Skyler: Static Analysis for Predicting API-Driven Costs in Serverless Applications

Unpredictable costs are a growing concern in serverless computing, where applications rely on cloud APIs with complex tiered pricing models. In many deployments, API calls dominate expenses, and a single overlooked design choice can escalate costs by thousands of dollars. Existing tools fall short: provider calculators need unrealistic manual estimates, and dynamic profilers only work post-deployment.

We present Skyler, a static analysis framework for predeployment cost estimation of API invocations in serverless workflows. Skyler models control flow behavior and pricing semantics to construct symbolic cost expressions using SMT formulas, exposing economic sinks, i.e., code paths where API usage disproportionately impacts cost. This enables developers to identify hotspots and prevent costly architectural errors early. Skyler supports JavaScript-based serverless applications across AWS Lambda, Google Cloud Functions, and Azure Functions, achieving high accuracy (mean absolute percentage error <1% for AWS and Google, 4.5% for Azure).