Google Cloud and Azure 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. Google Cloud is the stronger data and Kubernetes platform. Azure is the stronger enterprise-integration platform. Neither gap is small, and the right answer usually follows from what the rest of the business already runs.
Google Cloud and Azure at a glance
| Google Cloud | Azure | |
|---|---|---|
| Starting price | $30.00 | $25.00 |
| Configurations available | 9 | 5 |
| Best suited to | AI and data teams that want Vertex AI and BigQuery in one account. | Organisations already invested in Microsoft tooling and identity. |
| Coverage | Global | Global |
| Notable services | Vertex AI, Gemini Models, Model Garden, Compute Engine | Azure OpenAI Service, Azure Machine Learning, Azure Virtual Machines, Azure Storage |
View Google Cloud accountsView Azure accounts
Service breadth
Google Cloud
A narrower catalogue than AWS but unusually strong where it counts: data analytics, containers and machine learning.
Azure
A full-service catalogue with unusually deep hybrid and on-premises integration.
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
Google Cloud
Per-second billing with automatic sustained-use discounts, so long-running instances get cheaper without a commitment.
Azure
Per-second billing with reservations, plus licensing benefits that materially reduce cost if you already own Windows Server or SQL Server licences.
Beyond the instance rate, network egress is the cost that most often surprises people. On Google Cloud, egress is billed per gigabyte, with premium and standard network tiers that trade latency against cost. On Azure, egress is billed per gigabyte, with the first allowance free each month.
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. Google Cloud is chosen for data analytics with BigQuery and the most coherent managed Kubernetes experience. Azure is chosen for hybrid deployments and integration with existing Microsoft identity and licensing.
- What you give up. With Google Cloud, a smaller catalogue than AWS, so some niche managed services simply are not there. With Azure, a portal and resource model that can feel heavier than the alternatives for a simple server.
Console and learning curve
Google Cloud. A cleaner console than AWS with a genuinely good command line, and project-level isolation that maps well to teams.
Azure. A portal that will feel familiar to anyone who administers Microsoft products, with Entra ID handling identity.
Outbound email
Google Cloud. Outbound port 25 is blocked on Compute Engine with no exception process; email must go through a relay.
Azure. Outbound port 25 is blocked on most subscription types and is not unblocked on request.
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
Google Cloud. Broad global coverage with strong presence in North America, Europe and Asia-Pacific.
Azure. The widest declared region count of the three hyperscalers, including many sovereign and government regions.
AI and machine learning
Google Cloud. Vertex AI as a single platform for training, tuning and serving, with Gemini models and a large model garden behind it.
Azure. Azure OpenAI Service for enterprise access to GPT-family models, plus Azure Machine Learning and AI Foundry for the wider lifecycle.
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 Google Cloud when
Pick Google Cloud when the workload is analytics, containers or machine learning rather than a broad mix of everything.
Choose Azure when
Pick Azure when the organisation already runs on Microsoft identity, tooling or licensing.
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
Google Cloud is the stronger data and Kubernetes platform. Azure is the stronger enterprise-integration platform. Neither gap is small, and the right answer usually follows from what the rest of the business already runs.
Both platforms are available here with published configurations and pricing: Buy Google Cloud Accounts and Buy Azure 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 Google Cloud Accounts — from $30.00. Vertex AI, Gemini models and BigQuery.
- Buy Azure Accounts — from $25.00. Azure OpenAI and deep Microsoft integration.
Further reading
- Google Cloud Accounts: What You Get and How to Choose a Configuration
- Azure vs IBM Cloud: Which Cloud Account Should You Buy?
- Google Cloud vs Oracle Cloud: Which Cloud Account Should You Buy?
- Azure Accounts: What You Get and How to Choose a Configuration
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.
Google Cloud if the workload is analytics, containers or machine learning rather than a broad mix of everything. Azure if the organisation already runs on Microsoft identity, tooling or licensing.
