Kubiya transforms common operational tasks from time-consuming manual processes into intelligent, automated workflows. Here are the most impactful use cases teams implement, with concrete examples and benefits.
DevOps Automation Application Deployments Transform deployment processes from error-prone manual steps to reliable, auditable workflows:
Traditional Process Kubiya Automation Manual deployment steps (45-90 minutes):
Developer requests deployment in Slack
DevOps engineer validates readiness
Manual kubectl commands or CI/CD trigger
Manual monitoring of health checks
Manual rollback if issues detected
Status updates scattered across tools
Real example workflow: from kubiya_workflow_sdk import Workflow, Step # Deployment workflow with proper validation and rollback deployment_workflow = Workflow( name = "microservice-deployment" , description = "Deploy microservice with health checks and rollback" ) # Pre-deployment validation deployment_workflow.add_step( Step( "validate-build" ) .tool( "github" ) .command( "gh api repos/myorg/user-service/actions/runs --jq '.workflow_runs[0].conclusion'" ) .condition( "== 'success'" ) ) # Deploy to staging first deployment_workflow.add_step( Step( "deploy-staging" ) .tool( "kubectl" ) .command( "kubectl set image deployment/user-service user-service=user-service:v2.3.1 -n staging" ) .depends([ "validate-build" ]) ) # Health check deployment_workflow.add_step( Step( "health-check" ) .tool( "curl" ) .command( "curl -f http://user-service.staging.svc.cluster.local/health" ) .retry( 3 ) .depends([ "deploy-staging" ]) ) # Production deployment deployment_workflow.add_step( Step( "deploy-production" ) .tool( "kubectl" ) .command( "kubectl set image deployment/user-service user-service=user-service:v2.3.1 -n production" ) .depends([ "health-check" ]) ) Infrastructure Provisioning Automate infrastructure changes with safety and consistency:
Common scenarios:
“Spin up a development environment for the mobile team” “Scale production cluster capacity for Black Friday traffic” “Create disaster recovery environment in us-east-2” “Decommission old staging resources to reduce costs”
# Auto-scaling workflow example name : traffic-surge-response trigger : type : metric_threshold condition : "avg(cpu_utilization) > 75% for 10 minutes" steps : - name : analyze-traffic-patterns tool : datadog-analyzer - name : provision-additional-nodes tool : terraform-executor inputs : action : apply var_file : surge-scaling.tfvars - name : update-load-balancer tool : aws-alb inputs : targets : ${provision-additional-nodes.new_instances} - name : notify-team tool : slack message : "🚀 Auto-scaled cluster for traffic surge: +${new_node_count} nodes" Release Management Coordinate complex releases across multiple services and teams:
Multi-service release workflow: name : quarterly-release-q2-2024 description : "Coordinated release across 12 microservices" phases : - name : pre-release-validation parallel_checks : - database-migration-tests - api-compatibility-validation - security-vulnerability-scans - performance-regression-tests - name : staged-rollout sequence : - services : [ auth-service , user-service ] # Foundation services first strategy : blue-green - services : [ payment-service , order-service ] # Business logic strategy : canary depends_on : [ auth-service , user-service ] - services : [ notification-service , analytics-service ] # Supporting services strategy : rolling depends_on : [ payment-service , order-service ] - name : post-release-verification checks : - end-to-end-user-journey-tests - business-metrics-validation - error-rate-monitoring - performance-benchmarks Site Reliability Engineering (SRE) Accelerate mean time to resolution (MTTR) with intelligent automation:
Before Kubiya MTTR: 45-120 minutes
Manual log collection from multiple systems
Context switching between monitoring tools
Tribal knowledge required for diagnosis
Manual remediation steps
With Kubiya MTTR: 10-25 minutes
Automated evidence collection
AI-assisted root cause analysis
Context-aware remediation suggestions
One-click remediation execution
Example incident response workflow: PagerDuty Alert: "Payment API 5xx errors spiking" ↓ Kubiya automatically: 1. Collects logs from payment service pods 2. Queries database connection metrics 3. Checks recent deployments and changes 4. Analyzes error patterns with AI 5. Suggests remediation: "Scale payment-db connection pool" 6. Presents one-click remediation options Capacity Planning & Scaling Proactive resource management based on usage patterns and business events:
name : black-friday-capacity-preparation schedule : "October 1st annually" steps : - name : analyze-historical-traffic tool : traffic-analyzer inputs : timeframe : "last 3 black fridays" services : [ "web-frontend" , "api-gateway" , "payment-service" ] - name : forecast-capacity-needs tool : ml-forecaster inputs : historical_data : ${analyze-historical-traffic.patterns} growth_rate : 15% # Expected YoY growth - name : pre-scale-infrastructure tool : multi-cloud-scaler inputs : aws_scaling : ${forecast-capacity-needs.aws_requirements} gcp_scaling : ${forecast-capacity-needs.gcp_requirements} - name : validate-scaling tool : load-tester inputs : traffic_multiplier : ${forecast-capacity-needs.peak_multiplier} - name : create-runbook tool : documentation-generator inputs : scaling_decisions : ${pre-scale-infrastructure.actions} validation_results : ${validate-scaling.performance_metrics} Chaos Engineering & Testing Automated resilience testing with safe failure injection:
name : monthly-chaos-testing description : "Automated resilience validation" experiments : - name : database-connection-failure target : payment-service failure_type : network_partition blast_radius : 25% # Only affect 25% of instances duration : 5m success_criteria : - circuit_breaker_triggered : true - fallback_mechanism_activated : true - user_impact : < 0.1% # Less than 0.1% user errors - name : memory-pressure-test target : recommendation-engine failure_type : memory_leak_simulation ramp_up : gradual # Increase memory pressure slowly abort_conditions : - pod_restart_required : true - response_latency : > 2000ms Automated Runbooks Convert tribal knowledge into executable, maintainable automation:
Database performance investigation runbook: name : database-performance-investigation trigger : alert : "Database query latency > 500ms for 5 minutes" investigation_steps : - name : collect-slow-queries tool : postgres-analyzer outputs : [ slow_query_log , query_plans ] - name : check-connection-pool tool : pgbouncer-metrics outputs : [ pool_utilization , wait_times ] - name : analyze-resource-usage tool : system-metrics inputs : resources : [ cpu , memory , disk_io , network ] timeframe : 1h - name : generate-recommendations tool : ai-analyzer inputs : slow_queries : ${collect-slow-queries.slow_query_log} resource_metrics : ${analyze-resource-usage.metrics} pool_metrics : ${check-connection-pool.utilization} automated_remediation : - condition : "connection_pool_exhausted" action : scale_connection_pool parameters : new_pool_size : ${current_pool_size * 1.5} - condition : "missing_index_detected" action : create_maintenance_ticket parameters : priority : high description : "Index needed: ${generate-recommendations.suggested_indexes}" Self-Service Workflows Enable development teams with safe, governed self-service capabilities:
Developer self-service scenarios:
“Create a preview environment for PR #1234” “Run end-to-end tests against staging” “Scale down my development environment overnight” “Get performance metrics for my service over the last week”
name : developer-environment-management permissions : - role : developer allowed_environments : [ dev , staging ] restricted_operations : [ production_access , resource_deletion ] templates : - name : create-preview-env description : "Spin up isolated environment for feature testing" inputs : - name : branch_name type : string required : true - name : services_to_deploy type : array default : [ "frontend" , "api" ] - name : run-test-suite description : "Execute comprehensive test suite" inputs : - name : test_type type : select options : [ "unit" , "integration" , "e2e" , "performance" ] - name : target_environment type : select options : [ "dev" , "staging" ] Compliance & Security Automation Automate security checks and compliance reporting:
name : security-compliance-scan schedule : "daily at 2 AM" scans : - name : vulnerability-assessment tool : trivy-scanner targets : [ container-images , kubernetes-manifests ] - name : configuration-drift-check tool : config-validator policies : [ cis-benchmarks , company-policies ] - name : access-review tool : rbac-analyzer checks : [ unused-permissions , overprivileged-accounts ] - name : secret-scanning tool : secret-scanner repositories : [ all-active-repos ] reporting : - format : pdf recipients : [ security-team@company.com , compliance@company.com ] - format : dashboard url : https://security-dashboard.company.com - format : jira_tickets condition : critical_vulnerabilities_found assignee : security-team Cost Optimization Automated resource management to control cloud spending:
name : cost-optimization-automation triggers : - schedule : "weekdays at 7 PM" # Scale down after hours - metric : "monthly_spend > budget_threshold" actions : - name : scale-down-dev-environments condition : "time.hour >= 19" # After 7 PM tool : multi-environment-scaler inputs : environments : [ dev , qa , staging ] scale_factor : 0.3 # Scale to 30% capacity - name : identify-unused-resources tool : resource-analyzer age_threshold : 30d # Unused for 30+ days - name : rightsizing-recommendations tool : rightsizing-analyzer lookback_period : 2w utilization_threshold : 20% # Under 20% utilization - name : reserved-instance-optimizer tool : ri-optimizer savings_threshold : 15% # Only suggest if 15%+ savings notifications : - type : slack channel : "#platform-cost-alerts" message : "💰 Monthly savings achieved: $${cost_savings}" - type : executive-report recipients : [ cto@company.com , cfo@company.com ] frequency : monthly ChatOps & Collaboration Slack/Teams Integration Bring automation directly into team communication channels:
Slash Commands /kubiya deploy frontend v2.1.0 to staging
Natural Language “Can you check if the payment service is healthy?”
Interactive Buttons Click to approve deployments or execute runbooks
Status Updates Automated progress updates and completion notifications
Example ChatOps integration: name : slack-ops-integration channels : - name : "#deployments" permissions : [ deploy , rollback , status-check ] environments : [ staging , production ] - name : "#incidents" permissions : [ investigate , remediate , escalate ] auto_response : true - name : "#platform-requests" permissions : [ create-environment , run-tests , access-logs ] approval_required : false interactive_commands : - trigger : "/deploy {service} {version} to {environment}" workflow : service-deployment confirmations : - condition : environment == "production" message : "⚠️ This will deploy to production. Continue?" - trigger : "investigate {service} performance" workflow : performance-investigation auto_execute : true - trigger : "scale {service} to {replicas} replicas" workflow : service-scaling safety_checks : [ resource-limits , blast-radius ] Advanced Use Cases Multi-Cloud Orchestration Manage resources across different cloud providers seamlessly:
name : disaster-recovery-failover description : "Automated failover from AWS to GCP" steps : - name : detect-aws-outage tool : multi-cloud-health-checker condition : "aws_availability < 90%" - name : backup-aws-data tool : aws-backup-service parallel : true # Run while switching traffic - name : scale-up-gcp-infrastructure tool : gcp-autoscaler inputs : target_capacity : ${aws_current_capacity} regions : [ "us-central1" , "us-east1" ] - name : migrate-database tool : database-replicator source : aws_rds_primary destination : gcp_cloud_sql_replica - name : switch-dns tool : route53-updater inputs : records : [ "api.company.com" , "app.company.com" ] new_targets : ${scale-up-gcp-infrastructure.endpoints} - name : notify-stakeholders tool : multi-channel-notifier channels : [ slack , email , sms , status-page ] message : "🔄 DR failover completed: AWS → GCP" Compliance Automation Maintain regulatory compliance through automated processes:
name : gdpr-data-retention-compliance schedule : "monthly on 1st day" steps : - name : identify-expired-data tool : data-retention-scanner policies : user_activity_logs : 2_years payment_records : 7_years analytics_data : 1_year - name : data-anonymization tool : gdpr-anonymizer data_types : [ user_profiles , behavioral_data ] anonymization_method : k_anonymity - name : secure-deletion tool : secure-delete-service confirmation_required : true audit_trail : complete - name : compliance-reporting tool : compliance-reporter outputs : [ gdpr_compliance_certificate , audit_log ] recipients : [ dpo@company.com , legal@company.com ] Measuring Success Key Metrics Teams Track
Operational Efficiency
70% reduction in deployment time
85% fewer manual interventions
40% faster incident resolution
90% reduction in environment setup time
Quality & Reliability
95% deployment success rate
60% fewer production incidents
99.9% automated workflow reliability
Zero security policy violations
Team Productivity
4 hours/week saved per developer
80% of deployments are self-service
50% reduction in after-hours escalations
3x faster onboarding for new team members
Cost Optimization
25% reduction in cloud spend
90% reduction in idle resources
60% better resource utilization
ROI achieved within 3 months
Getting Started Ready to implement these use cases? Start with the patterns most relevant to your team’s pain points:
Success Pattern : Most teams start with read-only operations (monitoring, status checks) to build confidence, then gradually automate write operations (deployments, scaling) as they see the reliability and audit capabilities.
