AWS vs Google Cloud (GCP)
Amazon Web Services and Google Cloud Platform are two of the three dominant hyperscalers competing for enterprise cloud workloads. AWS leads in breadth of services and market share, while GCP leads in AI/ML capabilities, data analytics, and Kubernetes. The right choice often depends on existing technical stack and use cases.
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The world's most comprehensive cloud platform vs Build what's next with Google Cloud.
| Feature | AWS | Google Cloud (GCP) |
|---|---|---|
| Pricing from | Pay-as-you-go | Pay-as-you-go |
| Pricing | Pay-as-you-go; significant discounts with Reserved/Savings Plans | Pay-as-you-go; Committed Use Discounts up to 57% |
| Best for | General-purpose workloads and largest service catalog | AI/ML, data analytics, and Kubernetes |
| AI/ML services | SageMaker, Bedrock, Rekognition | Vertex AI, BigQuery ML, TPU access |
| Managed Kubernetes | EKS (Elastic Kubernetes Service) | GKE (Google Kubernetes Engine) – Kubernetes creator |
| Global regions | 33 regions, 105 availability zones | 40+ regions worldwide |
| Data analytics | Redshift, Glue, Athena | BigQuery (serverless, auto-scaling) |
The third option most teams miss
Picking between AWS and Google Cloud (GCP) isn't the only choice.
Appaca deploys on both AWS and GCP, letting you build multi-cloud AI workflows without architecture lock-in. Route workloads to the optimal cloud based on cost, latency, or compliance requirements from a single control plane.
- No code, no deployment, no devops
- Built-in database, dashboards, team access
- Refine with chat as your needs change
- Free to start, no per-seat pricing surprises
Common questions
GCP often wins on compute pricing and offers sustained use discounts automatically. AWS and Azure require manual configuration of Savings Plans or Reserved Instances to achieve equivalent savings. Actual cost depends heavily on workload mix.
GCP has an edge in ML infrastructure thanks to its TPUs, Vertex AI managed pipelines, and BigQuery ML integration. AWS SageMaker is more mature as an end-to-end ML platform for enterprise teams.
Yes, multi-cloud is common for enterprises. Terraform or Pulumi manage both from a single configuration. Data transfer costs between clouds are a consideration in multi-cloud architectures.