What problem does it solve?
Chaos Engineering helps teams discover weaknesses before users do by designing, running, and analyzing resilience experiments that reveal how systems behave under failure. It removes ambiguity from resilience testing by providing a hypothesis-driven framework, safety controls, and observable success criteria so teams can validate that systems degrade safely and recover predictably.
Core Features & Use Cases
- Experiment design framework: Define steady-state metrics, craft hypotheses, and produce executable runbooks with blast-radius and rollback plans.
- Fault injection catalog: Ready experiments for pod failures, CPU/memory pressure, network latency/partitions, DNS and dependency outages, and external API failures.
- Game day planning & analysis: Run safe game days with kill-switch ownership, monitoring checklists, and postmortem action items; ideal for validating auto-scaling, circuit breakers, and failover strategies.
- Use Case: Run a staged chaos experiment to validate Redis fallback behavior for the payments API while minimizing customer impact and ensuring observability.
Quick Start
Assess the resilience of the payments service by proposing a staged chaos experiment targeting cache and network failures with defined steady-state metrics, a kill switch, and monitoring steps.
