Top 9 AI Platforms in 2025 by Use Case and Business Size | Motion | Motion

2023 was a breakout year for generative AI. It finally went past newspaper headlines and started getting adopted in offices around the globe. In a McKinsey survey, 24% of C-suite executives said they regularly use GenAI for work.

This is partly a result of an explosion in the number of AI platforms available on the market. Gartner includes over 140+ AI platforms in its directory in 2025.

But among all these options, it can be hard to figure out the best platform to get started with. We’ll share our favorite AI platforms in 2025 and how you can find the right one for your company.

What is an AI platform?

An artificial intelligence platform is infrastructure and software that lets you build and maintain AI apps for your internal use cases. It helps you develop, deploy, test, and update machine learning or deep learning models and analyze the results.

How do they do this? By providing the entire environment, you need to run AI apps on cloud-based servers. AI platforms include:

This infrastructure allows you to train, tune, and deploy your model quickly. It also lets you get predictions from the live model and manage versions as you optimize them.

AI platforms are not the same as AI tools or apps with completed interfaces. You can’t just jump in and use AI for a specific use case, like automating task scheduling.

AI platforms allow you to build (or borrow open-source) and deploy custom algorithms to handle internal data.

What to look for in AI platforms in 2025

While no universal set of features makes “the best” AI platform, certain things are must-haves for any company looking to get started with AI in 2025.

Access to open-source libraries (or industry-leading commercial models)

You don't want to start from scratch unless you’re an AI researcher at a university or a giant corporation. Building an effective machine learning algorithm from scratch can take years and millions of dollars.

Instead, you want to use platforms that give you access to open-source libraries or frameworks.

So, one of the biggest things to consider is what models you get easy access to with the platform. Certain platforms have a unique advantage here:

Tools for segmenting and “productizing” your data

Just pouring your data into a model isn’t the ideal workflow for utilizing AI. Instead, you should start sorting and filtering the data to specific use cases.

Research from McKinsey shows that structuring your data into logical “data products” can help you deliver up to 90% faster new business use cases.

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And, of course, you can also save on computing costs by avoiding including irrelevant data. And computing costs are not insignificant. AI-related server computing costs are predicted to reach $76 billion by 2028.

Data governance and compliance tools

By the end of 2025, 40% of US citizens will be covered by privacy regulations similar to GDPR. And the global movement for privacy controls is only getting started.

So, with ever stricter privacy regulations globally, you need stringent data governance tools to remain compliant. The platform needs to give you visibility and control over your customer’s data.

(If you’re confused by any terms used in this article, check out our AI glossary.)

9 best AI platforms for 2025, by use case

Below, we’ve created a list based on the factors highlighted above — access to models, data segmentation, and governance tools — as well as pricing, customer reputation, and learning curve.

Best enterprise custom AI: Google Cloud Vertex AI

Vertex AI is Google Cloud’s enterprise AI platform, with robust machine learning operations tools. It also offers access to Gemini, Search, and other commercial AI models from Google. The Google AI sub-division of Google created it.

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It’s a good choice for companies with robust data science expertise. However, implementation can be challenging without the right know-how and experience. So it’s not suitable for small companies without that expertise.

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Best for custom generative AI apps: OpenAI API

OpenAI, best known for ChatGPT, launched an API (application programming interface) and developer platform in 2020. However, recently, with the increased power of ChatGPT models, it has become very popular.

Today, many SaaS apps use OpenAI’s API to use generative artificial intelligence to deliver a better experience.

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The OpenAI API is most suitable for SaaS or app developers who want to deliver GenAI functionality to their users. For example, this could be a virtual assistant that answers questions about the app.

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Best for using open-source frameworks: TensorFlow

TensorFlow is a machine learning platform that helps teams test and deploy various open-source models. The Google Brain team originally developed it for internal use.

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With its open-source models, robust training materials, and certification program, it’s suitable for aspiring ML students.

It’s also a good platform for teams that want to expand and improve machine learning models if you don’t want to rely on finished models from commercial providers like OpenAI.

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Best for small teams: Dataiku

Dataiku is a centralized AI platform with functionality like GenAI and AI-powered analytics. It offers no original AI models but allows you to leverage open-source models for various use cases.

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In addition, it offers pre-made applications powered by AI for use cases such as predictive analytics. It’s a solid option for smaller companies experimenting with open-source AI.

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Easiest to set up AI analytics: Alteryx

Alteryx is an AI platform that implements analytics across your entire business. It also offers a variety of tools for model training and model management.

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It’s a suitable option for teams that want to get started with AI analytics with limited up-front work.

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Best for managing multiple separate APIs: DataRobot

DataRobot is a platform that relies on various models and third-party APIs to help you leverage the power of AI. The main focus is on quickly building AI capabilities into internal software.

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It’s a good option if you have several separate use cases where one API or model is more suitable than another.

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Best for scalable solutions: Microsoft Azure AI

Microsoft Azure AI is an AI platform built on top of the cloud computing platform Microsoft Azure. It offers flexible scalability, robust data governance, and other enterprise features.

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It’s a suitable choice for large companies with a growing need for AI capabilities throughout their business. It also gives access to the ChatGPT model within a more robust platform.

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Best for integrated database management: SAP Hana Cloud

SAP Hana Cloud is SAP’s smart database management platform designed specifically for machine learning applications.

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It’s a solution built to store data uniquely through multi-model processing. It gives you the power of many traditional databases, like relational, document store, graph, geospatial, and vector.

This capability suits various AI use cases and large companies with big data sets. (It’s similar to other cloud platforms like Amazon Web Services in this sense.)

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Best for conversational AI use cases: watsonx (previously IBM Watson)

watsonx is IBM’s new and improved conversational AI, an update on IBM Watson. It offers an AI studio for building and deploying custom apps, governance tools, and a powerful virtual assistant platform.

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Before ChatGPT was all the rage in the news, IBM Watson was making the rounds. It’s been a capable conversational AI that has been useful for internal or customer chatbots for years. With watsonx, IBM has developed a more powerful solution for similar use cases.

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AI platforms are powerful tools in the hands of an experienced data science team. But for the average business owner who wants to use the power of AI, they’re often not the right choice.

Instead, AI-powered apps offer a faster (and cheaper) learning curve. For example, with Motion, you can start optimizing your schedules with AI by integrating with calendars you already use.

Our algorithm helps you automatically assign tasks based on availability, skill set, priority level, task dependencies, deadlines, and more.

It also automatically reshuffles your calendar based on new emergency tasks. For example, it can move low-priority tasks to a later date when an emergency meeting takes priority.

You can even automatically batch meetings to help you protect a time-blocked schedule.