Multi-Cloud Cost Orchestrator
Coordinates cost management across AWS, Azure, and GCP for enterprise-scale optimization
Orchestrator Agent
Multi-Cloud Cost Management
ITmedium Risk
Complexity 5/5
Track spend across clouds, identify waste, optimize resources, renegotiate contracts
Managing costs across AWS, Azure, and GCP is nearly impossible without automation—zombie resources, suboptimal commitment plans, and fragmented visibility lead to massive waste. This agentic workflow aggregates cost data from all cloud providers, identifies idle resources and inefficient spending patterns, analyzes reserved versus on-demand usage, recommends commitment discounts, right-sizes over-provisioned instances, and even supports vendor negotiations with data-driven usage reports. By providing unified visibility and automated optimization across multi-cloud environments, this approach transforms cloud FinOps from reactive firefighting to proactive cost management. Enterprises implementing multi-cloud cost management achieve 30-40% total cloud spend reductions with ROI of 20-30x at enterprise scale, while maintaining real-time visibility into cost drivers. This is critical for large organizations with complex cloud footprints—particularly financial services, healthcare systems, retail chains, telecommunications providers, and global technology companies—where multi-cloud strategies are essential for resilience but create cost management complexity.
20-30x
Typical ROI
16-24 weeks
Time to Value
IT
Department
Complexity
Agent Architecture
Agent Architecture
A complex orchestrator managing cost aggregation, waste identification, optimization recommendations, and vendor negotiation across multiple cloud providers.
Cost Aggregator
Aggregates cost data from all cloud providers
- •Collect AWS costs
- •Collect Azure costs
- •Collect GCP costs
Cost Aggregator
Aggregates cost data from all cloud providers
Waste Identifier
Identifies zombie resources and waste
- •Find zombie resources
- •Analyze reserved vs on-demand
- •Identify waste
Waste Identifier
Identifies zombie resources and waste
Optimizer
Recommends commitment discounts and right-sizing
- •Recommend commitments
- •Right-size instances
- •Calculate savings
Optimizer
Recommends commitment discounts and right-sizing
Vendor Negotiator
Negotiates with vendors based on usage data
- •Prepare usage reports
- •Support negotiations
- •Track ROI
Vendor Negotiator
Negotiates with vendors based on usage data
Complex Orchestrator Architecture
Workflow Steps
1
Aggregate cost data from AWS, Azure, GCP
2
Identify zombie resources and waste
3
Analyze reserved vs on-demand usage
4
Recommend commitment discounts
5
Right-size over-provisioned instances
6
Negotiate with vendors based on usage data
7
Track savings and ROI monthly
Required Dependencies
Cost Management & OptimizationCloudHealth, Cloudability, Apptio, Flexera
Key Performance Indicators
Click any KPI to view detailed measurement guidance, formulas, and typical ranges.
Governance Controls
Centralized LoggingVisibility
HIGH
Centralized Logging
Capture all agent interactions (prompts, outputs, data sources accessed) in a central, searchable system
Complexity: medium
Agent RegistryVisibility
HIGH
Agent Registry
Central inventory of all agents with metadata: owner, purpose, data sources, risk level, users
Complexity: low
Automated Policy EnforcementControl
HIGH
Automated Policy Enforcement
Programmatically block prohibited actions (e.g., uploading PII to external models, accessing restricted data)
Complexity: high
Agent Kill SwitchIncident Response
HIGH
Agent Kill Switch
Ability to instantly disable any agent in case of security incident, data leak, or policy violation
Complexity: low
Prompt Injection TestingRisk
Prompt Injection Testing
Regularly test agents for vulnerabilities (jailbreaks, prompt injection, data exfiltration attempts)
Complexity: medium
These controls help ensure secure, compliant, and auditable AI operations. High-priority controls are critical for production deployment.
Identified AI Risks
Hallucinations
Hallucinations
AI generating false or fabricated information presented as fact
Stale Information
Stale Information
AI using outdated data that no longer reflects current reality
Source Attribution
Source Attribution
Inability to verify or cite the original sources of AI-generated information
Unauthorized Data Access
Unauthorized Data Access
Users accessing data or performing actions beyond their permission level
Prompt Injection
Prompt Injection
Malicious manipulation of AI behavior through crafted input prompts
Data Leakage
Data Leakage
Unintentional exposure of sensitive data through model training or outputs
These risks should be mitigated through proper governance controls and operational procedures.
More IT Use Cases
Access Request Approval
Validate identity, check policy, and grant or deny access requests automatically
Learn more
AI Governance & Audit
Monitor AI systems for bias, compliance, data privacy, and regulatory adherence with automated audit trails
Learn more
Intelligent Application Deployment & Release Automation
Orchestrate multi-environment deployments, validate compatibility, coordinate rollbacks, and manage release risks
Learn more
Related AI Tools
Explore assistive AI tools that IT teams use to augment these agentic workflows.
Deploying AI agents in IT? Olakai gives you real-time monitoring, cost tracking, and governance across every agent in your stack.
Schedule a Demo