ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Machine Learning and Large Language Model Operations: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.
Artificial intelligence (AI) has continued to rise in prominence. ISG Market Lens Research shows that 79% of enterprises plan to increase their spending on AI technology and that the
ISG Research defines ML/LLMOps as the processes used to develop, deploy, monitor, manage and govern ML and LLMs. Developing and deploying AI models is a multistep process, beginning with collecting and curating the data that will be used to create the model. Once a model is developed and tuned using the training data, it needs to be tested to determine its accuracy and performance. Then the model needs to be applied in an operational application or process. For example, in a customer service application, a predictive AI model might make a recommendation for how a representative should respond to the customer’s situation. Similarly, a self-service customer application might use an LLM to provide a chatbot or guided experience to deliver those recommendations.
For many years, data scientists, data engineers and development operations (DevOps) teams needed to cobble together processes to support AI/ML models. Each team operated largely independently of the other, and there was no sense that the various steps were connected to one another. As AI grew in popularity and as models were updated more frequently to reflect changing market conditions, these ad hoc processes became an obstacle to scaling and governing AI. The notions of repeatability and automation had become common themes in DevOps. The AI community began to apply some of these same concepts to AI processes, eventually referring to them as machine learning operations or MLOps.
Data is flowing throughout these processes. Considerable time and effort are invested in preparing data to feed into predictive models. Feature engineering requires exploration and experimentation with the data. Once the features are identified, robust repeatable processes are needed to create data pipelines that feed these features into the models. In the case of generative AI (GenAI), data—often in the form of documents—feeds custom LLM development or fine-tuning. Additional data flows through the prompting process to direct LLMs to provide more specific and more accurate responses. Enterprises must govern these data flows to ensure compliance with internal policies and regulatory requirements. The regulatory environment is emerging and evolving, with the European Union passing the AI Act, the U.S. issuing and then rescinding an Executive Order on responsible development of AI, and dozens of U.S. states either enacting or proposing AI regulations.
The process does not conclude once a model is deployed. Enterprises need to monitor and maintain the models, ensuring they continue to be accurate and relevant as market conditions change. Realistically, it is only a matter of time before a model’s accuracy declines to the point where it can be replaced by another, more accurate model. The new model may simply be the result of retraining the old model on new data or it may be the result of using different modeling techniques. In either case, the models must be monitored constantly and updated as and when necessary. In the case of third-party LLMs, the providers are constantly updating and improving their models, so enterprises need to be prepared to deploy the newer models as well.
LLMs introduce additional operational concerns. Both prompts and responses must be monitored. In the case of prompts, enterprises need to ensure sensitive information or customers’ private data is not being shared with third parties. Prompts must also be monitored to prevent prompt injection to avoid hijacking. Responses must be monitored for toxicity and bias. Enterprises also want to be able to trace or explain the responses being generated to increase trust and understanding in the use of AI.
Software providers slowly recognized that the lack of ML/LLMOps tooling was inhibiting successful use of AI. Enterprises were left to their own devices to create scripts and
All of these capabilities are important to maximize the success of AI investments. As a result, our evaluation of AI software providers considers each of them. Our separate AI Platforms Buyers Guide includes a superset of AI functionality, including ML/LLMOps. Our Generative and Agentic AI Buyers Guide examines the subset of functionality to support the development and use of LLMs and agent processes. This Buyers Guide focuses on the ML/LLMOps functionality described above. While most software providers offer a combination of capabilities, many have evolved from one specific segment of the market or another. As a result, they tend to be more capable in the segment from which they originated. This Buyers Guide helps enterprises identify the relative strengths of providers in each segment of the market.
The ML/LLMOps segment of the market has been evolving rapidly to meet the needs of enterprises. Products may be incomplete in one area or another. Our previous Buyers Guide indicated that less than one-quarter of software providers fully met enterprise requirements for data governance or repeatability. Only 1 in 5 provided automatic documentation of models or had adequate approval processes to support new model deployment. As a result, enterprises should expect to supplement software-based ML/LLMOps with other processes to ensure they are meeting their internal and external compliance requirements. ISG Market Lens Research indicates that the most common thing enterprises would do differently is better coordination and governance of their AI implementations.
The ISG Buyers Guide™ for Machine Learning and Large Language Model Operations only evaluates software providers and products with specific ML/LLMOps support. The ML/LLMOps Buyers Guide uses portions the AI platform capability framework, and to be included in this Buyers Guide, products must include AI/ML pipelines, LLM fine-tuning processes, developer tooling, repeatability, monitoring, governance and deployment capabilities. The capability model evaluated includes: advanced model optimization, data preparation, developer and data scientist tooling, generative AI, MLOps, and types of AI/ML modeling. All of these capabilities are critical to ensure that enterprises can operationalize and rely upon the models that are produced in their AI processes.
This research evaluates the following software providers that offer products that address key elements of MLOps and LLMOps as we define it: Alibaba Cloud, Altair, Alteryx, Anaconda, AWS, C3 AI, Cloudera, Databricks, Dataiku, DataRobot, Domino Data Lab, Google Cloud, H2O.ai, Huawei Cloud, Hugging Face, IBM, MathWorks, Microsoft, NVIDIA, Oracle, Palantir, Quantexa, Red Hat, Salesforce, SAP, SAS, Snowflake, Teradata and Weights & Biases.
This research-based index evaluates the full business and information technology value of machine learning and large language model operations software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.
We urge organizations to do a thorough job of evaluating machine learning and large language model operations offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.
The Buyers Guide for Machine Learning and Large Language Model Operations in 2025 finds Oracle first on the list, followed by AWS and Databricks.
Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.
The Leaders in Product Experience are:
• Oracle.
• AWS.
• Microsoft.
The Leaders in Customer Experience are:
• Databricks.
• Oracle.
• Google Cloud.
The Leaders across any of the seven categories are:
• Oracle, which has achieved this rating in seven of the seven categories.
• Databricks in four categories.
• Google Cloud and Microsoft in three categories.
• AWS in two categories.
• Dataiku and Teradata in one category.
The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.
Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle machine learning and large language model operations, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.
ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our machine learning and large language model operations blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.
You can find more details on our community as well as on our expertise in the research for this Buyers Guide.