Chaos EngineeringSimplified
Build resilient systems through intelligent fault injection, automated analysis, and comprehensive insights. Our platform makes chaos engineering accessible to teams of all sizes.
Chaos Engineering is the discipline of experimenting on a system to build confidence in the system's capability to withstand turbulent conditions in production. It involves deliberately introducing failures to uncover weaknesses before they manifest as outages.
Benefits:
- Improved system reliability
- Faster incident response
- Increased confidence in deployments
- Better understanding of system behavior
Key Principles:
- Hypothesize about steady state
- Vary real-world events
- Run experiments in production
- Minimize blast radius
1. Upload & Analyze
Upload your Kubernetes YAML files and our AI analyzes them for potential vulnerabilities and failure points using advanced pattern recognition.
2. AI Planning
Our reinforcement learning algorithms generate intelligent fault injection plans tailored to your specific infrastructure and risk tolerance.
3. Safe Execution
Execute controlled chaos experiments with built-in safety mechanisms, automated rollbacks, and real-time monitoring.
4. Insights & Reports
Get comprehensive analysis with actionable insights, performance metrics, and recommendations for improving system resilience.
Phase 1: Infrastructure Analysis
Upload your Kubernetes manifests (YAML files) containing deployments, services, pods, and configurations. Our platform performs deep analysis to identify:
- • Resource constraints and limits
- • Service dependencies and communication patterns
- • Health check configurations
- • Security contexts and permissions
- • Network policies and exposure points
Phase 2: Vulnerability Detection
Our AI algorithms scan for common failure patterns and vulnerabilities:
- • Missing resource limits
- • No health checks
- • Single points of failure
- • Suboptimal configurations
- • Missing redundancy
- • Network vulnerabilities
Phase 3: Intelligent Planning
Based on the analysis, our RL-powered system generates targeted chaos experiments:
CPU/Memory Stress
Test resource limits and scaling behavior
Pod Termination
Verify restart policies and resilience
Network Faults
Test latency, packet loss, and timeouts
Phase 4: Controlled Execution
Execute experiments with built-in safety measures:
- • Gradual rollout with blast radius control
- • Real-time monitoring and automatic circuit breakers
- • Instant rollback capabilities
- • Comprehensive logging and metrics collection
Phase 5: Analysis & Insights
Generate actionable insights from experiment results:
- • Performance impact analysis
- • Recovery time measurements
- • System behavior patterns
- • Recommendations for improvements
- • Compliance and audit reports
Core Technologies
Safety Mechanisms
Quick Start
Upload your first YAML files and see results in under 5 minutes
Safe by Design
Built-in safety mechanisms ensure experiments don't cause outages
Continuous Learning
AI algorithms improve recommendations based on your results
Is chaos engineering safe for production?
Yes, when done correctly. Our platform includes multiple safety mechanisms including blast radius control, automated rollbacks, and real-time monitoring to ensure experiments don't cause outages.
What types of systems can I test?
Currently, we support Kubernetes-based applications. Support for other container orchestrators and cloud services is planned for future releases.
How does the AI planning work?
Our reinforcement learning algorithms analyze your infrastructure patterns, previous experiment results, and industry best practices to generate targeted fault injection plans.
Can I customize the experiments?
Yes, while our AI provides intelligent defaults, you can review, modify, and approve all experiments before execution to match your specific requirements and risk tolerance.