AWS vs Google Cloud vs Azure: Which Cloud Provider Should You Choose?
The honest starting point for this comparison is that AWS, Google Cloud, and Azure can all run essentially the same workload. Compute, storage, managed databases, container orchestration, serverless functions — the three providers all offer credible versions of each. If a feature comparison chart were the whole story, this would be a much shorter article. The real differences that actually affect a team are elsewhere.
AWS: the largest ecosystem, and the cost of that breadth
AWS launched first and has the largest service catalog by a wide margin, along with the largest pool of engineers who already know it, the deepest third-party tooling ecosystem, and documentation for nearly every edge case you'll hit. That breadth is also its biggest practical downside: AWS has an enormous number of services with overlapping purposes, and its console and pricing model are widely considered the least approachable of the three, especially for a team without a dedicated cloud specialist. AWS tends to be the safe, defensible choice — nobody gets questioned for picking it — but "safe" and "simple" aren't the same thing.
Google Cloud: strongest where Google's own engineering is strongest
Google Cloud's strongest ground is data and machine learning infrastructure — BigQuery for analytics, and a genuinely strong container story, since Google is where Kubernetes originated and its managed Kubernetes offering reflects that lineage. Its console and developer experience are generally considered more approachable than AWS's. The tradeoff is a smaller ecosystem and a noticeably smaller hiring pool of engineers with deep, specific GCP experience compared to AWS.
Azure: the default where Microsoft already runs the business
Azure's clearest advantage shows up in organizations already built on Microsoft's stack — Active Directory, Office 365, .NET — where the integration between Azure and the rest of the Microsoft ecosystem is deep and genuinely saves real engineering time. Outside of that context, Azure's independent advantage is less pronounced, though its enterprise sales relationships and compliance certifications make it a common default in larger, already-Microsoft-heavy organizations.
What actually should decide this for you
- What your team already knows. The productivity difference between an engineer who's used a platform for years and one learning it from scratch dwarfs any difference between the platforms themselves.
- What you're already using. Heavy Microsoft shop → Azure has a real advantage. Heavy data/ML workload → Google Cloud's tooling is genuinely strong there. Starting from nothing → AWS's documentation and hiring pool reduce risk.
- Pricing structure, not headline rate. All three are roughly comparable on raw compute cost; the real difference is in data egress fees, reserved-instance discount structures, and how easy it is to accidentally leave something expensive running. Get an estimate for your actual expected workload rather than comparing list prices.
- Multi-cloud is a real cost, not a free insurance policy. Running on more than one provider sounds prudent, but it means learning and maintaining two sets of tooling, IAM models, and networking setups. It's rarely worth it for a team that isn't already at a scale where a single provider's rare regional outage would be genuinely catastrophic.
For most teams building a new product, the right answer is less "which cloud is objectively best" and more "which cloud will my team be productive on fastest, and which one fits what we're already committed to." That's a much easier question to actually answer than the marketing pages make it sound.
We work across all three at Burncode, and part of the cloud infrastructure work we do with new clients is exactly this — helping a team choose (or migrate to) the provider that actually fits their stack and their team, then setting it up properly rather than accumulating configuration nobody fully understands.