AWS vs Google
Cloud Platform
For organizations evaluating cloud migration or multi-cloud strategy, the architectural and pricing differences between AWS and GCP determine which workloads benefit from which platform.
Side-by-Side Comparison
On-demand, reserved instances (1-3 year commits), spot instances, savings plans. Complex pricing requires dedicated FinOps. Reserved instance management is operational overhead.
On-demand with automatic sustained-use discounts (no commitment needed). Committed-use discounts for predictable workloads. Simpler pricing model with less operational overhead.
Redshift (provisioned clusters), Athena (per-scan), EMR (managed Hadoop). Multiple services for different analytics patterns. Cluster sizing and management required for Redshift.
BigQuery — serverless, per-query pricing, no cluster management. Single service handles interactive queries, batch processing, and ML. Slot reservations for predictable cost at scale.
EKS — managed control plane. Worker node management is customer responsibility. ALB ingress, IAM roles for service accounts, cluster autoscaler require configuration.
GKE — managed control plane and optional Autopilot mode (fully managed nodes). Workload Identity, GKE Ingress, and autoscaling require less configuration than EKS equivalents.
Regional VPCs. Cross-region connectivity requires VPC peering or Transit Gateway. Complex but flexible. More granular control over network security (security groups + NACLs).
Global VPCs with subnets spanning regions. Simpler cross-region networking. Premium tier uses Google's private backbone. Less granular than AWS but simpler to configure correctly.
SageMaker — model training, hosting, MLOps. Bedrock for managed LLM access. Comprehensive but complex. Multiple services for different ML stages.
Vertex AI — unified platform for training, hosting, and MLOps. Gemini models natively available. BigQuery ML for in-database ML. Simpler path from data to model.
200+ services. Broadest service catalog of any cloud. First-mover in many categories. More niche services available (IoT, satellite, blockchain).
100+ services. Focused on core categories. Fewer niche services. Deeper in data analytics, ML, and Kubernetes. Less breadth but strong depth in focus areas.
Egress fees: $0.09/GB (first 10TB). Cross-region transfer fees. Data transfer is a significant cost at scale. Makes multi-cloud and cloud migration expensive.
Egress fees: $0.12/GB (first 1TB), lower at scale. Similar cross-region fees. Google has announced reduced egress for customers leaving GCP. Still significant at petabyte scale.
When GCP is the better choice
Choose GCP if data analytics is a primary workload and BigQuery's serverless model eliminates Redshift cluster management, Kubernetes is the target platform and GKE Autopilot reduces operational overhead, AI/ML is strategic and Vertex AI's integrated approach simplifies the ML pipeline, or sustained-use discounts provide cost savings without reserved instance management.
Stay on AWS if service breadth matters — AWS has services for niche use cases that GCP does not cover, the organization has deep AWS expertise and retraining cost exceeds migration benefit, or compliance requirements favor AWS's broader certification coverage.
Consider multi-cloud for specific workloads — GCP for analytics and ML, AWS for everything else — as a lower-risk path that captures GCP's strengths without full migration.
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