| name | Multi-Cloud Cost Optimizer |
| slug | finops-multicloud-optimizer |
| description | Optimize costs across AWS, GCP, Azure with cross-cloud waste detection, workload placement, commitment balancing, and unified FinOps. |
| capabilities | ["Cross-cloud cost normalization and comparison (AWS + GCP + Azure)","Multi-cloud waste detection (duplicate resources, unused cross-cloud connectivity)","Workload placement optimization based on cost differentials","Commitment optimization across providers (RIs, SPs, CUDs balance)","Cross-cloud tagging compliance and unified cost allocation","Egress cost optimization (identify expensive inter-cloud data transfer)","Multi-cloud FinOps maturity assessment"] |
| inputs | ["Cloud accounts array (provider, account_id, billing API access) for AWS, GCP, Azure","Optimization scope (all, compute, storage, network, data-transfer)","Business constraints (critical workloads, compliance, migration flexibility)","Time range (30d, 90d, 180d)","Cost allocation model (showback, chargeback, unified)"] |
| outputs | ["Unified cost report with spend by provider and savings potential","Workload placement recommendations with migration ROI","Commitment balance plan across all cloud providers","Cross-cloud waste inventory with remediation actions","Prioritized action plan with effort and impact estimates"] |
| keywords | ["multi-cloud cost optimization","cross-cloud finops","workload placement","commitment optimization","cloud cost arbitrage","multi-cloud waste detection","unified cost allocation","egress cost optimization"] |
| version | 1.0.0 |
| owner | cognitive-toolworks |
| license | MIT |
| security | ["Read-only access to billing APIs across all cloud providers","Secure aggregation of cost data (contains sensitive business intelligence)","No automated resource migration without approval","Audit logging of all cross-cloud recommendations"] |
| links | ["https://www.finops.org/framework/","https://www.cloudzero.com/blog/finops-best-practices/","https://www.prosperops.com/blog/multi-cloud-cost-management-guide/","https://holori.com/20-best-finops-and-cloud-cost-management-tools-in-2025/"] |
Purpose & When-To-Use
Primary trigger conditions:
- Operating workloads across 2+ cloud providers (AWS, GCP, Azure) with monthly spend >$50k
- Seeking cost arbitrage opportunities by placing workloads on most cost-effective cloud
- Need unified view of waste and optimization opportunities across all clouds
- Balancing commitment purchases (RIs, Savings Plans, CUDs) across multiple providers
- High egress costs (>15% of total spend) from cross-cloud data transfer
- Executive request for multi-cloud cost consolidation and reduction
- FinOps team managing multiple cloud providers seeking unified optimization
When NOT to use this skill:
- Single cloud deployment → use finops-cost-analyzer instead
- Multi-cloud strategic planning phase → use cloud-multicloud-advisor first
- Real-time cost tracking → use native cloud dashboards
- Workloads cannot be migrated due to compliance/latency constraints
Value proposition: Identifies 20-35% additional savings beyond single-cloud optimization by leveraging cross-cloud price competition, workload placement optimization, and eliminating multi-cloud waste patterns. Organizations using multi-cloud cost optimization tools achieve 35-68% total cost reductions (CloudZero, accessed 2025-10-26T14:30:00-04:00).
Pre-Checks
Required inputs validation:
NOW_ET = "2025-10-26T14:30:00-04:00"
assert len(cloud_accounts) >= 2, "Multi-cloud optimization requires ≥2 cloud providers"
assert all(acc["billing_api_access"] for acc in cloud_accounts), "Billing API access required for all accounts"
assert time_range in ["30d", "90d", "180d"], "Valid time ranges: 30d, 90d, 180d"
assert optimization_scope in ["all", "compute", "storage", "network", "data-transfer"]
for account in cloud_accounts:
if account["last_billing_sync"] > 48h:
warn(f"{account['provider']} billing data stale; recommendations may be outdated")
total_monthly_spend = sum_monthly_spend(cloud_accounts)
if total_monthly_spend < 50000:
suggest("Multi-cloud optimization most valuable for monthly spend >$50k")
Authority checks:
- AWS: Cost Explorer API enabled,
ce:GetCostAndUsage, organizations:ListAccounts if using AWS Organizations
- GCP: Cloud Billing API enabled,
billing.accounts.get, billing.resourceCosts.list permissions
- Azure: Cost Management API access, Reader role on subscriptions,
Microsoft.CostManagement/query/action permission
Source citations (accessed 2025-10-26T14:30:00-04:00):
Procedure
Tier 1 (≤2k tokens): Quick Multi-Cloud Cost Health Check
Goal: Identify top 3 cross-cloud optimization opportunities in <5 minutes.
Steps:
-
Fetch unified cost summary for time_range across all providers
- Normalize currency and time periods (AWS monthly, GCP daily, Azure daily → unified monthly)
- Calculate total spend by provider and trend (% change from previous period)
- Identify largest cost contributors by service category (compute, storage, network)
-
Quick cross-cloud waste scan
- Duplicate resources: Same workload/data running on multiple clouds (accidental redundancy)
- Unused cross-cloud connectivity: VPN tunnels, Direct Connect/ExpressRoute/Interconnect with zero traffic (last 30 days)
- Orphaned cross-cloud resources: Load balancers, NAT gateways pointing to deleted resources
- Commitment under-utilization: RIs/SPs/CUDs with <70% utilization across all clouds
-
Cross-cloud price comparison (same workload on different clouds)
- Identify 5 largest compute workloads
- Calculate equivalent cost on each cloud (normalize instance types: AWS m5.xlarge ≈ GCP n2-standard-4 ≈ Azure D4s v3)
- Flag workloads with >20% cost differential for placement optimization
-
Output quick wins (3 highest impact items)
- Example: "Migrate analytics workload from AWS Redshift to GCP BigQuery → save $3,200/month (55% reduction)"
- Example: "Delete 6 unused AWS Direct Connect + Azure ExpressRoute connections → save $1,800/month"
- Example: "Rebalance commitments: reduce AWS RI, increase GCP CUD → save $2,400/month"
Token budget checkpoint: ~1.8k tokens for API calls, normalization, analysis, output formatting.
Tier 2 (≤6k tokens): Comprehensive Multi-Cloud Cost Optimization
Goal: Generate detailed cross-cloud optimization plan with quantified savings and migration recommendations.
Extends T1 with:
-
Cross-cloud workload placement analysis
- Fetch detailed resource inventory (compute, database, storage) from all clouds
- Calculate unit economics per cloud (cost per vCPU-hour, cost per GB storage, cost per 1M requests)
- Identify migration candidates (workloads without hard dependencies on current cloud):
- No compliance restrictions (data residency, FedRAMP, etc.)
- No vendor-specific services (avoid migrating from Aurora/BigQuery/Cosmos DB)
- Latency tolerance >50ms (can tolerate cross-region placement)
- Calculate migration cost vs savings ROI:
- Migration cost: data transfer (egress) + downtime + testing
- Annual savings: (current_cloud_cost - target_cloud_cost) × 12
- ROI = annual_savings / migration_cost (recommend if ROI >3x)
Example calculation (accessed 2025-10-26T14:30:00-04:00):
Workload: 500TB PostgreSQL database + 50 vCPU app tier
Current: AWS RDS Aurora PostgreSQL $12,000/month, EC2 m5.4xlarge reserved $1,500/month
Target: GCP Cloud SQL PostgreSQL $7,200/month, n2-standard-16 CUD $900/month
Monthly savings: $5,400/month
Migration cost: 500TB egress ($45,000) + 2 weeks downtime ($10,000) = $55,000
Annual savings: $64,800
ROI: $64,800 / $55,000 = 1.18x → recommend if strategic, defer if purely financial
-
Commitment optimization across clouds
- Analyze commitment coverage across all providers:
- AWS: Reserved Instances + Compute/EC2 Savings Plans coverage
- GCP: Committed Use Discounts (resource-based and spend-based)
- Azure: Reserved VM Instances + Azure Hybrid Benefit
- Calculate blended commitment rate (weighted average discount across clouds)
- Identify under-committed clouds (on-demand spend >50%) and over-committed clouds (RI/CUD utilization <80%)
- Recommend commitment rebalancing:
- Reduce commitments on expensive/declining clouds
- Increase commitments on cost-effective/growing clouds
- Target: 70-85% commitment coverage across all clouds (sweet spot)
Sources (accessed 2025-10-26T14:30:00-04:00):
-
Egress and data transfer cost optimization
- Map cross-cloud data flows (AWS → GCP, Azure → AWS, etc.)
- Calculate egress costs by route:
- Same-region cross-cloud: typically highest ($0.08-0.12/GB)
- Cross-region same-cloud: medium ($0.01-0.02/GB)
- Cloud → internet → cloud (via CDN): varies
- Recommend egress reduction strategies:
- Colocation: Place communicating services in same cloud
- Caching: Use CloudFront/Cloud CDN/Azure CDN to reduce origin fetches
- Compression: Enable gzip/brotli for API responses
- Direct peering: Use AWS Direct Connect + GCP Interconnect partner connections (not public internet)
Egress cost examples (accessed 2025-10-26T14:30:00-04:00):
- AWS to internet: $0.09/GB first 10TB, $0.085/GB next 40TB
- GCP to internet: $0.12/GB first 1TB, $0.11/GB next 9TB
- Azure to internet: $0.087/GB first 5GB
-
Cross-cloud tagging compliance and cost allocation
- Audit tagging across all clouds using unified tag schema (environment, team, cost-center, project)
- Calculate tag compliance rate per cloud (% resources with required tags)
- Identify untagged cost allocation gaps (spend that cannot be attributed to teams/projects)
- Recommend standardized tagging policy across AWS/GCP/Azure (harmonize tag keys)
-
Multi-cloud FinOps maturity assessment
- Evaluate FinOps maturity across dimensions:
- Visibility: Single dashboard for all clouds vs siloed per-cloud tools
- Optimization: Automated vs manual optimization across clouds
- Governance: Unified policies vs per-cloud inconsistency
- Culture: Cross-functional FinOps team vs isolated cloud admins
- Assign maturity score: Crawl (0-3), Walk (4-6), Run (7-10)
- Recommend next steps to improve maturity (e.g., "Implement unified tagging → +2 maturity points")
-
Generate comprehensive report
- Executive summary: Total multi-cloud spend, waste identified, savings potential
- Cost breakdown by cloud: AWS $X, GCP $Y, Azure $Z with trends
- Cross-cloud opportunities: Workload placement (top 10), commitment rebalancing, egress optimization
- Action plan: Prioritized by ROI (savings/effort) with owner assignments
Authority sources (accessed 2025-10-26T14:30:00-04:00):
Output: JSON report with sections: unified_cost_summary, cross_cloud_waste (T1), workload_placement_recommendations, commitment_balance_plan, egress_optimization, tagging_compliance, finops_maturity_score, prioritized_action_plan.
Token budget checkpoint: ~5.5k tokens (includes T1 + extended multi-cloud analysis + detailed outputs).
T3: Enterprise Multi-Cloud Optimization (≤12k tokens)
Goal: Deep financial modeling, predictive forecasting, and custom multi-cloud optimization strategies for >$1M annual spend.
Extends T2 with:
-
Predictive cost forecasting
- Machine learning models trained on historical spend patterns (6+ months data)
- Forecast next 12 months spend by cloud, service, and team
- Identify seasonal patterns (e.g., Q4 spike, weekend drop-off)
- Alert on forecast anomalies (>15% deviation from expected)
-
Custom commitment optimization algorithms
- Optimize commitment portfolio across clouds using linear programming
- Constraints: budget limits, risk tolerance, workload volatility
- Objective function: maximize total discount percentage across all clouds
- Account for commitment term trade-offs (1-year flexibility vs 3-year deeper discounts)
-
Multi-cloud vendor negotiation intelligence
- Aggregate total spend across clouds to strengthen negotiation position
- Benchmark against similar-sized organizations (anonymized peer data)
- Identify Private Pricing Agreement (PPA) opportunities with AWS/GCP/Azure
- Calculate Enterprise Discount Program (EDP) eligibility and potential savings
-
Sustainability and carbon cost optimization
- Map cloud regions to carbon intensity (gCO2/kWh)
- Calculate carbon footprint by cloud and workload
- Recommend low-carbon region placement (GCP Iowa vs AWS Virginia)
- Integrate carbon costs into TCO (emerging regulatory requirement)
-
Multi-account/multi-org consolidation
- AWS: Consolidate billing across AWS Organizations (50+ accounts)
- GCP: Aggregate billing across multiple billing accounts
- Azure: Unified cost view across subscriptions and management groups
- Enable volume discounts and cross-account commitment sharing
Authority sources (accessed 2025-10-26T14:30:00-04:00):
Output: Full enterprise-grade multi-cloud financial optimization plan including forecasts, custom commitment strategies, vendor negotiation playbook, sustainability metrics, and multi-account consolidation roadmap.
Token budget checkpoint: ~11k tokens (includes T1 + T2 + enterprise-grade analysis).
Decision Rules
When to abort:
- Billing API access fails for any cloud → insufficient permissions; emit setup instructions per cloud
- Cost data <30 days → insufficient for trend analysis; wait for more data
- Migration restrictions block all workload placement → report "no cross-cloud opportunities"
Ambiguity thresholds:
- Workload placement confidence: Only recommend migration if:
- Cost differential >20% AND annual savings >$10k (avoid noise)
- No hard compliance/latency constraints
- ROI >2x (conservative threshold; adjust to 3x for risk-averse orgs)
- Commitment rebalancing: Recommend only if:
- Current utilization <80% (under-utilized) OR coverage <60% (under-committed)
- Rebalance would improve blended discount rate by ≥5 percentage points
- Egress optimization: Flag only if egress costs >10% of total spend OR >$5k/month absolute
Prioritization logic:
- ROI-based ranking:
(annual_savings / implementation_effort_cost) descending
- Effort scale: Low (delete unused) < Medium (commitment rebalance) < High (workload migration)
- Quick wins first: Zero-downtime, zero-risk changes (delete unused cross-cloud connections) rank highest
- Strategic alignment: If business strategy favors specific cloud (e.g., AWS for ML), deprioritize migration away from it
FinOps principle application (accessed 2025-10-26T14:30:00-04:00):
Per FinOps Foundation principles (https://www.finops.org/framework/principles/):
- "Teams collaborate": Multi-cloud optimization requires cross-team coordination (cloud admins, finance, engineering)
- "Decisions are data-driven": All recommendations backed by normalized cost data across clouds
- "Take advantage of variable cost model": Leverage spot instances, preemptible VMs, and commitment flexibility across clouds
Output Contract
Schema (JSON):
{
"unified_cost_report": {
"period": "2025-09-26 to 2025-10-26",
"total_spend": 245000.00,
"breakdown_by_cloud": {
"aws": {"spend": 125000.00, "percentage": 51.0, "trend": "+5%"},
"gcp": {"spend": 80000.00, "percentage": 32.7, "trend": "-2%"},
"azure": {"spend": 40000.00, "percentage": 16.3, "trend": "+8%"}
},
"waste_identified": 68000.00,
"savings_potential": {
"monthly": 52000.00,
"annual": 624000.00,
"percentage": 21.2
}
},
"workload_placement_recommendations": [
{
"workload_id": "analytics-cluster-01",
"current_cloud": "aws",
"current_cost_monthly": 12000.00,
"recommended_cloud": "gcp",
"recommended_cost_monthly": 6800.00,
"monthly_savings": 5200.00,
"annual_savings": 62400.00,
"migration_cost": 55000.00,
"roi": 1.13,
"rationale": "BigQuery vs Redshift cost advantage for analytics workload"
}
],
"commitment_balance_plan": {
"current_coverage_rate": 58.0,
"target_coverage_rate": 75.0,
"current_blended_discount": 28.0,
"target_blended_discount": 42.0,
"recommendations": [
{
"cloud": "aws",
"action": "reduce",
"current_commitment_monthly": 60000.00,
"recommended_commitment_monthly": 48000.00,
"rationale": "RI utilization at 68%, under-utilized"
},
{
"cloud": "gcp",
"action": "increase",
"current_commitment_monthly": 15000.00,
"recommended_commitment_monthly": 32000.00,
"rationale": "On-demand spend at 72%, opportunity for 70% CUD savings"
}
]
},
"cross_cloud_waste_inventory": [
{
"waste_type": "unused_cross_cloud_vpn",
"resources": [
{"provider": "aws", "resource_id": "vpn-0a1b2c3d", "idle_days": 60},
{"provider": "azure", "resource_id": "vpn-xyz789", "idle_days": 60}
],
"monthly_cost": 1800.00
},
{
"waste_type": "duplicate_backup_storage",
"resources": [
{"provider": "aws", "resource_id": "s3://backups-prod", "size_tb": 50},
{"provider": "gcp", "resource_id": "gs://backups-prod", "size_tb": 50}
],
"monthly_cost": 2300.00
}
],
"action_plan": [
{
"priority": 1,
"action": "Delete unused cross-cloud VPN connections",
"impact": "medium",
"effort": "low",
"monthly_savings": 1800.00,
"owner": "cloud-networking-team"
},
{
"priority": 2,
"action": "Rebalance commitments (reduce AWS RI, increase GCP CUD)",
"impact": "high",
"effort": "medium",
"monthly_savings": 8400.00,
"owner": "finops-team"
}
]
}
Required fields: unified_cost_report (with breakdown_by_cloud, savings_potential), action_plan (prioritized).
Optional fields: workload_placement_recommendations, commitment_balance_plan (only if applicable based on business_constraints).
Examples
input: {scope: all, time_range: 90d, model: chargeback}
output:
total_spend: $245k, waste: $68k (28%), savings: $52k/mo
workload_placement:
- analytics: AWS Redshift $12k → GCP BigQuery $6.8k (save $5.2k/mo)
cross_cloud_waste:
- unused VPN (AWS+Azure): $1.8k/mo
- duplicate backups (AWS+GCP): $2.3k/mo
commitment_rebalance:
AWS RI: $60k → $48k/mo (reduce)
GCP CUD: $15k → $32k/mo (increase)
action_plan:
1. Delete unused VPN (LOW effort) → $1.8k/mo
2. Consolidate backups (LOW effort) → $2.3k/mo
3. Rebalance commitments (MED effort) → $8.4k/mo
4. Migrate analytics (HIGH effort, ROI 1.13x) → $5.2k/mo
Quality Gates
Token budgets (enforced):
- T1: ≤2,000 tokens - quick multi-cloud health check with top 3 cross-cloud opportunities
- T2: ≤6,000 tokens - comprehensive multi-cloud optimization with workload placement, commitment rebalancing, egress optimization, and unified FinOps analytics
- T3: ≤12,000 tokens - enterprise-grade optimization with ML forecasting, custom commitment algorithms, vendor negotiation intelligence, sustainability metrics
Accuracy requirements:
- Cost normalization must account for currency (USD/EUR/GBP) and time period differences
- Cross-cloud price comparisons validated against official pricing APIs (accessed on NOW_ET)
- Workload placement ROI calculations include migration costs (egress, downtime, testing)
Safety constraints:
- No automatic workload migration: All cross-cloud moves require manual approval and testing
- Compliance checks: Flag workloads with data residency/sovereignty requirements before recommending migration
- Commitment purchase limits: Never recommend commitments exceeding 85% coverage (maintain flexibility)
Auditability:
- Cite pricing source for all cost calculations (AWS Pricing API, GCP Cloud Billing, Azure Rate Card)
- Document assumptions in workload placement (instance type equivalence, network latency tolerance)
- Record baseline metrics for each cloud at analysis time
Determinism:
- Same inputs + same cost data → same recommendations
- Configurable thresholds (ROI minimum, egress cost %, commitment coverage targets)
Resources
Official cloud provider documentation:
FinOps Foundation resources:
Multi-cloud cost optimization guides:
Related skills:
finops-cost-analyzer: For single-cloud cost optimization (invoke before multi-cloud aggregation)cloud-multicloud-advisor: For strategic multi-cloud architecture design (invoke before deployment)cloud-provider-advisor: For initial cloud provider selection (invoke during planning phase)
