What is the pricing model for serverless services?

Serverless pricing models are primarily based on two factors: execution time and the number of requests. Providers charge for the compute resources consumed during the execution of a function, measured in milliseconds or gigabytes per second, and the total number of times the function is triggered. For example, AWS Lambda bills users for the time their code runs, rounded up to the nearest millisecond, multiplied by the memory allocated to the function. If a function uses 1GB of memory and runs for 1.2 seconds, the cost is calculated as (1.2 seconds * 1GB) at a per-GB-second rate. Additionally, most providers include a free tier, such as AWS’s monthly allowance of 1 million requests and 400,000 GB-seconds of compute time.

Beyond compute and requests, costs can vary based on ancillary services and provider-specific features. For instance, services like API Gateway (used to expose serverless functions via HTTP) often have separate pricing based on the number of API calls and data transferred. Azure Functions, for example, charges for execution units (a combination of memory and CPU) and offers a consumption plan where costs scale with usage. Google Cloud Functions includes network egress costs, which apply when data is sent outside the provider’s network. Memory allocation also plays a role: functions configured with higher memory tiers cost more per execution, even if they finish faster. Providers may also apply minimum billing durations (e.g., 100ms increments in some cases), which can add up for short-running functions.

Developers can optimize serverless costs by focusing on code efficiency and resource configuration. Reducing execution time through optimized code or caching can directly lower compute costs. For example, a function that processes data in 500ms instead of 1,000ms cuts compute time in half. Adjusting memory settings to match the workload (avoiding overallocation) and setting shorter timeouts to prevent idle execution also help. Monitoring tools like AWS CloudWatch or Azure Monitor can identify underused or overprovisioned functions. Some teams use provisioned concurrency (e.g., in AWS Lambda) to reduce cold-start latency, but this adds fixed costs. Finally, understanding a provider’s free tier and tiered pricing (e.g., discounts for high-volume usage) ensures cost-effective scaling. By balancing performance and resource allocation, developers can leverage serverless without overspending.