AWS and Google Cloud get compared constantly, usually by people who have already decided the workload could run on either. That is normally true. What differs is cost shape, how much of the stack you run yourself, and which constraints each platform imposes.
This comparison works through the differences that actually change a decision, then says plainly which one to pick and when. Live starting prices and configuration counts for both are shown in the table below and come straight from the catalog.
Short answer. Choose AWS when the project will reach for many different managed services over its life. Choose Google Cloud when the centre of gravity is data or machine learning, and BigQuery or Vertex AI is the reason you are there at all.
AWS and Google Cloud at a glance
| AWS | Google Cloud | |
|---|---|---|
| Starting price | $25.00 | $30.00 |
| Configurations available | 25 | 9 |
| Best suited to | Teams that need one platform covering compute, storage, data and generative AI. | AI and data teams that want Vertex AI and BigQuery in one account. |
| Coverage | Global | Global |
| Notable services | EC2, S3, Amazon Bedrock, SageMaker | Vertex AI, Gemini Models, Model Garden, Compute Engine |
View AWS accountsView Google Cloud accounts
Service breadth
AWS
The widest service catalogue of any provider, spanning compute, storage, databases, networking, analytics and machine learning.
Google Cloud
A narrower catalogue than AWS but unusually strong where it counts: data analytics, containers and machine learning.
The practical question is not which catalogue is larger but whether the one you choose contains the specific services your project needs. A large catalogue you never touch is not an advantage.
How each one charges
AWS
Per-second and per-hour billing across dozens of instance families, with reserved and spot options that reward committed usage.
Google Cloud
Per-second billing with automatic sustained-use discounts, so long-running instances get cheaper without a commitment.
Beyond the instance rate, network egress is the cost that most often surprises people. On AWS, egress is billed per gigabyte and becomes a material line item on data-heavy workloads. On Google Cloud, egress is billed per gigabyte, with premium and standard network tiers that trade latency against cost.
If the workload moves a lot of data, read our breakdown of egress charges before comparing instance prices, because egress can dominate the bill.
The differences that matter
- Where each is strongest. AWS is chosen for unmatched service breadth and the deepest managed AI stack. Google Cloud is chosen for data analytics with BigQuery and the most coherent managed Kubernetes experience.
- What you give up. With AWS, cost and complexity that grow quickly if nobody is watching the bill. With Google Cloud, a smaller catalogue than AWS, so some niche managed services simply are not there.
Console and learning curve
AWS. A large console that rewards familiarity but is genuinely complex for newcomers, with IAM as a first-class concept from day one.
Google Cloud. A cleaner console than AWS with a genuinely good command line, and project-level isolation that maps well to teams.
Outbound email
AWS. Outbound port 25 is restricted by default; production email is expected to go through Amazon SES.
Google Cloud. Outbound port 25 is blocked on Compute Engine with no exception process; email must go through a relay.
This is a hard constraint rather than a preference, so settle it before anything else if your workload sends mail. The guide to port 25 explains which providers permit it.
Where they run
AWS. Regions on every populated continent, with fine-grained control over where data lives.
Google Cloud. Broad global coverage with strong presence in North America, Europe and Asia-Pacific.
AI and machine learning
AWS. Bedrock for hosted foundation models and SageMaker for training and deployment, alongside GPU instance families.
Google Cloud. Vertex AI as a single platform for training, tuning and serving, with Gemini models and a large model garden behind it.
If AI work is the reason for the account, our guide to choosing a cloud for AI workloads covers the ground in more detail.
Which one to choose
Choose AWS when
Pick AWS when you need more than servers, and expect to reach for managed data, analytics or AI services in the same account.
Choose Google Cloud when
Pick Google Cloud when the workload is analytics, containers or machine learning rather than a broad mix of everything.
Before you commit
- Confirm the specific services you need exist in the region you need them in. Availability varies by region on every provider.
- Estimate egress volume and price it on both platforms. It is the cost most often left out of a comparison.
- Check quotas. A new account starts with conservative limits, and raising them takes time on either platform.
- If outbound email matters, settle that question first, because it eliminates options rather than merely adding cost.
- Consider running both. Using each platform for what it is good at is more common than standardising on one.
Conclusion
Choose AWS when the project will reach for many different managed services over its life. Choose Google Cloud when the centre of gravity is data or machine learning, and BigQuery or Vertex AI is the reason you are there at all.
Both platforms are available here with published configurations and pricing: Buy AWS Accounts and Buy Google Cloud Accounts. If you are still weighing them, the full comparison table puts every provider side by side, and support can talk through the specific workload.
Related cloud accounts
Accounts on the platforms discussed here, with every configuration and price published up front:
- Buy AWS Accounts — from $25.00. Over 200 services, from EC2 to Bedrock.
- Buy Google Cloud Accounts — from $30.00. Vertex AI, Gemini models and BigQuery.
Further reading
- Running Kubernetes: Comparing the Managed Options
- AWS vs IBM Cloud: Which Cloud Account Should You Buy?
- Google Cloud vs Azure: Which Cloud Account Should You Buy?
- AWS vs Hetzner: Which Cloud Account Should You Buy?
Official documentation
Provider behaviour, quotas and pricing are defined by the platform itself. These are the first-party references for the services discussed above:
Frequently asked questions
It depends on the workload shape rather than the list price. Compare the starting prices in the table above, then add expected egress, because that is where the two most often diverge.
Plain virtual machines and object storage move relatively easily. Managed services do not, so the difficulty depends on how much of each platform's proprietary surface you used.
AWS if you need more than servers, and expect to reach for managed data, analytics or AI services in the same account. Google Cloud if the workload is analytics, containers or machine learning rather than a broad mix of everything.
