Proactively discover weaknesses before they become incidents. Build confidence in your system's ability to withstand turbulent conditions.
Modern distributed systems fail in unexpected ways. No amount of testing can predict every failure mode. Chaos engineering is the discipline of experimenting on a system to build confidence in its capability to withstand turbulent conditions in production.
Instead of waiting for 3 AM incidents to discover your system's weaknesses, chaos engineering lets you find them on your terms—during business hours, with the team ready, in a controlled environment.
The result: fewer surprises, faster recovery, and systems that gracefully degrade instead of catastrophically fail.
A structured approach to building a sustainable chaos engineering practice.
Establish measurable indicators of healthy system behavior—your steady state hypothesis
Map critical paths, dependencies, and high-risk components for experimentation
Create hypotheses about system behavior under specific failure conditions
Schedule experiments with clear scope, blast radius limits, and abort criteria
Run controlled experiments using AWS Fault Injection Simulator (FIS)
Monitor system behavior, compare against steady state, record findings
Common fault injection scenarios we run with AWS Fault Injection Simulator.
Terminate instances, stress CPU/memory, simulate spot interruptions. Verify auto-scaling and failover behavior.
Inject latency, packet loss, and blackhole traffic. Test timeouts, retries, and circuit breakers.
Force RDS failovers, simulate replica lag, test connection pool exhaustion and recovery.
Kill ECS tasks, stress EKS pods, test container orchestration resilience and scheduling.
Simulate availability zone outages. Verify multi-AZ deployments actually work under pressure.
Block calls to downstream services, APIs, and third parties. Test graceful degradation patterns.
Machine learning that makes your chaos experiments smarter and more effective.
AI analyzes your architecture, traffic patterns, and incident history to recommend the most valuable experiments to run next.
ML models identify components likely to fail under stress before you run experiments, helping prioritize hardening efforts.
Automated generation of experiment hypotheses based on system topology, past incidents, and industry failure patterns.
AI-powered impact prediction helps you understand potential customer impact before running experiments.
Automatically correlate experiment results with metrics, logs, and traces to surface hidden dependencies and failure modes.
Experiments that automatically adjust intensity based on real-time system response, maximizing learning while minimizing risk.
From one-off game days to embedded chaos engineering practices.
A facilitated chaos engineering session to stress-test a specific system.
Build a sustainable chaos engineering practice with embedded support.
Ongoing chaos engineering support and experiment development.
Let's discuss how chaos engineering can build confidence in your system's resilience.
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