AWS
Amazon's public cloud (market leader)
Google Cloud
Google's public cloud with deep AI/data
AWS has the broadest service catalog, largest market share, and deepest enterprise footprint. GCP wins on data/AI (BigQuery, Vertex AI, TPU), networking simplicity, and developer UX in specific areas. Both are production-ready at any scale — the call is usually driven by existing team expertise and contracts.
Pick AWS when you want the broadest services, deepest enterprise support, and most 3rd-party tooling.
Pick GCP when your workload is data/ML-heavy (BigQuery, Vertex, TPU) or you value simpler networking.
| Feature | ☁️AWS | 🌤️Google Cloud | Winner |
|---|---|---|---|
| Service catalog breadth | ~240 services | ~150 services | A |
| Market share | ~31% | ~12% | A |
| Data warehouse | Redshift | BigQuery (industry-leading) | B |
| AI/ML stack | SageMaker + Bedrock | Vertex AI + TPU | B |
| Kubernetes | EKS | GKE (native, best) | B |
| Global network | Broadest | Fastest private backbone | Tie |
| Pricing complexity | Notoriously complex | Simpler | B |
| Enterprise contracts | Deepest | Growing | A |
Service catalog breadth
AAWS
~240 services
Google Cloud
~150 services
Market share
AAWS
~31%
Google Cloud
~12%
Data warehouse
BAWS
Redshift
Google Cloud
BigQuery (industry-leading)
AI/ML stack
BAWS
SageMaker + Bedrock
Google Cloud
Vertex AI + TPU
Kubernetes
BAWS
EKS
Google Cloud
GKE (native, best)
Global network
TieAWS
Broadest
Google Cloud
Fastest private backbone
Pricing complexity
BAWS
Notoriously complex
Google Cloud
Simpler
Enterprise contracts
AAWS
Deepest
Google Cloud
Growing
Best for
Best for
Multi-cloud migrations are big projects. Use Terraform/OpenTofu to define infra in the destination, rehost with tools like CloudEndure (to AWS) or Migrate for Compute Engine (to GCP), move databases via DMS or Database Migration Service, and plan a DNS cutover weekend. Budget 6-18 months for a non-trivial workload.
AWS has the broadest service catalog, largest market share, and deepest enterprise footprint. GCP wins on data/AI (BigQuery, Vertex AI, TPU), networking simplicity, and developer UX in specific areas. Both are production-ready at any scale — the call is usually driven by existing team expertise and contracts. In short: AWS — Amazon's public cloud (market leader). Google Cloud — Google's public cloud with deep AI/data.
Pick AWS when you want the broadest services, deepest enterprise support, and most 3rd-party tooling.
Pick GCP when your workload is data/ML-heavy (BigQuery, Vertex, TPU) or you value simpler networking.
Multi-cloud migrations are big projects. Use Terraform/OpenTofu to define infra in the destination, rehost with tools like CloudEndure (to AWS) or Migrate for Compute Engine (to GCP), move databases via DMS or Database Migration Service, and plan a DNS cutover weekend. Budget 6-18 months for a non-trivial workload.
Yes. Both have MCP servers installable via MCPizy (mcpizy install aws and mcpizy install gcp). They work identically across Claude Code, Claude Desktop, Cursor, Windsurf, and any other MCP-compatible client. You can install both side by side and route queries in your agent's prompt.
AWS is the market leader with the broadest catalog. Azure is #2 with deep Microsoft enterprise integration (AD, Office, Windows Server, SQL Server, .NET) and strong government/regulated footing. Pick AWS for greenfield / open-source stacks; Azure for Microsoft-shop enterprises.
Cloudflare is the largest CDN + edge platform — free tier, Workers, R2, D1, WAF, DNS, tunnels, Zero Trust. Fastly is the premium real-time CDN — instant purge, VCL power, used by Reddit/GitHub/NYT. Cloudflare wins on breadth + cost; Fastly wins on configurability and real-time invalidation.
Kubernetes is the industry-standard orchestrator — massive ecosystem, every cloud supports it. HashiCorp Nomad is dramatically simpler (one binary, fewer abstractions), supports containers + VMs + raw executables, and is cheaper to operate for small-to-medium fleets.
Not sure? Run both side by side — swap between them in your AI agent with a single config line.