AWS and Google Cloud overlap on almost everything and diverge sharply on a few things that matter. Choosing well means identifying which of those few things applies to you.
Quick comparison
| AWS | Google Cloud | |
|---|---|---|
| Service breadth | Widest of any platform | Narrower but well integrated |
| Data and analytics | Redshift, Athena, Glue, EMR: assembled | BigQuery: adopted |
| Managed AI | Bedrock and SageMaker | Vertex AI and Model Garden |
| Discounts | Savings Plans, Reserved Instances (commitment) | Sustained use applied automatically |
| Machine shapes | Fixed instance types | Fixed plus custom shapes |
| Ecosystem and hiring | Largest talent pool | Smaller |
| Accounts here | 20 configurations from $20 | 7 configurations from $50 |
Where Google Cloud wins
Analytics. BigQuery separates storage from compute, so large ad-hoc queries need no cluster sitting idle. On AWS the equivalent capability exists but you assemble it from several services and operate more of it. If analytics is central to your product, this alone can decide the platform. See BigQuery cost control.
Pricing mechanics. Sustained use discounts apply automatically without forecasting or commitment. On AWS, equivalent savings require Savings Plans or Reserved Instances, which means predicting your baseline.
Custom machine shapes. You can specify vCPU and memory independently, which saves real money on unbalanced workloads. AWS rounds you up to the next fixed shape. See Google Cloud machine types.
Where AWS wins
Breadth. If a specialised capability exists anywhere, it usually exists on AWS first. For unusual requirements this matters more than any pricing difference.
Ecosystem. More third-party tooling, more documentation, more engineers who already know it. Ramp-up time is a real cost and AWS minimises it.
Instance variety. A far larger range of instance families, including deep Graviton Arm options that are frequently the best price for performance available. See EC2 instance families.
AI and machine learning
Both offer managed foundation models and full training platforms. The practical difference is integration: if your data already sits in BigQuery, Vertex AI is a short path from warehouse to model. If you want maximum model choice and infrastructure control, Bedrock and SageMaker give it.
On both platforms the binding constraint for inference is usually requests per minute rather than raw compute, and for training it is accelerator quota in your chosen region. See Vertex AI and AWS configurations.
Cost
Headline compute pricing is close enough that it rarely decides anything. The differences that matter are egress pricing, managed service premiums and cross-zone traffic, all of which are easy to forget when modelling. See cloud egress costs.
If your workload is plain virtual servers, neither platform is the cheapest option. Hetzner and DigitalOcean are substantially less expensive for that. See AWS vs DigitalOcean.
Account options on BuyCloudAccess
- AWS accounts: 20 configurations from $20, covering vCPU tiers up to 1,920, credit balances up to 100K, and Bedrock enabled options described by requests per minute.
- Google Cloud accounts: 7 configurations from $50, including credit tiers of $300, $1,300, $5,000 and $25,000.
Remember that credits and quotas are separate systems on both platforms. A large balance does not raise what you can launch. See quotas vs credits.
Which should you choose
- Analytics or ML is the product? Google Cloud.
- Need obscure services, or the largest hiring pool? AWS.
- Unbalanced CPU-to-memory workload? Google Cloud custom shapes.
- Want automatic discounts without forecasting? Google Cloud.
- Just need cheap virtual servers? Neither.
Common questions
Which is cheaper overall?
It depends on workload shape. Google Cloud often wins on unbalanced and bursty workloads; AWS wins where Graviton or reserved commitments fit.
Can I run both?
Yes, and many teams keep one primary platform plus a second for a specific capability. Treat it as a deliberate choice, since it costs operational complexity.
Do the account tiers compare directly?
No. Each platform has its own quota model and credit programme. Compare configurations rather than assuming equivalence on the comparison page.