Evaluate your organization's DevOps practices across key areas and receive actionable insights for improvement.
Teams work in complete isolation with minimal communication
Teams occasionally collaborate but mostly work independently
Teams regularly collaborate with established communication channels
Teams are fully integrated with shared goals and continuous collaboration
Failures are blamed and punished
Failures are investigated but learning is limited
Failures are treated as learning opportunities with some process improvements
Failures drive systematic learning with blameless post-mortems and continuous improvement
All decisions require multiple levels of approval
Teams can make minor decisions but need approval for most changes
Teams have autonomy for most technical decisions within guidelines
Teams are fully autonomous and accountable for their outcomes
0-25% - Mostly manual configuration
26-50% - Some automation with manual processes
51-75% - Majority automated with some manual tasks
76-100% - Fully automated infrastructure as code
Mostly manual testing with minimal automation
Basic unit tests with some automated integration tests
Comprehensive automated testing including unit, integration, and some end-to-end tests
Fully automated testing pipeline with unit, integration, end-to-end, and performance tests
Manual setup taking days or weeks
Semi-automated with scripts but still requires manual intervention
Mostly automated with consistent environments across stages
Fully automated on-demand environment provisioning in minutes
Rarely - Few times per year
Monthly - Once or twice per month
Weekly - Multiple times per week
Daily - Multiple times per day
Manual deployments with significant downtime
Semi-automated deployments with some downtime
Automated deployments with minimal downtime
Zero-downtime automated deployments with rollback capabilities
No formal CI/CD pipeline
Basic CI with manual deployment steps
Automated CI/CD with testing and deployment to staging
Fully automated CI/CD with testing, security scans, and production deployment
Manual rollback process that takes hours
Semi-automated rollback with some manual steps
Automated rollback capabilities with monitoring alerts
Instant automated rollback with comprehensive failure detection
Basic server monitoring only
Application-level monitoring with basic metrics
Comprehensive monitoring with custom metrics and dashboards
Full observability with metrics, logs, traces, and business KPIs
Issues discovered by users, response time in hours
Basic alerting with response time in 30-60 minutes
Proactive monitoring with response time in 10-30 minutes
Real-time detection with automated response in under 10 minutes
Logs stored locally with manual analysis
Centralized logging with basic search capabilities
Structured logging with automated analysis and alerting
Advanced log analytics with ML-powered insights and correlation
Security is handled separately at the end
Basic security checks during development
Security integrated into CI/CD with automated scans
Security is fully integrated throughout the entire development lifecycle
Hardcoded in source code or configuration files
Basic secret management with limited access control
Dedicated secret management system with role-based access
Enterprise-grade secret management with rotation and audit trails
Manual compliance processes with limited documentation
Basic compliance tracking with some automation
Automated compliance monitoring with regular reporting
Continuous compliance with real-time monitoring and automated remediation
No formal metrics or tracking
Basic metrics like deployment frequency
Comprehensive metrics including lead time, MTTR, and change failure rate
Advanced analytics with business impact correlation and predictive insights
Limited feedback collection with slow response
Basic feedback mechanisms with periodic reviews
Regular feedback collection with systematic improvements
Continuous feedback loops with real-time adjustments and experimentation
Decisions based on intuition and experience
Some data used but decisions often subjective
Data-informed decisions with regular analysis
Fully data-driven decisions with advanced analytics and A/B testing