Cloud Provider Selection Advisor

name: "Cloud Provider Selection Advisor"
slug: "cloud-provider-advisor"
description: "Compare AWS, GCP, and Azure to select the best cloud provider based on workload requirements, cost, compliance, and migration complexity."
capabilities:
- Service comparison matrix (compute, storage, database, networking)
- Cost model analysis (pricing, discounts, commitments across providers)
- Regional coverage and compliance assessment (FedRAMP, HIPAA, ISO 27001)
- Migration complexity evaluation (current state → target cloud)
- Multi-cloud and hybrid strategy recommendations
- Vendor lock-in risk assessment
- Ecosystem maturity comparison (tooling, SDKs, community)
inputs:
- requirements: "functional and non-functional requirements (object with performance, security, cost, compliance)"
- workload_type: "web-app, data-processing, real-time, batch, machine-learning, hybrid (string)"
- current_state: "existing infrastructure (on-premises, AWS, GCP, Azure, multi-cloud) (string, optional)"
- priorities: "ranked list of decision factors (cost, performance, compliance, ecosystem, migration-ease) (array)"
- constraints: "hard requirements (region, compliance, vendor preferences) (object, optional)"
outputs:
- recommendation: "primary cloud provider with detailed rationale"
- service_mapping: "equivalent services across AWS/GCP/Azure for workload"
- cost_comparison: "estimated costs across providers with discount strategies"
- migration_assessment: "migration complexity and timeline if applicable"
- multi_cloud_strategy: "when and how to use multiple providers (if applicable)"
keywords:
- cloud-comparison
- aws
- gcp
- azure
- cloud-selection
- multi-cloud
- vendor-comparison
- cost-comparison
- migration-planning
version: "1.0.0"
owner: "cognitive-toolworks"
license: "MIT"
security: "Public; no secrets or PII; safe for open repositories"
links:
- https://aws.amazon.com/
- https://cloud.google.com/
- https://azure.microsoft.com/
- https://www.gartner.com/en/documents/cloud-infrastructure-services

Purpose & When-To-Use

Trigger conditions:
- Choosing cloud provider for new project or workload
- Evaluating migration from on-premises to cloud
- Switching cloud providers for cost or feature reasons
- Designing multi-cloud or hybrid cloud strategy
- Comparing specific services across AWS, GCP, Azure
- Assessing vendor lock-in risk and mitigation strategies
- Compliance-driven cloud selection (FedRAMP, HIPAA, ISO 27001)

Not for:
- Deep architecture design within a single cloud (use cloud-aws-architect, cloud-gcp-architect, cloud-azure-architect)
- Detailed cost optimization within one provider (use finops-cost-analyzer)
- Kubernetes-only deployment (cloud-agnostic, use cloud-kubernetes-integrator)
- Edge computing or CDN-specific decisions (use cloud-edge-architect)

Pre-Checks

Time normalization:
- Compute NOW_ET using NIST/time.gov semantics (America/New_York, ISO-8601): 2025-10-26T18:00:00-04:00
- Use NOW_ET for all citation access dates

Input validation:
- requirements must include at least one priority: cost, performance, security, compliance
- workload_type must be one of: web-app, data-processing, real-time, batch, machine-learning, hybrid
- priorities must be ranked list (highest priority first)
- constraints if provided must specify hard requirements (e.g., "must support eu-west-1", "must have FedRAMP High")

Source freshness:
- AWS Well-Architected Framework (accessed 2025-10-26T18:00:00-04:00): https://aws.amazon.com/architecture/well-architected/
- GCP Architecture Framework (accessed 2025-10-26T18:00:00-04:00): https://cloud.google.com/architecture/framework
- Azure Well-Architected Framework (accessed 2025-10-26T18:00:00-04:00): https://learn.microsoft.com/en-us/azure/well-architected/
- Gartner Magic Quadrant for Cloud Infrastructure (accessed 2025-10-26T18:00:00-04:00): https://www.gartner.com/en/documents/cloud-infrastructure-services

Procedure

T1: Quick Provider Recommendation (≤2k tokens)

Fast path for 80% of cloud selection decisions:

1. Service equivalence mapping:

1. Cost model comparison:

AWS:
- Pricing: On-demand, Savings Plans (1-3 year), Spot (up to 90% discount)
- Free tier: 12 months (EC2 t2.micro 750hrs, S3 5GB, RDS 750hrs)
- Billing: Per-second (EC2/Fargate), per-request (Lambda), per-GB-month (S3)

GCP:
- Pricing: On-demand, Committed Use Discounts (1-3 year, up to 70%), Spot VMs (60-91% discount)
- Free tier: Always free (Cloud Run 2M requests, Cloud Functions 2M invocations, Compute Engine e2-micro)
- Billing: Per-second (all compute), sustained use discounts (automatic 20-30% for consistent usage)

Azure:
- Pricing: On-demand, Reserved Instances (1-3 year, up to 72%), Spot VMs (variable)
- Free tier: 12 months (VMs B1S 750hrs, Blob Storage 5GB, SQL Database 250GB)
- Billing: Per-second (VMs), per-execution (Functions), per-DTU/vCore (SQL Database)
- Azure Hybrid Benefit: Up to 80% savings with Windows Server/SQL Server licenses

1. Quick decision criteria:

Choose AWS if:
- Broadest service catalog (200+ services)
- Largest global footprint (33 regions, 105 availability zones)
- Mature ecosystem and largest market share (32% global market)
- Enterprise-grade compliance (FedRAMP High, DoD IL5, HIPAA)
- Strong serverless capabilities (Lambda, Step Functions, EventBridge)

Choose GCP if:
- Data analytics and machine learning focus (BigQuery, Vertex AI, TensorFlow)
- Kubernetes-native workloads (GKE is most mature managed K8s)
- Automatic cost optimization (sustained use discounts without commitment)
- Developer-friendly APIs and tooling (gcloud CLI, Cloud Shell)
- Open-source and multi-cloud compatibility (Anthos, Terraform)

Choose Azure if:
- Microsoft ecosystem integration (Active Directory, Office 365, Dynamics 365)
- Hybrid cloud requirements (Azure Arc, Azure Stack)
- Windows Server and SQL Server workloads (Azure Hybrid Benefit)
- Enterprise agreements and licensing flexibility
- Strong regional presence in Europe and government clouds

1. Migration complexity assessment:

From on-premises:
- AWS: AWS Migration Hub, Database Migration Service (DMS), Server Migration Service
- GCP: Migrate to Virtual Machines, Database Migration Service, Transfer Service
- Azure: Azure Migrate, Database Migration Service, Azure Site Recovery

From AWS to GCP/Azure:
- Complexity: Medium to High (service mapping, IAM translation, networking redesign)
- Timeline: 3-6 months for small apps, 12-24 months for large enterprises
- Tools: CloudEndure, Velostrata, third-party migration platforms

From GCP to AWS/Azure:
- Complexity: Medium (fewer proprietary services, Kubernetes portability)
- Timeline: 3-6 months for containerized apps, 6-12 months for GCP-native services
- Tools: Terraform for IaC translation, Kubernetes manifest migration

1. Output (T1):
2. Primary cloud provider recommendation with 3-5 bullet justification
3. Service mapping table for workload
4. Rough monthly cost estimate across providers (±30% accuracy)
5. Migration complexity rating (Low/Medium/High) if applicable
6. Vendor lock-in assessment and mitigation strategies

Abort conditions:
- Requirements conflict (e.g., "cheapest possible + must use AWS")
- Insufficient workload details to map services
- Specialized requirements needing vendor consultation (e.g., quantum computing, specialized hardware)

T2: Detailed Multi-Cloud Strategy (≤6k tokens)

For complex cloud selection with detailed comparison:

1. All T1 steps plus:

2. Comprehensive service comparison:

Compute tier:
- Serverless: AWS Lambda (15min timeout) vs GCP Cloud Functions (9min) vs Azure Functions (10min)
- Containers: AWS ECS/Fargate (AWS-specific) vs GCP Cloud Run (serverless, Knative) vs Azure Container Apps (Dapr integration)
- Kubernetes: AWS EKS vs GCP GKE (most mature, Autopilot mode) vs Azure AKS (Azure AD integration)
- VMs: AWS EC2 (broadest instance types) vs GCP Compute Engine (custom machine types) vs Azure VMs (Azure Hybrid Benefit)

Storage tier:
- Object Storage: AWS S3 (industry standard, 11 9s durability) vs GCP Cloud Storage (uniform API, multi-region) vs Azure Blob Storage (Hot/Cool/Archive tiers)
- Block Storage: AWS EBS (io2 Block Express 256K IOPS) vs GCP Persistent Disk (flexible sizing) vs Azure Managed Disks (Ultra Disk 160K IOPS)
- File Storage: AWS EFS (NFS) vs GCP Filestore (NFS/SMB) vs Azure Files (SMB, AD integration)

Database tier:
- Relational: AWS Aurora (MySQL/PostgreSQL compatible, 5x perf) vs GCP Cloud SQL (managed PostgreSQL/MySQL) vs Azure SQL Database (SQL Server compatibility)
- NoSQL: AWS DynamoDB (key-value, single-digit ms) vs GCP Firestore (document, real-time) vs Azure Cosmos DB (multi-model, 5 consistency levels)
- Global: AWS DynamoDB Global Tables vs GCP Cloud Spanner (strong consistency) vs Azure Cosmos DB (turnkey global distribution)
- Analytics: AWS Redshift (columnar, Spectrum) vs GCP BigQuery (serverless, petabyte-scale) vs Azure Synapse Analytics (unified analytics)

1. Cost comparison deep-dive:

Compute cost example (4 vCPU, 16GB RAM, 730 hrs/month):
- AWS: EC2 m5.xlarge on-demand $140/mo, 1-yr Savings Plan $91/mo (35% savings), Spot $28/mo (80% savings)
- GCP: n2-standard-4 on-demand $122/mo, 1-yr CUD $73/mo (40% savings), Spot VMs $24-$49/mo (60-80% savings)
- Azure: Standard D4s v3 on-demand $140/mo, 1-yr Reserved $91/mo (35% savings), Spot $28-$70/mo (variable)

Storage cost example (1TB object storage, 100K requests/month):
- AWS: S3 Standard $23/mo + requests $0.40 = $23.40/mo, Glacier $4/mo (archival)
- GCP: Cloud Storage Standard $20/mo + requests $0.40 = $20.40/mo, Archive $1.20/mo
- Azure: Blob Hot $18/mo + requests $0.44 = $18.44/mo, Archive $1/mo

Data transfer costs (100GB outbound/month):
- AWS: $9/100GB (first 10TB)
- GCP: $12/100GB (first 1TB)
- Azure: $8.70/100GB (first 10TB)

1. Compliance and regional coverage:

FedRAMP:
- AWS: FedRAMP High (us-gov-east-1, us-gov-west-1)
- GCP: FedRAMP High (us-east4, us-west2)
- Azure: FedRAMP High (Government regions: usgovvirginia, usgovtexas)

HIPAA:
- AWS: Business Associate Agreement (BAA) available for most services
- GCP: HIPAA-compliant with BAA, best for healthcare analytics (BigQuery)
- Azure: HIPAA/HITECH compliant, strong healthcare integration (HL7 FHIR)

Data residency:
- AWS: 33 regions globally, most granular region control
- GCP: 40 regions, strong presence in Asia-Pacific
- Azure: 60+ regions, strongest European presence (GDPR compliance)

1. Ecosystem and tooling:

Developer experience:
- AWS: aws-cli (comprehensive), CloudFormation (YAML/JSON), CDK (TypeScript/Python)
- GCP: gcloud CLI (intuitive), Terraform (GCP preference), Deployment Manager
- Azure: az CLI, ARM templates (JSON), Bicep (DSL), Azure PowerShell

CI/CD integration:
- AWS: CodePipeline, CodeBuild, CodeDeploy (AWS-specific)
- GCP: Cloud Build, Cloud Deploy (GKE-native)
- Azure: Azure DevOps (strongest integration with GitHub, Microsoft stack)

Monitoring and observability:
- AWS: CloudWatch (comprehensive), X-Ray (tracing), Cost Explorer
- GCP: Cloud Monitoring (Stackdriver), Cloud Trace, Cloud Profiler
- Azure: Azure Monitor, Application Insights (best APM), Log Analytics

1. Multi-cloud strategy recommendations:

When to use multi-cloud:
- Avoid vendor lock-in (distribute workloads across AWS, GCP, Azure)
- Leverage best-of-breed services (BigQuery on GCP, Lambda on AWS, Azure AD on Azure)
- Compliance requirements (data residency in multiple jurisdictions)
- Disaster recovery and business continuity (cross-cloud failover)
- Mergers and acquisitions (consolidate different cloud footprints)

Multi-cloud patterns:
- Best-of-breed: Use AWS Lambda, GCP BigQuery, Azure AD (manage via Terraform, Pulumi)
- Primary + DR: AWS primary, Azure secondary (cross-cloud replication, Route 53/Traffic Manager failover)
- Data distribution: Data processing in GCP (BigQuery), application in AWS (ECS), identity in Azure (AD)
- Geographic: AWS in US, GCP in APAC, Azure in Europe (latency optimization)

Multi-cloud challenges:
- Complexity: Multiple IaC tools, separate monitoring, cross-cloud networking
- Cost: Egress charges (AWS→GCP: $0.02-0.09/GB, AWS→Azure: similar)
- Skillset: Team needs expertise in multiple clouds
- Support: Separate support contracts, fragmented troubleshooting

1. Vendor lock-in mitigation:

High lock-in risk:
- AWS: DynamoDB (proprietary NoSQL), Step Functions (workflow orchestration), SageMaker (ML platform)
- GCP: BigQuery (analytics), Firestore (NoSQL), Vertex AI (ML platform)
- Azure: Cosmos DB (multi-model NoSQL), Azure AD (identity), Synapse Analytics

Low lock-in risk:
- Kubernetes (portable across EKS, GKE, AKS)
- PostgreSQL/MySQL (RDS, Cloud SQL, Azure Database)
- Object storage (S3 API compatible with Cloud Storage, Blob Storage)
- Terraform/Pulumi for IaC (cloud-agnostic)

Mitigation strategies:
- Use open-source databases (PostgreSQL, MySQL, MongoDB, Cassandra)
- Containerize applications (Docker, Kubernetes)
- Avoid proprietary services (DynamoDB → PostgreSQL, BigQuery → ClickHouse)
- Abstract cloud services with APIs (use CDN abstraction layer instead of CloudFront/Cloud CDN/Front Door directly)

1. Output (T2):
2. Detailed cloud provider recommendation with comprehensive justification
3. Service-by-service comparison table with pros/cons
4. Accurate monthly cost estimate (±10% accuracy) across providers
5. Migration plan with timeline, phases, and tools (if applicable)
6. Multi-cloud strategy with specific architecture patterns (if applicable)
7. Vendor lock-in risk matrix with mitigation tactics
8. Next steps: contact sales, set up pilot, begin migration planning

Abort conditions:
- Proprietary hardware or software requirements (e.g., mainframe, specialized GPU)
- Geopolitical constraints (e.g., data sovereignty laws prohibiting certain clouds)
- Extreme scale requirements needing vendor-specific optimization (>1PB data, >100K req/sec)

T3: Not Implemented

Note: This skill implements T1 (quick recommendations) and T2 (detailed comparison with multi-cloud strategy) tiers only. T2 provides comprehensive cloud provider comparison with service-by-service analysis, cost breakdowns, migration planning, and multi-cloud architecture patterns. For highly specialized scenarios requiring deeper vendor consultation (custom pricing negotiations, enterprise agreements, dedicated support contracts), engage directly with AWS, GCP, or Azure sales teams.

Future T3 considerations:
- Enterprise agreement negotiation strategies across providers
- Custom pricing analysis for >$1M/year cloud spend
- Dedicated support contract comparison (AWS Enterprise Support, GCP Premium Support, Azure Premier Support)
- Private cloud integration (AWS Outposts, Google Distributed Cloud, Azure Stack)
- Specialized compliance frameworks (ITAR, CUI, classified workloads)
- Quantum computing and emerging technology roadmaps

Decision Rules

Primary cloud selection:

AWS if:
- Broadest service catalog needed (AI/ML, IoT, blockchain, quantum)
- Largest ecosystem and community support
- Enterprise compliance (FedRAMP High, DoD IL5)
- Existing AWS expertise or investment
- Serverless-first architecture

GCP if:
- Data analytics and BigQuery use case (petabyte-scale analytics)
- Kubernetes-native workloads (GKE Autopilot)
- Machine learning with TensorFlow/Vertex AI
- Open-source preference (Knative, Istio, Anthos)
- Automatic cost optimization (sustained use discounts)

Azure if:
- Microsoft ecosystem (Windows Server, SQL Server, Active Directory, Office 365)
- Hybrid cloud requirements (Azure Arc, Azure Stack)
- Enterprise agreement and licensing flexibility
- Strongest regional presence in Europe
- .NET or Microsoft-stack development

Multi-cloud if:
- Avoid vendor lock-in (distribute risk across providers)
- Best-of-breed services (BigQuery + Lambda + Azure AD)
- Compliance requires multiple providers (data residency)
- Mergers and acquisitions (legacy multi-cloud)

Cost priority:
1. GCP (sustained use discounts automatic, competitive pricing)
2. Azure (Reserved Instances + Hybrid Benefit can be cheapest for Windows/SQL)
3. AWS (Savings Plans flexible, but requires commitment planning)

Performance priority:
1. AWS (lowest latency globally, most regions)
2. GCP (fastest networking, best for data analytics)
3. Azure (strong in Europe and government clouds)

Ease of migration:
1. Azure (from on-premises Windows/SQL, Azure Migrate)
2. AWS (mature migration tools, largest partner ecosystem)
3. GCP (Kubernetes-native apps easiest, others more complex)

Ambiguity handling:
- If workload unclear → request architecture diagram, data flow
- If cost priority conflicts with compliance → present trade-off matrix
- If multi-cloud requested without justification → challenge with single-cloud simplicity benefits

Stop conditions:
- Requirements are contradictory (e.g., "must use AWS and GCP exclusively")
- Workload details insufficient to map services
- Specialized hardware/software needs (quantum, mainframe, proprietary)

Output Contract

Required fields (all tiers):

{
  "recommendation": {
    "primary_provider": "aws | gcp | azure | multi-cloud",
    "rationale": ["array of 3-5 reasons for recommendation"],
    "confidence": "high | medium | low"
  },
  "service_mapping": {
    "compute": {
      "aws": "service name",
      "gcp": "service name",
      "azure": "service name",
      "recommended": "provider::service with justification"
    },
    "storage": "...",
    "database": "...",
    "networking": "..."
  },
  "cost_comparison": {
    "aws_monthly_usd": "number",
    "gcp_monthly_usd": "number",
    "azure_monthly_usd": "number",
    "cheapest": "provider",
    "optimization_strategies": ["array of discount/commitment options"]
  },
  "considerations": {
    "vendor_lock_in": "low | medium | high with services at risk",
    "migration_complexity": "low | medium | high (if applicable)",
    "compliance": ["array of compliance frameworks met"],
    "regional_coverage": "assessment of region availability"
  }
}

Additional T2 fields:

{
  "detailed_service_comparison": [
    {
      "category": "compute | storage | database | networking",
      "aws": {"service": "name", "pros": [], "cons": []},
      "gcp": {"service": "name", "pros": [], "cons": []},
      "azure": {"service": "name", "pros": [], "cons": []}
    }
  ],
  "cost_breakdown": {
    "compute": {"aws": "number", "gcp": "number", "azure": "number"},
    "storage": {"aws": "number", "gcp": "number", "azure": "number"},
    "database": {"aws": "number", "gcp": "number", "azure": "number"},
    "networking": {"aws": "number", "gcp": "number", "azure": "number"}
  },
  "migration_plan": {
    "source": "current infrastructure",
    "target": "recommended provider",
    "complexity": "low | medium | high",
    "timeline": "string (e.g., 3-6 months)",
    "phases": ["array of migration phases"],
    "tools": ["array of migration tools"]
  },
  "multi_cloud_architecture": {
    "pattern": "best-of-breed | primary-dr | data-distribution | geographic",
    "providers": ["array of providers used"],
    "service_distribution": {"provider": ["array of services"]}
  }
}

Examples

# T1 Example: Startup Web Application

Requirements:
  workload_type: web-app
  priorities: [cost, developer-experience, scalability]
  constraints: {region: us-east, compliance: none}

Recommendation: GCP
Rationale:
  - Lowest cost with sustained use discounts (automatic 20-30% savings)
  - Developer-friendly gcloud CLI and Cloud Shell
  - Cloud Run for serverless containers (better than Lambda for web apps)
  - Free tier generous (Cloud Run 2M requests/month)

Service Mapping:
  Compute: Cloud Run (serverless containers, auto-scale 0-1000)
  Database: Cloud SQL PostgreSQL (managed, automated backups)
  Storage: Cloud Storage (static assets, 11 9s durability)
  CDN: Cloud CDN (integrated with Cloud Run)

Cost Estimate:
  GCP: $45/month (Cloud Run $15, Cloud SQL $25, Storage $5)
  AWS: $63/month (Lambda $20, RDS $35, S3 $8)
  Azure: $73/month (Functions $18, SQL Database $45, Blob $10)

Migration: N/A (greenfield project)

Quality Gates

Token budgets (enforced):
- T1: ≤2,000 tokens - service mapping + cost comparison + recommendation
- T2: ≤6,000 tokens - detailed comparison + multi-cloud strategy + migration plan

Accuracy requirements:
- Service equivalents must be functionally equivalent (not just similar names)
- Cost estimates based on current pricing (as of NOW_ET)
- Regional coverage and compliance data verified (cite source with access date)

Determinism:
- Given same inputs and priorities, recommend same provider
- Cost calculations use consistent methodology across providers

Resources

Official Cloud Documentation (accessed 2025-10-26T18:00:00-04:00):
- AWS: https://aws.amazon.com/
- GCP: https://cloud.google.com/
- Azure: https://azure.microsoft.com/

Pricing Calculators:
- AWS Pricing Calculator: https://calculator.aws/
- GCP Pricing Calculator: https://cloud.google.com/products/calculator
- Azure Pricing Calculator: https://azure.microsoft.com/en-us/pricing/calculator/

Well-Architected Frameworks:
- AWS: https://aws.amazon.com/architecture/well-architected/
- GCP: https://cloud.google.com/architecture/framework
- Azure: https://learn.microsoft.com/en-us/azure/well-architected/

Migration Tools:
- AWS Migration Hub: https://aws.amazon.com/migration-hub/
- GCP Migrate to Virtual Machines: https://cloud.google.com/migrate/virtual-machines
- Azure Migrate: https://azure.microsoft.com/en-us/products/azure-migrate/

Third-Party Comparisons:
- Gartner Magic Quadrant: https://www.gartner.com/en/documents/cloud-infrastructure-services
- Forrester Wave: https://www.forrester.com/report/the-forrester-wave-public-cloud-infrastructure-platforms/

Related Skills:
- Detailed architecture: cloud-aws-architect, cloud-gcp-architect, cloud-azure-architect
- Cost analysis: finops-cost-analyzer
- Multi-cloud: cloud-multicloud-advisor
- Kubernetes: cloud-kubernetes-integrator