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10 November 2026
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AWS vs Azure vs GCP: which cloud is best? Wrong question. The better one is which cloud provider is right for your product, and who will move you there. Both answers matter, and the second one gets far less airtime in most AWS vs Azure vs Google Cloud comparisons.
Think of it as choosing a city to relocate your office to. Rent, transport links, local rules, where your people already live. Nobody picks a city because a table says it has more cafes. Yet that is how most comparisons are written.
This guide stays at the level of the business decision. We draw on products we have built and migrated on all three providers: Aurora Analytica on AWS, VestaConnect on Google Cloud, and Neom across AWS and Azure. For the technical detail, we point you to pages that already cover it properly.
Five things should decide any AWS vs Azure vs Google Cloud for migration decision: what your business already runs on, what your data and AI plans look like, what regulation applies, what your team can run day to day, and how easily you could leave. Feature lists come after, not before. Most providers can do most things.
Here is how each one plays out:
(If a vendor tells you one of these does not matter, check who is paying them.)
What about market share? It tells you who is big, not who fits. For the record, Synergy Research Group's Q3 2026 figures put Amazon at 26%, Microsoft at 19% and Google at 16% of a cloud infrastructure services market worth about $168 billion in the quarter. (Synergy counts infrastructure, platform and hosted private cloud services, and Microsoft's share covers the whole of that business, not Azure alone.) Three providers, three very different customers.
If you want the formal playbooks, each provider publishes its own. AWS Prescriptive Guidance sets out seven migration strategies, and the Microsoft Cloud Adoption Framework covers the business side of an Azure move. For a view across all of them, our guide to cloud migration strategies for enterprises is the next stop.
AWS tends to fit best when you want the widest choice of managed services, a large talent pool and a mature ecosystem of partners and tooling. It suits products that will keep changing shape, and teams that would rather have options than a single opinionated path.
That breadth is also the catch. More choice means more decisions, and someone has to make them.
Aurora Analytica is a good example of AWS doing what it does well. We built the Geo Matrix Decision Engine for them, a scenario-planning tool that lets contract research organisations run scenarios with their own data and see the results by country on a world map.
It sits close to regulated clinical work, so hosting needed to be routine and well understood. The stack is Next.js, Nest.js, Prisma and PostgreSQL, with AWS for hosting. Read the full story in the Aurora Analytica case study.
One point applies to any provider, but it is worth making here. Moving to AWS does not move your responsibility. The AWS shared responsibility model is the principle that the provider secures the cloud itself, while you remain responsible for what you put in it and how you configure it. A good migration partner makes that line visible early.
Azure tends to fit best when your business already runs on Microsoft: identity, productivity tools, licensing and hybrid setups with on-premises systems. Its enterprise AI services are a second draw. If your people already live in the Microsoft world, Azure usually costs less in friction than any alternative.
Is that the whole story? No, but it is the part that decides most projects. Azure is rarely chosen because of one clever service. It is chosen because everything around it already fits.
We have written about Azure in detail. If you want to go further than a business-level view, these are the pages to read:
The Azure side of the Neom work, covered below, shows the other use case: Azure as one part of a larger estate, not the whole of it.
Google Cloud tends to fit best for product-led teams that favour managed platforms, containers and data work, and that want to move quickly without carrying a large infrastructure team. It is often the quiet choice, and the right one more often than its market share suggests.
VestaConnect shows how that looks in practice. They partnered with us to build a platform for seniors, including people facing cognitive challenges, and for the families, caregivers and professionals around them. The whole infrastructure runs on Google Cloud Platform, alongside FastAPI, Python, PostgreSQL, React and React Native.
The path was an MVP first, beta user feedback shaping the roadmap, a native mobile app in a later phase, and then the move from beta to a paid model. You can read the details in the VestaConnect case study.
The lesson is not that Google Cloud is cheaper or better. It is that, for a young product with a fast roadmap, a platform that stays out of the way is worth more than a longer feature list. If you need a map between providers, Google publishes a handy service comparison across AWS, Azure and Google Cloud.
A multi-cloud migration makes sense when different parts of the business have genuinely different needs, or when resilience demands it. It rarely makes sense as a default. Each extra provider adds skills to hire, governance to maintain and a bill to reconcile.
It is common, though. Flexera's 2026 State of the Cloud report, a survey of more than 750 IT decision-makers, found that 88% of organisations use multiple cloud providers (the press release is in German). Often that happens by accident, through mergers, acquisitions and teams buying their own tools, not by design.
Neom is our clearest example of doing it on purpose. Our own work for them spans large-scale AI and software infrastructure across AWS and Azure, supporting more than 200 concurrent development teams.
At that scale, one provider rarely suits every team. The detailed scope and results are covered by a non-disclosure agreement, so we can only share what is already public. Our Neom case study page explains why.
If resilience is the reason you are thinking about two providers, start with AWS and Azure disaster recovery solutions compared. It goes into the technical depth that this article deliberately skips.
No provider is cheapest for every workload. The bill depends on what you run, how you commit to it, how much data you move and which licences you already own. Return on investment then comes from what you retire, speed to market and fewer outages, not from a lower list price.
Four things move the bill most:
Price your real workload in each provider's own pricing calculator, not in a generic comparison table. Then plan to manage the spend after migration. In the same Flexera survey, 85% of organisations said managing cloud costs is their biggest challenge.
What about time to value? Start with one workload, not the whole estate. A first migration that goes live in a controlled window gives you real numbers, a rehearsed process and a team that has done it once.
For an example of the return side, read how a global fintech reduced operating costs by 40 to 50% through cloud modernisation. That result was about the approach, not about any single provider.
Sustainability is increasingly part of enterprise buying decisions, and all three providers publish environmental reporting. Read the reports, but check what each one measures before comparing headline figures. Ask your migration partner to flag it early, since it can shape region and architecture choices.
Choose a partner who has delivered on more than one provider, who will tell you when your preferred cloud is the wrong one, and who owns the migration plan from assessment to cut-over. Choosing a cloud migration partner and choosing a provider are linked decisions, so test them together.
Five questions help:
We have built products on AWS, Azure and Google Cloud, so none of them is our default answer. (We still have opinions, as you may have noticed.) The three projects in this article are our evidence, and you can ask us about any of them.
The right provider depends on your product profile. Regulated or data-sensitive products often suit AWS, Microsoft-centric businesses suit Azure, and fast-moving young products often suit Google Cloud. Very large organisations with many teams may use two. We call this sorting method the Product-Fit Matrix.
The Product-Fit Matrix works in two steps. First, score your product against the five criteria at the top of this article. Second, find the profile below that looks most like yours. These are tendencies, not rules, and each one comes from a project we have delivered.
| Product profile | Likely fit | Why | Our example |
|---|---|---|---|
| Regulated or data-sensitive product that needs broad, well-understood services | AWS | Mature ecosystem, wide choice, clear responsibility model | Aurora Analytica |
| Microsoft-centric business, hybrid setup, enterprise AI | Azure | Identity, licensing and tooling already fit | The Azure side of Neom |
| Young product with a fast roadmap, from MVP to paid | Google Cloud | Managed platform that stays out of the way | VestaConnect |
| Very large organisation with many teams and different needs | AWS and Azure | Different teams, different strengths | Neom |
It depends on what your business already runs on. Choose Azure if your identity, licences and tooling are Microsoft-based, and AWS if you want the widest choice of services and a large talent pool. Neither wins every migration, so match the choice to your product.
None is cheapest for every workload. Cost depends on usage patterns, commitments, data transfer and licences you already own, such as Windows Server and SQL Server. Price your actual workload in each provider's calculator, then add the cost of the people who will run it.
There is no single answer, but the method is simple. Price your expected workload in each provider's calculator, then favour the platform your team already knows. For a young product, speed of delivery usually matters more than a small gap in monthly cost.
Start with an assessment of your applications, then choose a strategy for each workload, such as rehosting, replatforming or refactoring. Migrate one workload first as a pilot, then move in waves. AWS Prescriptive Guidance, linked earlier, describes the seven strategies in detail.
Yes. Large organisations often do, as in our Neom work across AWS and Azure. For most businesses, one provider is simpler and cheaper to run. Add a second only when a clear business reason justifies the extra skills and governance.
You cannot avoid it entirely, but you can manage it. Keep your data portable, favour open standards where they cost you little, and document the decisions that tie you to one provider. Then decide, on purpose, how much convenience is worth.
AWS vs Azure vs GCP is not a contest with a single winner. Pick the cloud that fits your product, your team and your risk profile, count the full cost including people and data movement, and expect to manage that cost after launch. Then pick a migration partner who has done this on more than one provider and will say so when the answer is not the one you hoped for.
Planning a migration, or not sure which provider fits? Talk to our cloud team about cloud modernisation and automation, or get in touch directly and tell us about your product. We will give you a straight answer, even if it is not the one that suits us.
Alexandra Mendes is a Senior Growth Specialist at Imaginary Cloud with 3+ years of experience writing about software development, AI, and digital transformation. After completing a frontend development course, Alexandra picked up some hands-on coding skills and now works closely with technical teams. Passionate about how new technologies shape business and society, Alexandra enjoys turning complex topics into clear, helpful content for decision-makers.
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