Datarobot was the most recommended MLOps platform in our measurement, named in 5 of the 6 answers we collected (83%). We asked six buyer questions, let ChatGPT and Gemini answer each with one live web search on 6 August 2026, and recorded every product they named. Datarobot led the field, and no single platform was named in all six answers.
Behind Datarobot sat a six-way tie. ClearML, cnvrg.io, Databricks, Dataiku, Iguazio and Valohai were each named in 4 of the 6 answers (67%). So seven platforms cleared the two-thirds line, which means a buyer reading these answers does not get one obvious winner, they get a shortlist of about seven names to work through.
How the shortlist changed from question to question
The shortlist rewrote itself across the six questions. Only 7 platforms were named in 4 or more of the 6 answers, and no single answer contained all of them, so the list a buyer sees depends heavily on how they phrase the question.
Asked the plain question, "best mlops platforms," the two assistants returned 7 platforms in one answer: Databricks, Snowflake, Azure Machine Learning, ClearML, cnvrg.io, Dataiku and Datarobot. Three of those, Databricks, Snowflake and Azure Machine Learning, are the heavyweight cloud data platforms.
The moment the question mentioned money, those three heavyweights fell out. "Best free mlops platforms" and "most affordable mlops platforms" returned the exact same 8 platforms as each other: ClearML, cnvrg.io, Dataiku, Datarobot, Iguazio, SageMaker, Seldon and Valohai. The free list and the affordable list were word-for-word identical, and neither one included Databricks, Snowflake or Azure Machine Learning.
That swap is the real story of this category. Iguazio and Valohai were each named 4 times, yet never once for the bare "best mlops platforms" query, they only showed up when the question added "small teams," "free," "affordable" or "recommendations." Databricks, Snowflake and Azure Machine Learning ran the opposite way, named for "best" and absent from both money questions.
Two open-source standards behaved oddly. MLflow and Kubeflow were each named in 3 of the 6 answers, but only for "best mlops platforms for small teams," "what mlops platforms should I use" and "mlops platforms recommendations." Neither was named for the plain "best" question, and, despite both being free to run, neither was named for "best free mlops platforms" or "most affordable mlops platforms" either.
Two platforms surfaced in only one question each. Gradient was named once, only for "best mlops platforms for small teams." AWS SageMaker was named once, only for "mlops platforms recommendations." If you sell either, your whole presence in AI answers here rides on a single phrasing, and a buyer who types the question another way never sees you.
The source map behind the answers
The answers did not come from nowhere. Three domains fed all six answers: reddit.com, truefoundry.com and valohai.com each appeared as a source in 6 of the 6 answers. Community opinion and vendor roundups, not a neutral league table, are what these assistants read.
Community threads carried real weight. reddit.com was a source in all 6 answers and quora.com in 1, and github.com, mostly awesome-lists and project repos, fed 5 of the 6. The single most-cited well in this category is Reddit, present in every answer we collected.
Roundup and how-to blogs made up most of the rest: truefoundry.com (6 of 6), lakefs.io (4 of 6), digitalocean.com and hopsworks.ai (2 of 6 each), plus one appearance apiece from addepto.com, anaconda.com, apprecode.com, aws.amazon.com, comet.com, datacamp.com and io.net. These are companies publishing "best MLOps platforms" explainers, and the assistants lean on them.
Here is the insight a vendor should take from this. valohai.com fed 6 of the 6 answers and the product Valohai was named in 4 of 6. databricks.com fed 5 of 6 and Databricks was named in 4 of 6. In both cases the company's own domain fed the answer and the company's own product came out named, so writing the comparison page the assistant reads is a route into the answer, not a vanity page.
It is not automatic, though. truefoundry.com fed all 6 answers, yet TrueFoundry itself was named in 0 of them. Being read as a source and being picked as a product are two different outcomes, and the first does not buy you the second.
Review directories showed up, but lightly. g2.com was a source in 3 of the 6 answers, and Capterra did not appear at all. In this category a Reddit thread (6 of 6) fed twice as many answers as G2 (3 of 6), which is the reverse of what you would expect if AI simply echoed the big software directories.
What we would do to get named in this category
If we sold an MLOps platform, we would treat this as a source problem, not a market-share problem. Datarobot leads at 5 of 6 because it appears across the pages these assistants read, not because a model scored the market. The source list decides the answer.
In practice that means being present in the exact places that fed these six answers: the Reddit threads people actually read (reddit.com fed 6 of 6), the GitHub awesome-lists (5 of 6), and the roundup blogs (truefoundry.com 6 of 6, lakefs.io 4 of 6). And, as Valohai and Databricks show, publishing your own well-sourced comparison page can put your domain into the answer, since valohai.com and databricks.com fed 6 and 5 of the six answers.
The full list, counted
Counts are the number of the six answers each platform was named in, and share is that count out of six.
| Product | Named in | Share |
|---|---|---|
| Datarobot | 5 of 6 | 83% |
| ClearML | 4 of 6 | 67% |
| cnvrg.io | 4 of 6 | 67% |
| Databricks | 4 of 6 | 67% |
| Dataiku | 4 of 6 | 67% |
| Iguazio | 4 of 6 | 67% |
| Valohai | 4 of 6 | 67% |
| Azure Machine Learning | 3 of 6 | 50% |
| Kubeflow | 3 of 6 | 50% |
| MLflow | 3 of 6 | 50% |
| Snowflake | 3 of 6 | 50% |
| SageMaker | 2 of 6 | 33% |
| Seldon | 2 of 6 | 33% |
| AWS SageMaker | 1 of 6 | 17% |
| Gradient | 1 of 6 | 17% |
SageMaker appears on two lines because the assistants labelled it two ways, "SageMaker" in 2 of 6 answers and "AWS SageMaker" in 1 of 6. We keep them separate rather than combine them, because the answers never actually said "SageMaker, 3 of 6," and we are not going to invent a number the measurement did not produce.
Why Glotier is not on this list
One honest note to close. Glotier is not an MLOps platform, so it was named in none of the six answers, and that is the correct result. We do not sell in this category and we are not going to pretend we belong in it.
What you just read, though, is exactly what Glotier does, pointed at a category we do sell into: yours. We put the buyer questions to ChatGPT and Gemini, record which products get named across the six answers, and show you the source pages behind each name, the reddit.com, valohai.com and g2.com of your own market. The check is free, needs no account, and takes about a minute. If you sell an MLOps platform, the useful question is not that Datarobot led at 5 of 6, it is where your product sits and which of these sources you would need to be in to move.