Serverless computing and Kubernetes are both used to deploy applications, but they differ in architecture, management, and use cases. Serverless platforms like AWS Lambda or Azure Functions abstract away infrastructure management entirely. Developers deploy code, and the cloud provider handles servers, scaling, and maintenance. In contrast, Kubernetes is a container orchestration system where you manage clusters of servers (physical or virtual) to run containerized applications. Kubernetes gives you control over infrastructure configuration, networking, and scaling policies, but requires ongoing operational oversight.
The key distinction lies in scaling and cost models. Serverless automatically scales to zero when there’s no traffic, and you pay only for the time your code runs. For example, a Lambda function triggered by an HTTP request executes on demand, with no cost when idle. Kubernetes, however, requires pre-allocated resources (like nodes in a cluster) even if applications aren’t active. While Kubernetes can autoscale pods (containers) based on metrics like CPU usage, the underlying nodes still incur costs. This makes serverless cost-effective for sporadic workloads but less predictable for high-traffic scenarios, where Kubernetes’ fixed infrastructure might be more efficient.
Use cases also differ. Serverless suits event-driven tasks like image processing after a file upload or short-lived API backends. For example, a serverless function could resize images stored in a cloud bucket without needing a constantly running service. Kubernetes excels for complex, long-running applications like microservices with dependencies (e.g., databases, caching layers) or stateful workloads. A company running a multi-tier e-commerce platform with custom scaling rules might choose Kubernetes for granular control. Serverless prioritizes simplicity and reduced ops overhead, while Kubernetes offers flexibility at the cost of operational complexity.