GCP vs AWS is the second hyperscaler comparison. GCP is cheaper for sustained workloads (automatic sustained-use discounts, no commitment needed), has the best global network (premium-tier, private backbone), and wins for data (BigQuery), ML (TPUs, Vertex AI), and Kubernetes (GKE). AWS has more services, more regions, bigger ecosystem, and the largest hiring market. The team that picks GCP usually has data, ML, or Kubernetes-heavy workloads. The team that picks AWS picks the safe default.
Table of contents
- Pricing
- Network
- Compute and Kubernetes
- Data and ML
- Ecosystem and support
- When to pick GCP
- When to pick AWS
- FAQ
Pricing
GCP’s pricing model favors steady-state workloads:
- Automatic sustained-use discounts: VMs running for >25% of the month get up to 30% off automatically. No commitment required.
- Custom machine types: Pay for exactly the vCPU and RAM you need, not a fixed shape.
- Committed-use discounts: Additional savings for 1-year or 3-year commitments.
- Spot VMs: Up to 80% off for preemptible workloads.
AWS:
- On-demand: Baseline pricing.
- Savings Plans: 1- or 3-year commitments, up to 72% off.
- Reserved Instances: Older model, similar savings.
- Spot Instances: Up to 90% off, can be interrupted.
The team that runs 24/7 workloads on GCP pays less than on-demand AWS. The team that commits to AWS gets close. The team that uses spot/preemptible on either is cheapest for interruptible workloads.
Network
GCP’s premium-tier network:
- Inter-region traffic over Google’s private backbone.
- Lower latency than public internet between regions.
- Costs more than AWS for inter-region transfer (premium vs standard).
AWS:
- CloudFront for CDN (most mature).
- Global Accelerator for anycast IPs.
- Direct Connect for dedicated connections.
- Transit Gateway for hub-and-spoke.
The team that needs global low-latency picks GCP premium. The team that needs the most mature global network picks AWS.
Compute and Kubernetes
GCP wins on Kubernetes:
- GKE: Most-managed K8s (Autopilot, auto-upgrade, auto-repair).
- Cloud Run: Easier-than-Lambda containers.
- Compute Engine: Custom machine types.
AWS wins on services breadth:
- EC2: 400+ instance types.
- EKS: Good K8s but more setup.
- Lambda: Function-as-a-service pioneer.
- Specialty hardware: Trainium, Inferentia, DL1 (Deep Learning AMIs).
The team that runs K8s at scale picks GKE. The team that needs specialty hardware or huge service variety picks AWS.
Data and ML
GCP:
- BigQuery: The data warehouse leader.
- Vertex AI: Unified ML.
- TPUs: Custom ML chips, the cheapest for large training.
- Bigtable, Spanner: Google’s globally-distributed database heritage.
AWS:
- Redshift: Data warehouse, older than BigQuery.
- SageMaker: ML platform, comprehensive.
- Inferentia, Trainium: Custom ML chips.
- DynamoDB, Aurora, Neptune: Wide database variety.
The team that runs BigQuery-style analytics or large-scale ML picks GCP. The team that uses specific AWS services (Aurora, Step Functions, SageMaker) stays on AWS.
Ecosystem and support
AWS:
- Most third-party integrations and tools.
- Largest community (Stack Overflow, GitHub, blogs).
- Most certifications and training.
- ~31% market share vs GCP’s ~11%.
GCP:
- Smaller but growing community.
- Strong enterprise customers (Twitter, Spotify, PayPal).
- Better integration with Google’s other products (Workspace, Maps, etc.).
The team that hires a lot of cloud engineers picks AWS (more candidates). The team that values simplicity and managed services picks GCP.
When to pick GCP
Pick GCP when:
- Data analytics is a primary workload (BigQuery).
- ML at scale (TPUs, Vertex AI).
- Multi-region low-latency (premium network).
- Kubernetes at scale (GKE).
- Sustained workloads (auto-discounts).
- Google Workspace integration.
The team that fits one or more of these picks GCP.
When to pick AWS
Pick AWS when:
- Broad service catalog matters.
- Large ecosystem / hiring market.
- Specific AWS services required.
- Existing AWS investment.
- Compliance / FedRAMP.
- Specialty hardware (Trainium, etc.).
The team that fits one or more of these picks AWS.
FAQ
Is GCP cheaper than AWS?
For sustained workloads, yes - automatic discounts without commitment. For bursty workloads, depends on usage. The team that runs the same workload on both usually finds GCP 5-15% cheaper for steady-state, similar for bursty with spot.
Which is easier to learn?
GCP. Cleaner console, simpler IAM model, better default behavior. AWS has more depth but more complexity. The team that values simplicity picks GCP.
Which has better global reach?
AWS: 30+ regions, 400+ edge locations. GCP: 40+ regions, premium network. The team that needs the most regions picks AWS. The team that needs the fastest network between regions picks GCP.
Can I migrate from AWS to GCP?
Yes - tools like Google Transfer Appliance (for data), Database Migration Service (for databases), and manual effort for the rest. The team that migrates a non-trivial workload should plan 3-6 months and use a migration partner.
What about GCP’s future?
GCP is profitable now (Google Cloud became profitable in 2024). The team that picks GCP is betting on a stable hyperscaler with continued investment. The team that picks based on long-term stability has a reasonable bet either way.
If you are sizing the infrastructure for the kind of project this post covers, the RunxBuild hosting calculator is the right place to model the line items. The compute, the memory, the storage, the bandwidth, the database - each one is a separate number, and the team’s mental model for the platform is the sum of those numbers. The RunxBuild dashboard is where the team sees the actual usage in one place.
Useful related references: