What are the differences between serverless and PaaS?

Serverless computing and Platform-as-a-Service (PaaS) both simplify application deployment but differ in how they handle infrastructure, execution models, and cost structures. Serverless abstracts servers entirely, allowing developers to focus solely on code, while PaaS provides a managed platform where developers deploy apps without managing underlying infrastructure but still configure runtime environments. Understanding these distinctions helps choose the right tool for specific use cases.

The first major difference lies in infrastructure management. With serverless (e.g., AWS Lambda, Azure Functions), the cloud provider dynamically allocates resources only when code runs, scaling automatically to zero when idle. Developers don’t configure servers or runtime environments—they simply upload code. PaaS (e.g., Heroku, Google App Engine), however, requires defining runtime settings (e.g., language version, dependencies) and scaling rules. For example, deploying a Node.js app on Heroku involves specifying the Node version and scaling dynos (containers), whereas a Lambda function runs without manual environment setup. PaaS applications typically run continuously, even with no traffic, while serverless functions activate only on demand.

Execution models also differ. Serverless functions are event-driven and stateless, triggered by events like HTTP requests or database changes. Each invocation is isolated, and functions terminate after execution, leading to potential cold-start delays if inactive. PaaS apps, in contrast, run persistently. A Flask app deployed on Google App Engine remains available 24/7, handling requests as they arrive. This makes PaaS better suited for long-running processes or stateful applications, while serverless excels at sporadic, short-lived tasks (e.g., processing a file upload or handling an API request).

Cost structures vary significantly. Serverless pricing is based on execution time and resource consumption per invocation. For example, AWS Lambda charges per millisecond of runtime and memory used, making it cost-effective for low-traffic or unpredictable workloads. PaaS platforms bill for allocated resources (e.g., Heroku dynos) regardless of usage, so idle apps still incur costs. A high-traffic web app might be cheaper on PaaS due to steady resource use, while a backend processing task that runs once daily would save costs with serverless. Choosing between them depends on workload patterns and the need for persistent availability versus granular scaling.