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Where Google Cloud stands against AWS and Azure
Omdia’s March 2026 estimate ranks AWS first, Microsoft Azure second, and Google Cloud third for global cloud infrastructure services in Q4 2025. Google Cloud had the highest year-over-year growth rate of the three in that quarter. These are dated estimates, not a permanent ranking or a measure of which provider delivers the best service for a particular workload.
Omdia’s category covers BMaaS, IaaS, PaaS, CaaS, and third-party hosted serverless services. It is not the entire software-cloud market and should not be read as market share for AI services specifically.
| Provider | Global infrastructure market share, Q4 2025 | Year-over-year growth in Q4 2025 |
|---|---|---|
| AWS | 32% | 24% |
| Microsoft Azure | 22% | 39% |
| Google Cloud | 12% | 50% |
All figures in the table are Omdia’s Q4 2025 estimates for cloud infrastructure services, published in March 2026. The share figures describe relative market position; the growth figures describe year-over-year revenue growth during that quarter. Neither tells you how a provider will perform for your application. Omdia’s Q4 2025 cloud infrastructure estimate
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What Microsoft’s FY2025 figures add—and what they do not
Microsoft reported that Azure and other cloud services revenue grew 34% in fiscal year 2025. This is a company-reported fiscal-year figure, not a directly comparable substitute for Omdia’s calendar-quarter growth estimates. Microsoft also reported a footprint of more than 400 datacenters in 70 regions. That is Microsoft’s own description, not an independently verified, like-for-like comparison of cloud regions across vendors.
In the same annual report, Microsoft presents Fabric and Azure AI Foundry as parts of its data and AI platform story. It also says, “Every Azure region is now AI-first and can support liquid cooling, increasing the fungibility and the flexibility of our fleet.” Treat that as Microsoft’s corporate description, not an independent benchmark of capacity, availability, or AI performance. Microsoft 2025 Annual Report
Rank #2
Compare the services you need in the regions you need
A provider’s headline footprint does not establish that every service, feature, or configuration is available in your target location. Start with the exact regions required for latency, data residency, disaster recovery, or regulatory reasons, then check the specific products and capabilities in each one.
Google Cloud’s locations page, last updated October 5, 2026, provides a region and product availability view. Google says new regions begin with a defined minimum set of services and that availability evolves over time. Its wording is explicit: “Available products in the region will continue to evolve based on customer demand.” Confirm current service details before committing to an architecture. Google Cloud global locations, regions, and zones
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Rank #3
- List the services and features your design actually depends on, rather than comparing region totals alone.
- Verify that each required service is offered in the intended region and check any region-specific limitations.
- Map where data is stored, processed, backed up, and transferred, including any cross-region dependencies.
- Check that failover and recovery designs can use the same required capabilities in the secondary region.
Choose around the workload, not a vendor-wide AI claim
For data and AI workloads, compare the models, data services, governance controls, throughput, deployment locations, and integration requirements that matter to the application. Microsoft’s FY2025 report documents its positioning around Fabric and Azure AI Foundry, but the available evidence does not establish a neutral feature or performance winner across AWS, Azure, and Google Cloud.
Turn the comparison into a workload test plan: specify the data and model requirements, measure performance under representative conditions, and validate operational controls in the regions you intend to use. Do not infer model quality, application reliability, or speed from a provider’s market share or growth rate.
Account for your existing ecosystem and migration costs
A cloud decision is rarely a clean-sheet feature comparison. Existing identity systems, software contracts, staff experience, operational processes, and the location of large datasets can all affect the effort and cost of moving. Those factors may favor staying with a current provider, adding a second provider for a specific need, or migrating only selected workloads; none makes one ecosystem the universal best choice.
The UK Competition and Markets Authority’s public cloud investigation examined customer purchasing, pricing, switching, and multi-cloud use. Its 2025 final decision recommended that the regulator use its digital markets powers to consider strategic market status investigations for Microsoft and AWS in cloud services. This is UK-specific regulatory context; it does not establish the status of later decisions or the position in other jurisdictions. UK Competition and Markets Authority: Cloud services market investigation
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- Inventory dependencies that would need to change, including application interfaces, identity, data pipelines, monitoring, and deployment processes.
- Estimate the time and risk of migration, parallel operation, retraining, and ongoing support—not just the effort to provision replacement infrastructure.
- Identify data movement and egress needs, as well as any contractual or operational constraints on moving data.
- Decide whether multi-cloud is justified by a specific requirement; operating across providers can add integration and support complexity.
Compare total cost for a defined workload
There is no established apples-to-apples price comparison here for a defined workload, so a universal claim that Google Cloud, AWS, or Azure is cheapest would be misleading. A useful estimate must match the workload configuration, location, usage pattern, and commercial terms.
Build the comparison around the same requirements for every provider:
- Compute type, size, runtime, and expected utilization.
- Storage capacity, performance tier, retention, and backup requirements.
- Network traffic, including data transfer between services, regions, and the public internet.
- Support level, commitment period, and any negotiated discounts or credits.
- Migration, training, tooling, and the cost of operating the service after deployment.
Use your expected usage rather than list prices alone, and make the assumptions visible. A lower infrastructure quote may not remain lower after transfer charges, support, migration work, or underused committed capacity are included.
Do not confuse public-cloud or AI-compute estimates
The OECD’s 2025 report gives a separate estimate of public-cloud market shares: 31% for AWS, 24% for Microsoft Azure, and 11.5% for Google Cloud. Those estimates draw on source data from 2022–2024, so they are older and not directly comparable with Omdia’s Q4 2025 infrastructure figures. The OECD also describes these as general public-cloud estimates, not AI-specific shares. Its work on AI compute availability includes other providers as well, so AWS, Azure, and Google Cloud should not be treated as the entirety of global competition. OECD, Measuring domestic public cloud compute availability for artificial intelligence
A practical way to make the decision
- Set the constraints: define required regions, data-location rules, service capabilities, recovery objectives, and security or governance needs.
- Shortlist providers against the actual design: confirm that each required product and feature is available where it must run.
- Test representative workloads: compare operational fit and measured performance using your own requirements, rather than relying on broad vendor claims.
- Model full lifecycle cost: include infrastructure, data movement, support, migration, training, and commercial commitments.
- Account for organizational fit: weigh existing systems and skills against the cost and benefits of staying, switching, or introducing another provider.
Google Cloud’s faster growth in Omdia’s Q4 2025 estimate shows competitive momentum, while AWS remained the largest and Azure the second-largest provider by that measure. For an organization choosing a platform, the deciding evidence should be whether a provider meets the workload’s regional, technical, operational, and cost requirements.
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