Demystifying Serverless Costs on Public Platforms: Bridging Billing, Architecture, and OS Scheduling

TL;DR

This paper provides a comprehensive analysis of serverless platform costs, revealing how billing practices and OS scheduling contribute to inflated expenses and offering insights for more cost-effective designs.

Contribution

It offers the first holistic characterization of serverless costs, linking billing, architecture, and OS scheduling to identify key cost drivers and inefficiencies.

Findings

Billing practices inflate costs up to 4.35x beyond actual resource use.

Operational patterns and OS scheduling impact performance and billing.

Insights enable designing more cost-efficient serverless systems.

Abstract

Public cloud serverless platforms have attracted a large user base due to their high scalability, plug-and-play deployment model, and pay-per-use billing. However, compared to virtual machines and container hosting services, modern serverless offerings typically impose higher per-unit time and resource charges. Additionally, billing practices such as wall-clock time allocation-based billing, invocation fees, and usage rounding up can further increase costs. This work, for the first time, holistically demystifies these costs by conducting an in-depth, top-down characterization and analysis from user-facing billing models, through request serving architectures, and down to operating system scheduling on major public serverless platforms. We quantify, for the first time, how current billing practices inflate billable resources up to 4.35x beyond actual consumption. Also, our analysis reveals previously unreported cost drivers, such as operational patterns of serving architectures that create overheads, details of resource allocation during keep-alive periods, and OS scheduling granularity effects that directly impact both performance and billing. By tracing the sources of costs from billing models down to OS scheduling, we uncover the rationale behind today's expensive serverless billing model and practices and provide insights for designing performant and cost-effective serverless systems.