| name | optimize-costs |
| description | AI-powered multi-cloud cost optimization skill with ML-based recommendations, predictive spending analysis, and automated cost-saving measures. Use when cloud costs spike unexpectedly or when you need to optimize resource allocation across providers with advanced AI capabilities. |
| license | AGPLv3 |
| metadata | {"author":"agentic-reconciliation-engine","version":"2.0","category":"enterprise","risk_level":"medium","autonomy":"conditional","layer":"temporal"} |
| compatibility | Requires Python 3.8+, cloud provider CLI tools (AWS CLI, Azure CLI, gcloud), and access to multi-cloud monitoring systems |
| allowed-tools | Bash Read Write Grep |
Cost Optimizer — Multi-Cloud Enterprise Automation with AI
Purpose
Enterprise-grade automation solution for cost optimizer operations across AWS, Azure, GCP, and on-premise environments with advanced AI capabilities including ML-based cost optimization, predictive spending analysis, and automated cost-saving measures to maximize operational efficiency while maintaining security and compliance standards.
When to Use
- Cost Spikes: When monthly cloud bills increase unexpectedly (>20% month-over-month)
- Resource Waste: When you suspect unused or underutilized resources (idle instances, abandoned storage)
- Budget Planning: For quarterly budget reviews and cost forecasting with AI predictions
- Provider Comparison: When evaluating which cloud provider offers better pricing for specific workloads
- Rightsizing: When VMs or containers appear over-provisioned for actual usage
- Storage Optimization: When storage costs grow faster than data usage patterns
- Network Costs: When data transfer egress charges become significant
- AI-Powered Analysis: For ML-based cost optimization and predictive spending insights
Gotchas
Common Pitfalls
- Reserved Instance Conflicts: Don't recommend RI purchases without checking existing commitments and expiration dates
- Usage Forecasting: Cost optimization based on single-day data can be misleading. Use 30-90 day trends
- Cross-Provider Arbitrage: Network egress costs often offset compute savings between providers
- Tagging Inconsistency: Untagged resources cannot be properly categorized or optimized
Edge Cases
- Burst Workloads: Seasonal businesses need different optimization strategies than steady-state workloads
- Compliance Requirements: Some regions/instance types may be required for compliance despite higher costs
- Multi-Region Deployments: Latency requirements may prevent consolidation to cheaper regions
- Legacy Applications: Older systems may not support modern instance types or storage classes
Performance Issues
- API Rate Limits: Cost Explorer APIs have strict limits (5 requests/second for AWS). Implement caching
- Large Account Analysis: Accounts with >10,000 resources may timeout. Use pagination and parallel processing
- Currency Conversion: Always convert to single currency for accurate comparison across providers
- Data Retention: Cost and usage data typically only available for 12 months in most cloud providers
Security Considerations
- Cost Data Sensitivity: Cloud billing data can reveal company size, usage patterns, and architecture details
- Access Permissions: Cost optimization requires billing access which may be restricted in some organizations
- Third-Party Tools: Be cautious with external cost management tools that require full billing access
- Data Export: Exporting cost data may violate compliance or data residency requirements
Troubleshooting
- Missing Cost Data: Check if Cost and Usage Reports are enabled (not automatic in all accounts)
- Inconsistent Metrics: Different providers categorize costs differently (e.g., data transfer vs. networking)
- Optimization Conflicts: Storage tiering recommendations may conflict with performance requirements
- Currency Fluctuations: International deployments affected by exchange rate changes
Inputs
- operation: Operation type (required)
- targetResource: Target resource identifier (required)
- cloudProvider: Cloud provider -
aws|azure|gcp|onprem|all (optional, default: all)
- parameters: Operation-specific parameters including AI model configurations (optional)
- environment: Target environment (optional, default:
production)
- dryRun: Dry run mode (optional, default:
true)
- historicalData: Include historical cost data for predictive analysis (optional, default:
false)
- optimizationType: Type of optimization -
rightsizing|scheduling|storage|networking|licenses|reservations (optional)
Process
- Cloud Provider Detection: Identify target cloud providers and environments
- Input Validation: Comprehensive parameter validation and security checks
- Multi-Cloud Context Analysis: Analyze current state across all providers
- AI-Powered Cost Analysis: Apply ML-based cost optimization, predictive spending analysis, and anomaly detection
- Optimization Planning: Generate AI-enhanced optimization recommendations with risk assessment
- Safety Assessment: Risk analysis and impact evaluation across providers
- Execution: Perform optimizations with monitoring and validation
- Results Analysis: Process results and generate AI-enhanced cost reports
Outputs
- Operation Results: Detailed execution results and status per provider
- AI-Enhanced Recommendations: ML-generated cost optimization recommendations with predictive insights
- Cost Analysis: Comprehensive cost metrics and waste identification
- Predictive Forecasts: Future spending predictions and budget planning insights
- Compliance Reports: Validation and compliance status across environments
- Performance Metrics: Operation performance and efficiency metrics by provider
- Cost Optimization Results: Before/after cost comparisons and ROI analysis
- Audit Trail: Complete operation history for compliance across all providers
Environment
- AWS: EKS, EC2, Lambda, CloudWatch, IAM, S3, Cost Explorer
- Azure: AKS, VMs, Functions, Monitor, Azure AD, Cost Management
- GCP: GKE, Compute Engine, Cloud Functions, Cloud Monitoring, Cloud Billing
- On-Premise: Kubernetes clusters, VMware, OpenStack, Prometheus, Cost Analysis Tools
Dependencies
- Python 3.8+: Core execution environment
- AI/ML Libraries: scikit-learn, pandas, numpy, statsmodels, tensorflow/keras
- Cloud SDKs: boto3, azure-sdk, google-cloud
- Cost Analysis Tools: Cloud provider cost APIs, billing exports
- Optimization Libraries: scipy, cvxopt for advanced optimization
- Time Series Libraries: prophet, pmdarima for predictive analysis
- Kubernetes: kubernetes client for cluster operations
- Multi-Cloud Libraries: terraform-python, ansible-python
Scripts
core/scripts/automation/cost-optimizer.py: Main AI-powered optimization implementationcore/scripts/automation/cost-optimizer_handler.py: Cloud-specific operationscore/scripts/automation/multi_cloud_orchestrator.py: Cross-provider coordination
Trigger Keywords
cost, optimizer, automation, enterprise, multi-cloud, ai, ml, predictive, aws, azure, gcp, onprem
Human Gate Requirements
- Production changes: Production environment operations require approval
- High-impact operations: Critical operations require review
- Security changes: Security modifications need validation
- AI Model Updates: Changes to AI models require approval
- Resource Scaling: Major scaling operations need human oversight
Enterprise Features
- Multi-tenant Support: Isolated operations per tenant
- Role-based Access Control: Enterprise IAM integration
- Audit Logging: Complete audit trail for compliance
- Performance Monitoring: SLA tracking and metrics
- Security Hardening: Encryption and compliance standards
- Dynamic Code Generation: Agents can modify logic dynamically
- Cross-Cloud Orchestration: Coordinated optimizations across providers
- AI-Powered Insights: Advanced analytics and predictive capabilities
- Automated Learning: Continuous improvement from cost patterns
References
Load these files when needed:
scripts/cost-analyzer.py - Core cost analysis algorithms with AIscripts/provider-handlers/ - Cloud provider specific cost APIsreferences/cost-optimization-patterns.md - Proven optimization strategiesassets/instance-type-mapping.csv - Cross-provider instance comparisonsexamples/cost-reduction-cases/ - Real-world optimization examples
