MLOps Platforms
MLOps platforms provide end-to-end machine learning lifecycle management. +Show More
| Products | Position | Customer satisfaction | |||
|---|---|---|---|---|---|
DataRobot |
Leader
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Satisfactory
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DataRobot's automated machine learning platform makes it fast and easy to build and deploy accurate predictive models.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
40-50 case studies
Company's number of employees
1k-2k employees
Company's social media followers
100k-1m followers
Total funding
$1-5bn
# of funding rounds
11
Latest funding date
June 27, 2021
Last funding amount
$250-500m
Company
Type of company
private
Founding year
2012
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Dataiku |
Leader
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Satisfactory
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Dataiku's single, collaborative platform powers both self-service analytics and the operationalization of machine learning models in production.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
50-100 case studies
Company's number of employees
1k-2k employees
Company's social media followers
100k-1m followers
Total funding
$1-1bn
# of funding rounds
11
Latest funding date
December 12, 2022
Last funding amount
$100-250m
Company
Type of company
private
Founding year
2013
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Cloudera |
Leader
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Satisfactory
|
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At Cloudera, we believe data can make what is impossible today, possible tomorrow. We deliver an enterprise data cloud for any data, anywhere, from the Edge to AI. We enable people to transform vast amounts of complex data into clear and actionable insights to enhance their businesses and exceed their expectations. Cloudera is leading hospitals to better cancer cures, securing financial institutions against fraud and cyber-crime, and helping humans arrive on Mars — and beyond. Powered by the relentless innovation of the open-source community, Cloudera advances digital transformation for the world’s largest enterprises
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
200-300 case studies
Company's number of employees
3k-4k employees
Company's social media followers
100k-1m followers
Company
Type of company
private
Founding year
2020
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Amazon SageMaker |
Leader
|
Satisfactory
|
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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models. Amazon SageMaker also allows for deep learning models on Amazon EC2 P3 instances, i.e. GPU instances available in the cloud. These instances come equipped with up to 8 V100 Tensor Core GPUs.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
10-20 case studies
Company's number of employees
100k-1m employees
Company's social media followers
10m-20m followers
Company
Type of company
private
Founding year
1996
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H2O |
Leader
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Satisfactory
|
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H2O.ai is the leading AI Cloud company, on a mission to democratize AI and drive an open AI movement around the world. They focus on drawing insights from structured and unstructured data like video and documents with their award-winning products like Hydrogen Torch and Document AI. Customers use the H2O AI Cloud to rapidly solve complex business problems and accelerate the discovery of new ideas. H2O.ai is the trusted AI provider to more than 20,000 global organizations, millions of data scientists and over half of the Fortune 500, including AT&T, Commonwealth Bank of Australia, Citi, GlaxoSmithKline, Hitachi, Kaiser Permanente, Procter & Gamble, PayPal, PwC, Reckitt, Unilever, Goldman Sachs, NVIDIA, and Wells Fargo are not only customers and partners, but strategic investors in the company. More than 30 Kaggle Grandmasters (the community of best-in-the-world machine learning practitioners and data scientists) are makers at H2O.ai. A strong AI for Good ethos to make the world a better place and Responsible AI drive the company’s purpose. Please join our movement at www.h2o.ai. H2O.ai offers enterprise customers with multiple platforms for AI and machine learning, including the open source distributed machine learning platform H2O-3, automatic machine learning platform H2O Driverless AI, and the recently announced H2O Q, an AI platform for business users: H2O-3 is an open source, scalable and distributed in-memory AI and machine learning platform. H2O-3 also has a strong AutoML functionality and supports the most widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning, XGBoost and more. H2O Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish tasks quickly with automatic feature engineering, model tuning, model tuning, model selection, model validation and machine learning interpretability, custom recipes, time-series and automatic deployment pipeline generation for model scoring. H2O Q is a new AI platform that provides the essential building blocks to make AI apps and will bring the power of AI to millions of business users. It delivers automatic insights and predictions for “in the moment” business questions and is ideal for data analysts, citizen data scientists and all business users.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
300-400 employees
Company's social media followers
100k-1m followers
Total funding
$250-500m
# of funding rounds
9
Latest funding date
May 12, 2023
Company
Type of company
private
Founding year
2012
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Valohai |
Challenger
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Satisfactory
|
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A deep learning platform for scalable infrastructure, version control and team management.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
10-20 case studies
Company's number of employees
20-30 employees
Company's social media followers
5k-10k followers
Total funding
$1-5m
# of funding rounds
2
Latest funding date
October 1, 2019
Company
Type of company
private
Founding year
2016
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ClearML |
Challenger
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Satisfactory
|
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The key features of ClearML’s open source, end-to-end MLOPs Platform are: ClearML Experiment – ClearML Experiment allows you to track every part of the ML experimentation process and automate tasks. With it, you can log, share and version all experiments and instantly orchestrate pipelines. ClearML Orchestrate – With ClearML Orchestrate DevOps and data scientists are empowered through autonomy and control over compute resources. The cloud native solution also enables kubernetes and bare-metal resource scheduling with a simple and unified interface to control costs and workloads. ClearML DataOps – ClearML DataOps delivers data store automation. Automate data collection into searchable, accessible, and ML-ready data repositories through cutting-edge MLOps technology. ClearML Hyper-Datasets – ClearML Hyper-Datasets allows MLOps teams to build data-centric AI workflows. Make the most out of unstructured-data using queryable datasets, made possible through ClearML Hyper-Datasets. ClearML Deploy – ClearML Deploy delivers a unifying model repository, custom pipelines, and model serving. This allows MLOps teams to Transition from model development to production and gain full workflow visibility with seamless integration to the experiment manager and orchestration. Every component of ClearML integrates seamlessly with each other, delivering cross-department visibility in research, development, and production.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
20-30 employees
Company's social media followers
100k-1m followers
Company
Type of company
private
Founding year
2016
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Iguazio |
Challenger
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N/A
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It's finally simple to deliver intelligent applications without managing infrastructure.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
5-10 case studies
Company's number of employees
50-100 employees
Company's social media followers
10k-20k followers
Total funding
$50-100m
# of funding rounds
4
Latest funding date
January 27, 2020
Last funding amount
$10-50m
Company
Type of company
private
Founding year
2014
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Alibaba Machine Learning Platform |
Challenger
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N/A
|
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Machine Learning Platform For AI provides end-to-end machine learning services
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
4k-5k employees
Company's social media followers
100k-1m followers
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Polyaxon |
Challenger
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N/A
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An enterprise-grade platform for agile, reproducible, and scalable machine learning.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfactionMarket presence
Company's social media followers
100-200 followers
Company
Type of company
private
Founding year
2018
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“-”: AIMultiple team has not yet verified that vendor provides the specified feature. AIMultiple team focuses on feature verification for top 10 vendors.
Sources
AIMultiple uses these data sources for ranking solutions and awarding badges in MLOps platforms:
MLOps Leaders
According to the weighted combination of 4 metrics
What are MLOps
customer satisfaction leaders?
Taking into account the latest metrics outlined below, these are the current MLOps customer satisfaction leaders:
Which MLOps solution provides the most customer satisfaction?
AIMultiple uses product and service reviews from multiple review platforms in determining customer satisfaction.
While deciding a product's level of customer satisfaction, AIMultiple takes into account its number of reviews, how reviewers rate it and the recency of reviews.
- Number of reviews is important because it is easier to get a small number of high ratings than a high number of them.
- Recency is important as products are always evolving.
- Reviews older than 5 years are not taken into consideration
- older than 12 months have reduced impact in average ratings in line with their date of publishing.
What are MLOps
market leaders?
Taking into account the latest metrics outlined below, these are the current MLOps market leaders:
Which one has collected the most reviews?
AIMultiple uses multiple datapoints in identifying market leaders:
- Product line revenue (when available)
- Number of reviews
- Number of case studies
- Number and experience of employees
- Social media presence and engagement
What are the most mature MLOps platforms?
Which one has the most employees?
Which MLOps companies have the most employees?
1000 employees work for a typical company in this solution category which is 977 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 9 companies with >10 employees are offering mlops platforms. Top 3 products are developed by companies with a total of 100k employees. The largest company in this domain is AWS with more than 100,000 employees. AWS provides the MLOps solution: Amazon SageMaker
Insights
What are the most common words describing MLOps platforms?
This data is collected from customer reviews for all MLOps companies. The most positive word describing MLOps platforms is “Easy to use” that is used in 12% of the reviews. The most negative one is “Expensive” with which is used in 3% of all the MLOps reviews.
What is the average customer size?
According to customer reviews, most common company size for MLOps customers is 1,001+ employees. Customers with 1,001+ employees make up 40% of MLOps customers. For an average Machine Learning solution, customers with 1,001+ employees make up 34% of total customers.
Customer Evaluation
These scores are the average scores collected from customer reviews for all MLOps platforms. MLOps Platforms are most positively evaluated in terms of "Overall" but falls behind in "Likelihood to Recommend".
What are the benefits of MLOps?
The most commonly cited benefits of MLOps are:
- Time saving
- Cost saving
- Enhanced collaboration
- Improved data quality
- Improved data collection
- Scalability
- Improved customer experience
- Increased security
- Increased privacy
- Improved compliance
- Downtime reduction
Discover all MLOps benefits
Where are MLOps vendors' HQs located?
Trends
What is the level of interest in MLOps platforms?
This category was searched on average for 1.1k times per month on search engines in 2024. This number has decreased to 0 in 2025. If we compare with other machine learning solutions, a typical solution was searched 3.6k times in 2024 and this decreased to 0 in 2025.