AWS, Azure, GCP… or Something Smaller? What to choose?

High costs. Long training times. Tools that promise a lot but end up slowing your team or blocking progress. We hear this from teams all the time. The cloud you choose matters. It affects how quickly your team can test ideas, how much control they have, and how much of your budget gets used up before you go live.

Most teams start with the big three: AWS, Azure, or Google Cloud. They’re familiar, well-supported, and feature-rich. But when it comes to AI workloads, especially training or running large models, the trade-offs become more noticeable. And importantly, many of their pros and cons overlap.

Here’s what we’ve seen in real projects:

🟡 AWS. It’s flexible and widely adopted, offering everything from raw GPU, or Graphics Processing Unit, power to managed machine learning services. But long training jobs get expensive quickly. You can use discounted spot instances, but they may shut down without warning, interrupting progress and wasting time. Azure and GCP have the same challenge.

🟦 Azure. A strong choice if your company already uses Microsoft tools like Teams, Power BI, or Active Directory. It offers reliable enterprise support and good integration. But if your team wants flexibility or needs to experiment, Azure’s structure can feel limiting.

🔴 Google Cloud. Often picked by data science and research teams. It’s built around AI, with tools like Vertex AI and TensorFlow. But that power comes with complexity. If your product only uses AI as one feature, GCP might feel like more than you need.

🟢 CoreWeave, Hivenet, DigitalOcean. These smaller, GPU-first providers focus on performance and simplicity. If you just need compute power without extra setup or orchestration tools, they’re often faster and cheaper.

Questions to Ask Before You Decide:

• Are we building models or just running them?
• Do we need managed tools or just GPU access?
• Is predictable cost more important than full-service features?
• Are we tied to a platform already, or are we optimizing for speed?

Bottom line

There’s no single “best” cloud for AI. The right choice depends on your team’s needs, your workload, and how much flexibility you want. The important thing is to look beyond the biggest names and choose what fits your product and pace.