Breadth of Features and Scale Driving Leading ML/AI Vendors

Written by Daren Brabham | May 20, 2025 1:04:38 PM

This report focuses on ML/AI platforms, with data on the following vendors:

While many organizations have long explored ML/AI for various business use cases, often starting with open-source programming languages like R and Python and open-source tools like Jupyter Notebook and TensorFlow, today, the most mature-and-established ML/AI applications leverage robust platforms to manage at an enterprise scale. As ML/AI programs evolve, reproducibility and reuse of ML models become important, as well as all of the governance and data integration activities and monitoring that are part of an end-to-end MLOps frameworks. There are some vendors, too, that offer turnkey, business user-friendly solutions focused on “democratizing” data science, allowing organizations to ramp up ML/AI efforts quickly. Generative AI also looms large, fully arriving in the zeitgeist with OpenAI’s public release of ChatGPT in late 2022. Thanks to these recent innovations in generative AI and large language models (LLMs), AI has become even more accessible, and its applicability even more apparent for a range of use cases. Most organizations are now exploring possible avenues for ML/AI, and vendors in a variety of sectors – from security to robotic process automation, to enterprise applications – are weaving AI capabilities into their core product offerings.

The ML/AI market reflects this diversity of maturity and use cases, with classes of vendors that speak to the needs of different organizations. Large public cloud platforms like Microsoft, AWS, and Google offer full MLOps capabilities for enterprise-scale data science programs, competing alongside other popular end-to-end offerings like Oracle and IBM, and open-source packages like TensorFlow and Anaconda. Another class of tools aims to simplify the complexity of data science work by offering business-user-friendly products like pre-trained ML models to speed time to business value, such as DataRobot, C3.ai, H2O.ai, and Hugging Face. Still, others have broadened their appeal as ML/AI platforms by focusing earlier in the data and analytics pipeline, such as Databricks with its popular data lakehouse paradigm for data management; Snowflake with its solid data warehousing foundation; and Dataiku with its finesse as a data preparation tool. Across enterprise, we see varied spending and utilization on these many ML/AI vendors, with the more robust MLOps offerings occupying leading positions.

It is important to note that the ML/AI market is a complicated and extremely fast-moving space. In the past two years, the generative AI boom has supercharged the ML/AI race with new generative AI use cases and LLMs. Foundational AI models have proliferated, and nearly every enterprise tech product across every domain has rolled out new AI features and capabilities. Certainly, too, the market is flooded with AI-focused branding, a reorientation around new interest in these cutting-edge technologies. ETR closely tracks these developments in generative AI and LLMs with our AI Product Series, a six-times-per-year survey tracking spending, utilization, and perceived value for dozens of AI tools and features, including foundational AI models and AI features embedded in broader products. Thus, foundational models and specific AI features embedded in other tools have been excluded from the present study. This Observatory for ML/AI instead focuses on more robust, end-to-end, complete platforms capable of orchestrating ML/AI programs at enterprise scale. Or, at the very least, contained tool packages that some organizations, especially small and midsize firms, may deploy to tackle complete ML/AI use cases. Although many open-source ML/AI tools are widely used in enterprises, they have been excluded from this study in favor of paid tools and products that have clearer sales motions as “freemium” offerings with significant spending intentions data.

Dataiku, Hugging Face, Databricks, Snowflake, and Major Public Cloud Platforms Lead Key Spending and Usage Measures

The ETR Observatory for ML/AI surveyed 314 IT decision makers. Most (63%) represent Large enterprises of more than 1,200 employees, with more than a fifth (22%) at Fortune 500 firms and nearly a third (31%) at Global 2000 enterprises. The three most representative industry verticals are Services/Consulting, IT/TelCo, and Financials/Insurance, collectively comprising more than half (55%) of the sample. Almost three-quarters (72%) of respondents are in North America and 20% are in EMEA, with the remainder representing APAC (8%) and Latin American (<1%) regions. About half (51%) of respondents hold VP or Director-level titles, and the remainder are split between C-level roles (31%) and practitioner roles (18%).

The report categorizes vendors across different categories, reflecting their Momentum and Presence within the ML/AI space:

Leaders like Microsoft Azure Machine Learning, Google Vertex AI, Snowflake ML, and Amazon SageMaker show strong adoption and market share, driven by broad capabilities and ease of use.
Sole Advancing vendor Databricks Mosaic AI is gaining Momentum but still lags in Presence compared to market leaders.
Tracking vendors Hugging Face, TensorFlow, and Anaconda are long-established names in enterprise tech stacks, with stronger Presence but relatively lower Momentum.
Pursuing Vendors, including IBM watsonx, Dataiku, and Cloudera AI, are experiencing slower growth, with less impact in the market.

Public cloud giants Microsoft and Google sit atop the ML/AI market in spending plans, with Microsoft Azure Machine Learning posting a Net Score of 83% and Google Vertex AI with a Net Score of 76%. Net Scores are a snapshot of positive spending plans (Adoption and Increase indications) minus negative spending plans (Replacement and Decrease indications). Databricks Mosaic AI (67%), Dataiku (63%), Snowflake ML (62%), and Amazon SageMaker (60%) follow with Net Scores in the low and mid-60% range. On the lower end, SAS and DataRobot each have a 24% Net Score, and Cloudera AI stands at 35%. Perhaps a testament to the widespread enthusiasm for all-things AI in the past few years since the public release of OpenAI’s ChatGPT, few vendors have any replacement indications. Just SAS, DataRobot, IBM watsonx, and TensorFlow have any replacement plans, and all are in low single digits (N=2 for SAS and N=1 for the other three).

In addition to strong spending intentions, high usage, and relatively high levels of stickiness, the three major public cloud platforms – along with Dataiku – rank high in expected return on investment (ROI) (see Figure 3). Three-quarters (76%) of respondents expect ROI within the first three years for Google Vertex AI. Microsoft Azure Machine Learning (73%), Dataiku (72%), Amazon SageMaker (72%), and Snowflake ML (70%) also have high rates of ROI within the first three years. Behind Snowflake ML in rank order is Hugging Face, where two-thirds (67%) of respondents expect ROI within three years. On Dataiku, one Head of Data and AI Transformation for a global insurance enterprise explained that though Dataiku’s “cost is quite expensive for the moment,” the tool is valuable because “you can boost your productivity, and you can more easily build your data pipelines and data flows,” which means “you can reduce your IT development spending.”

Major Public Cloud Vendors Dominate Desired and Innovative Vendor List

Organizations invest in ML/AI platforms for a variety of reasons, and often they invest in more than one. Understanding which vendors are seen as the innovators in the market sheds light on where the market may be going in terms of its technical features, while knowing which vendors are the most desired for centering in a tech stack speaks more to deeper strategic needs for organizations as they evolve with the market. In the Observatory study, we ask respondents to provide open-ended answers to which vendor they view as the most innovative, and which vendor they could most prioritize if given the opportunity to rebuild their ML/AI program. Analyzing these open-ended responses shows that Microsoft is seen as both most desired and most innovative, and Google and Amazon occupy the next two spots on both analyses. Amazon and Google are tied for second-most desired vendor, but lag Microsoft by a wide margin.

Among many attributes, what makes a product desired is a tool’s completeness or ability to do what is expected of it, as well as its ability to integrate with an organization’s existing technical ecosystem. Appropriately, then, these top desired vendors rank high in the individual vendor strengths analysis in their rates of agreement with the statements “this product does everything I expect an ML/AI tool to do” (see Figure 5) and “this product integrates easily with our existing ecosystem.” Microsoft Azure Machine Learning, Amazon SageMaker, and Google Vertex AI, in order, have the highest rates of agreement in doing everything expected of it as an ML/AI tool, and Microsoft Azure Machine Learning and Amazon SageMaker hold the top two spots in ease of integration with an existing ecosystem. Snowflake ML is tied with Amazon SageMaker in the second rank in terms of integration with existing ecosystem.

In the innovative vendor analysis, Google edges out Amazon for second place, and the margin is narrower between them and leader Microsoft. Likewise, in the individual vendor strengths analysis, Microsoft Azure Machine Learning, Google Vertex AI, and Amazon SageMaker, in order, have the highest rates of agreement to the statement “this product has an innovative technical roadmap.”

Conclusion – ML/AI Tools Suited to Different Organizational Needs

As with any enterprise IT tool market, ML/AI tools have evolved to fit the needs of a variety of organizations that are all maturing their advanced analytics initiatives at different rates. What is clear, however, is that the early stages of exploration in ML/AI have, for many organizations – and especially larger enterprises – reached a point of widespread enterprise-scale adoption, requiring robust end-to-end data architectures and sophisticated management and governance programs. Smaller organizations or those just now dipping their toes in the waters of data science are finding early value in free, open-source offerings or tools with pre-built, pre-trained ML models ready to accelerate time to business value. With the broad awareness of generative AI technology in the last two years, too, vendors and organizations alike are scrambling to imagine new possibilities with AI to leverage the power of LLMs.

To respond to these organizational stages, the ML/AI vendor landscape is dotted with big players offering full MLOps capabilities for mature data science operations, with large public cloud players like Microsoft, AWS, and Google leading the way alongside open-source stalwarts TensorFlow and Anaconda and popular open libraries and languages like Jupyter Notebook, Python, and R. Other vendors have built reputations for strength in particular areas, like Databricks, Snowflake, and Dataiku, which offer sophisticated data management setups that feed data science use cases as well as more line-of-business-focused reporting and self-service business intelligence use cases. A separate set of vendors aim for democratizing data science, with off-the-shelf, turnkey ML solutions that are alluring to business executives and less technically savvy business users.

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