run as long-lived processes, typically on Kubernetes or a container service. They give full control over runtime, networking and execution time, and are cost-efficient for steady traffic. Serverless functions trade that control for zero server management, automatic scaling and paying nothing when idle. Serverless containers, such as Cloud Run and AWS Fargate, blur the line by running containers without managing nodes.
Pros and cons of serverless
The advantages are no infrastructure to manage, scaling from zero to high volume automatically, paying only for execution time and faster delivery for small teams. Frameworks such as AWS SAM, the Serverless Framework and SST make deployment repeatable. It also encourages small, focused components that are easy to change independently.
The disadvantages are cold starts, the extra latency when a new environment starts; execution time and memory limits, such as AWS Lambda's 15-minute maximum; harder local testing and debugging across many functions; vendor lock-in through provider-specific triggers; and costs that can exceed containers for constant high traffic. Observability needs careful setup from the start.
Example: an image processing pipeline
A marketplace lets sellers upload product photos. Each upload to object storage triggers a function that validates the image, creates thumbnails in several sizes, strips location metadata and writes the results back, while another function updates the product record. The pipeline costs almost nothing on quiet days and scales automatically during busy sale periods. Nexzem builds serverless backends like this with infrastructure as code, tracing and cost alerts in place from the first deployment.