What is serverless-first development?

Serverless-first development is an approach where applications are designed to use serverless cloud services by default, unless there’s a specific reason to choose another architecture. In this model, developers prioritize managed services like AWS Lambda, Azure Functions, or Google Cloud Functions, which abstract server management entirely. The goal is to focus on writing code for discrete functions or services while the cloud provider handles infrastructure, scaling, and maintenance. This approach shifts responsibility away from tasks like provisioning servers or configuring clusters, allowing teams to concentrate on business logic and user-facing features.

A key benefit of serverless-first development is reduced operational complexity. For example, a web application might use AWS Lambda for backend APIs, Amazon S3 for hosting static frontend files, and DynamoDB for database needs. The Lambda functions automatically scale with traffic, and costs are based on execution time rather than pre-allocated server capacity. Event-driven workflows are another common use case: an image upload to cloud storage could trigger a serverless function to resize the image, which then saves the result to a database. These patterns eliminate the need to manage long-running servers, as the cloud provider starts and stops compute resources on demand.

However, serverless-first isn’t always the best fit. Cold starts—delays when a function initializes—can affect latency-sensitive applications. Vendor lock-in is another concern, as migrating serverless code between cloud providers often requires rewrites. Debugging distributed serverless systems can also be challenging, requiring tools like AWS X-Ray or dedicated logging services. Developers often use frameworks like the Serverless Framework or AWS SAM to streamline deployment and testing. While serverless-first excels for sporadic workloads or rapid prototyping, tasks like video encoding or machine learning training might still require traditional servers or containers for better cost control or performance. The approach works best when teams evaluate trade-offs between convenience, cost, and technical constraints upfront.