October 2026 Summaries

Chaos engineering is presented as a way for organizations to test how systems respond to failures that conventional automated tests often overlook, such as unavailable cloud dependencies, slow databases, network disruptions, or third-party API errors. Citing a 2025 AWS us-east-1 incident and rising estimates of outage costs, the discussion argues that concentrated dependencies, faster AI-assisted software changes, and resilience regulations such as the EU’s Digital Operational Resilience Act have increased its relevance. It proposes calculating return on investment by comparing the cost of outages prevented with the costs of running experiments and any experiment-induced harm, using a SaaS example in which preventing two 90-minute outages yields a reported 309% ROI and suggesting that a program costing $220,000 would break even after preventing roughly 44 minutes of downtime at $300,000 per hour. Organizations are advised to estimate their downtime costs, review postmortems to identify failures that testing could have exposed, contain experiment risk through limited blast radii and safeguards, and measure risks discovered, fixes completed, and coverage improvements. The piece also identifies less easily quantified benefits, including faster recovery, audit evidence, more confident releases, and potentially better cloud-cost management, while promoting Steadybit’s tools and templates for implementing and reporting on chaos engineering programs.