AWS vs. Azure vs. GCP: How to Actually Choose
Stop choosing a cloud by brand
The three major hyperscalers get compared like sports teams. Someone read that a competitor runs on one of them, an executive has a preference from a past job, or a vendor's sales team ran a good dinner — and suddenly the platform is chosen before anyone has looked at what the business actually needs to run. That is how organizations end up fighting their own cloud for the next five years.
All three are excellent, mature platforms. Compute is compute, object storage is object storage, and for a large share of ordinary workloads any of them would do the job. The differences that matter are not in the marketing comparisons. They are in how well a given cloud fits your existing stack, data and ML ambitions, pricing sensitivity, compliance geography, and the skills your team already has. Choose against those, not against a logo.
Here is the framework we use with clients when we build out their public cloud strategy — the factors that actually decide it, and the order to weigh them in.
Figure: no cloud wins every row — the right choice is the one that wins the rows your business weighs most heavily.
The factors that actually decide it
Your existing stack and identity
This is the single most under-weighted factor, and often the most decisive. If your organization already runs Microsoft 365, Active Directory, Entra ID, and a fleet of Windows Server and SQL Server, then Azure is not just an option — it is the path of least resistance. Identity federation is native, licensing benefits such as Hybrid Benefit reduce the cost of moving existing Windows and SQL workloads, and your admins already speak the language. Fighting that gravity to stand up the same environment elsewhere means rebuilding identity, re-licensing, and retraining, all for a benefit you often cannot name.
The same logic runs the other way. A team already deep in open-source tooling, Linux, and containers will feel at home on AWS, which has the broadest service catalog and the largest ecosystem of third-party integrations. A data-first organization frequently finds GCP's tooling the most natural. The lesson holds regardless of direction: the cloud that fits your existing identity, licensing, and operational muscle memory starts with a real head start. Do not throw it away without a reason you can defend in a sentence.
The shape of your workload
Match the workload to the platform's strengths rather than to its brand:
- General enterprise applications, broadest service selection, largest partner ecosystem. AWS has the deepest catalog and the most mature marketplace, which matters when you need a niche managed service to exist rather than to build it.
- Microsoft-centric estates, Windows and SQL Server, hybrid with on-premises. Azure's integration with the Microsoft stack and its hybrid tooling make it the low-friction choice.
- Data engineering, analytics, and machine learning at scale. GCP's data and ML services — its warehouse, its pipelines, its ML platform — are frequently the most cohesive experience for teams whose center of gravity is data.
None of these are hard walls. You can run a superb data platform on any of the three. But building against a cloud's strengths means less custom plumbing, fewer rough edges, and less time spent recreating something the platform would have given you natively.
Data gravity and ML ambitions
Data has gravity: once a large dataset lives in one cloud, the compute and the analytics tend to follow it there, because moving petabytes is slow and expensive. Decide early where your system of record and largest datasets will live, because that decision quietly pulls the rest of the architecture with it. If your roadmap is heavy on analytics, warehousing, and machine learning, weight the platform whose data and ML services your team will actually use — and confirm the specific services, not the category, because the gap is real at the leading edge and negligible for routine work.
Pricing models and the egress trap
Sticker prices for compute and storage are close enough across the three that they rarely decide anything. Two pricing dimensions matter far more:
- Commitment discounts. All three reward commitment heavily — reserved or committed-use pricing and savings plans can cut compute costs by a large margin versus on-demand. Your real rate depends on how much of your baseline you are willing to commit to, not the list price.
- Egress fees. Moving data out of a cloud, and between regions or providers, is billed and is easy to overlook. Egress is the mechanism that makes leaving expensive and makes casual multi-cloud costly. Model it before you commit, because it is the line item that surprises people at scale.
Right-sizing, commitment strategy, and egress modeling usually save more than platform choice ever will. That is a cloud infrastructure discipline, not a procurement event, and it is where most of the recoverable spend actually lives.
Compliance regions and data residency
If you have data-residency or sovereignty obligations, the question narrows fast to a boring but decisive one: does the provider operate a compliant region in the geography you need, with the certifications your auditors require? All three have broad global footprints and deep compliance portfolios, but coverage is not identical in every country, and the specific certifications available can differ region by region. For regulated workloads, confirm the exact region and the exact attestation before anything else — this is a constraint that can override every other preference on the list.
The skills your team already has
A platform your team can operate well beats a theoretically superior one they cannot. Deep expertise in one cloud is worth more than shallow familiarity with two. Be honest about where your engineers are strong today, what your hiring market looks like, and how much retraining you are actually willing to fund. The best cloud on paper is the wrong choice if it becomes an unstaffed platform nobody can run safely.
Why multi-cloud is a cost, not a default
"Use the best of each" sounds prudent and is usually a trap when adopted by reflex rather than requirement. Running two or three clouds means every operational function — identity, networking, security posture, observability, cost management — has to be done multiple times, in multiple dialects. You give up the deep managed services that make a single cloud productive, because they rarely port cleanly, and you add inter-cloud egress costs on top.
There are legitimate reasons to run more than one provider: resilience against a single-vendor failure, a genuine best-of-breed service only one cloud offers, avoiding lock-in where you can name the concrete risk, or an acquisition that arrived on a different platform. What is not a reason is a vague sense that concentration is dangerous. Adopt multi-cloud deliberately, with the platform and staffing to operate it consistently — or keep it as a planned hybrid cloud posture with clear boundaries — not by accident, one uncoordinated decision at a time. The most expensive estates are the ones that became multi-cloud without anyone deciding to.
A decision framework
Work through these in order. The first hard constraint usually settles most of it.
- Is there a compliance or residency constraint? If a workload requires a specific region or certification, filter to the providers that offer it. This can end the decision on its own.
- What does your existing stack pull toward? Heavy Microsoft 365, Active Directory, Entra, Windows, and SQL point strongly to Azure. Deep open-source and Linux experience lowers friction on AWS. A data-first roadmap often favors GCP. Name the head start before you spend it.
- What is the shape of the workload? Match it to the platform's genuine strengths — breadth and ecosystem, Microsoft integration, or data and ML depth.
- Where does your data live and want to grow? Put compute next to your largest datasets and let data gravity work for you, not against you.
- Model the real cost. Commitment discounts and egress, not sticker price. Include the operational cost of every additional platform you take on.
- Can your team operate it well? Score yourself honestly. Depth in one beats breadth across three almost every time.
If two clouds tie after this, pick the one your team knows best and commit. A decisive single-cloud choice you operate well will outperform a hedged multi-cloud estate you operate poorly.
Getting the choice right
The AWS-versus-Azure-versus-GCP question is rarely a technology decision. All three can run almost anything. It is a decision about fit — to your stack, your data, your compliance map, your budget model, and the people who have to run it at 2 a.m. Choose against those constraints, adopt only the complexity you can genuinely operate, and revisit the decision when the constraints change.
If you are weighing this choice, that assessment is where intSignal starts. Talk to our team about designing your public cloud strategy, or contact us to map your workloads, existing stack, and compliance obligations to the right platform before the decision hardens into five years of architecture.