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Before we begin, we'd like to clarify that we're not a cloud-based AI provider. Our expertise lies in on-prem architecture and hybrid solutions tailored for business needs. But don't worry we care about small businesses who cannot afford expensive architecture. That's why we compiled a list of a major cloud-based AI providers, complete with their pros and cons, so you can make an informed decision.

Revision date: 16/06/2025

Executive Summary

This summary identifies and compares seven leading cloud AI providers including: AWS (Amazon Web Services), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, Oracle Cloud, NVIDIA, and Databricks.

Selection Criteria & Methodology

Providers were selected based on their compliance with regulations in EEA (European Economic Area) and North-America such as GDPR and HIPAA, their benchmarks, and access to leading AI models and infrastructure. Due to this reasons Chinese-based providers (e.g., Alibaba, Baidu, Tencent) were excluded since they introduce risks with area compliance.

Benchmark data was obtained from sources such as Artificial Analysis, LayerLens, and internal vendor publications. These benchmarks consist of:

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1. Microsoft Azure

We would choose it for: Azure OpenAI Service (GPT-4o, DALL·E), Azure AI Foundry, Cognitive Services, Azure ML, Phi-3 models.

In the beginning we need to say that Azure is a quite a powerfull cloud platform that offers just about everything. We're talking virtual machines, storage, AI tools, and DevOps services. If you’re already using Microsoft products like Office 365 or Windows Server, Azure might be your best choice because it integrates smoothly. Sorry for the mistake we meant smoother than the others.

Talking about hardware, Microsoft works with big names like Dell and HP to build custom servers, which are optimized for both Windows and Linux systems. Speed is generally solid, though it can vary a bit depending on the service and location.

You should know that with Azure’s hybrid cloud support—you can blend your existing on-premise systems with cloud infrastructure pretty seamlessly. Again we are emphasizing the word pretty.

Security is plus though since Microsoft has built-in protections, compliance with tons of industry standards, and services like Azure Sentinel to monitor threats in real-time.

With that being said we have to adress the downsides. The pricing model is messy and if you won't pay attention you’re about to pay quite an hefty ammount. Also the infrastructure is honestly too hard to natively understand so expect longer time until you get the things working as they are supposed to.

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2. Amazon Web Services (AWS)

We would choose it for: Amazon Bedrock (Claude, Llama), SageMaker, Inferentia/Trainium chips.

AWS is basically the giant of the cloud world. It was one of the first major players and still leads in size, services, and innovation (due to Claude by Anthropic). It offers a massive range of tools, from virtual servers to databases, AI, machine learning, IoT, and even satellite ground stations - if you for some weird purpose need that for AI integration in your company. If there’s something you want to do in the cloud, chances are AWS already has a service for it so don't worry.

Their servers are custom-designed by Amazon, and they use powerful, energy-efficient hardware that’s constantly being upgraded (including new NVIDIA Blackwell).

Security is also taken seriously, with tools for encryption, access control, and compliance already present.

However, it can feel overwhelming with so many services, settings, and options. And honestly the pricing is also not the most transparent part of it.

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3. Google Cloud Platform (GCP)

We would choose it for: Vertex AI, Gemini 2.5, TPUs, TensorFlow/Kubernetes support.

It’s "well" loved by us developers and data scientists because of its clean interface, some powerful data tools, and deep integration with open-source technologies. So, if you’re into machine learning or big data, GCP is hard to beat—think BigQuery, Vertex AI, and TensorFlow, all running natively.

Google also custom-build their servers, using efficient, high-performance hardware, which they’ve been pushing forward with their "own" custom chips like TPUs (Tensor Processing Units) for AI workloads.

Plus, it plays really well with Kubernetes. After all, Google created it so, containerized workloads run like a dream.

On the downside, GCP doesn’t have quite as many services or global regions, which can be limiting for certain use cases. It’s also not as tightly integrated with enterprise grade software.

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4. NVIDIA (AI Enterprise)

We would choose it for: DGX Cloud, H100/Blackwell GPUs, NGC catalog, NeMo framework.

Let's get to the king of AI - NVIDIA. It’s designed for AI deep learning, machine learning, data analytics, and even generative AI. The cool part is, you don't have to start from the scratch since they already have pretrained models ready for you. Plus it’s optimized for NVIDIA GPUs, so you get serious performance right out of the box.

The other major win is the support. It’s enterprise-grade, meaning you get stability, regular updates, and help when you need it, which is huge for businesses deploying AI at scale.

That being said, it’s not for hobbyists or small teams just experimenting with AI. Sadly not even for small businesses if they don't have IT sector, since NVIDIA's business efforts and strategy is very much aimed at serious enterprise use, with a license cost to match. You’ll also need the right infrastructure (i.e., NVIDIA-certified servers or GPUs) to take full advantage. And while it's powerful, there's a lot to learn if you're new to the NVIDIA ecosystem.

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5. Databricks

We would choose it for: Lakehouse Platform, Mosaic AI, Delta Lake, Spark integration.

It is like the dream workspace for data teams. Why? B-cause it brings together data engineering, data science, machine learning, and analytics in one unified platform. Built on top of Apache Spark (which the creators of Databricks helped invent), it’s designed to handle massive datasets fast and efficiently.

One of the coolest things is the Lakehouse architecture which combines the best of data lakes and data warehouses. So instead of jumping to separate systems for raw and structured data, you can manage everything in one place. It works well with tools like Delta Lake for reliable, versioned data storage and supports SQL, Python, R, Scala, etc.

So for better understanding it’s a favorite among companies doing serious AI/ML and real-time analytics.

On the downside its quite pricy.

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6. IBM Cloud

We would choose it for: Watsonx platform, Watson Assistant, hybrid cloud flexibility.

IBM Cloud is built with a focus on security, and compliance which makes it a good fit for industries like finance, healthcare, and government where those things really matter.

One standout feature is its support for hybrid and multi-cloud environments. With tools like Red Hat OpenShift and IBM Cloud Satellite, you can run workloads across your own data centers, IBM's cloud, or even other clouds like AWS and Azure while keeping control and consistency. Pretty cool right?

On the downside user interface and service catalog aren’t as slick or vast as what you'd find on other clouds, and the ecosystem is a bit more closed. Even though pricing is reasonable, especially for predictable workloads, it’s not as flexible or developer-centric as others.

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7. Oracle

We would choose it for: its own AI Platform, Autonomous Database, Digital Assistant.

New company or startup? Probably the worst choice, but for those already using Oracle it's a dream.

One of OCI’s (Oracle Cloud Infrastructure) big strengths is performance. It was designed from the ground up for high throughput, low latency, and predictable pricing. Their servers, block storage, and networking are fast and scalable, and they give you a lot of control over how things run.

What really sets Oracle apart is how seriously they take enterprise needs. Their infrastructure is built with security, isolation, and compliance in mind.

But now the downsides. Their services are pretty narrow when compared to others and if you are not with Oracle then expect quite a lot to learn to get at least even on your investment.

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If you found any inconsistencies in our report or you have new publicly available information please contact us here.