AWS vs GCP is the second hyperscaler comparison. GCP is cheaper for sustained workloads (automatic sustained-use discounts, no commitment), has the best global network (premium-tier), 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/K8s workloads. The team that picks AWS picks the safe default.
Table of contents
Pricing
GCP favors steady-state workloads:
- Automatic sustained-use discounts: VMs running >25% of the month get up to 30% off automatically. No commitment.
- Custom machine types: Pay for exactly the vCPU and RAM you need.
- Committed-use discounts: 1- or 3-year for additional savings.
- Spot VMs: Up to 80% off for preemptible workloads.
AWS:
- On-demand: Baseline.
- Savings Plans: 1- or 3-year commitments, up to 72% off.
- Reserved Instances: Older model.
- Spot Instances: Up to 90% off.
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 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.
- Higher cost for premium tier ($0.08/GB inter-region).
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.
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.
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.
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.).
FAQ
Is GCP cheaper than AWS?
For sustained workloads, yes (5-15% typical with auto-discounts). For bursty workloads, similar with spot. The team that runs the same workload on both usually finds GCP cheaper for steady-state.
Which is easier to learn?
GCP. Cleaner console, simpler IAM, better default behavior. The team that values simplicity picks GCP.
Which has better Kubernetes?
GKE. More managed, auto-upgrade on by default, deeper cloud-native networking. EKS is good but more setup.
Can I migrate from AWS to GCP?
Yes - tools like Transfer Appliance (data), Database Migration Service (databases), manual for the rest. Plan 3-6 months for non-trivial migrations.
What about GCP’s future?
GCP is profitable now (since 2024). Stable hyperscaler with continued investment. The team that picks GCP has a reasonable long-term bet.
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.
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