Breadth of Features and Scale Driving Leading ML/AI 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.

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