Compare 45+ MLOps Tools in August 2025

Machine Learning Operations (MLOps) brings DevOps principles into machine learning to simplify workflows from model development to deployment and maintenance.

Explore the landscape of MLOps tools for different components of the ML lifecycle, such as:

• Data management tools
• Operationalization tools
• Modeling tools
• End-to-end MLOps platforms.

Expand Image

What are the types of MLOps solution providers?

Open source MLOps

Half of IT organizations use open-source tools for AI and ML and the figure is expected to be around two-thirds in 2023. On GitHub alone, there are 65 million developers and 3 million organizations contributing to 200 million projects.

Therefore, it is no surprise that there are advanced open-source toolkits in AI and ML landscape. Open-source tools concentrate on specific tasks within MLOps rather than providing end-to-end machine learning lifecycle management. These tools and platforms typically require a development environment in Python and R.

Startups that offer MLOps

Like open-source tools, most startups in the MLOps landscape provide tools for specific tasks within MLOps. Unlike open-source, startups tend to offer tools that target non-technical users.

Tech Giants that deliver MLOps

There are open-source tools developed by tech giants that address specific use cases in MLOps practices. However, the end-to-end MLOps solutions (or MLOps platforms) landscape is dominated by tech giants such as Google, Microsoft, or Alibaba.

What are the different types of MLOps tools?

MLOps tools typically fall into three categories:

• Data management
• Modeling
• Operationalization

There are also tools that can be considered as “MLOps platforms”, providing end-to-end machine learning lifecycle management.

We’ll explore tools for individual tasks within the major areas and MLOps platforms in turn.

Major data management solutions

Top data labeling tools

Data labeling tools (also called data annotation, tagging, or classification tools) are used to label large volumes of data such as texts, images, or audio. Labeled data is then used to train supervised machine learning algorithms in order to make predictions about new, unlabeled data. Some examples of data labeling tools include:

For more, check our article on how to choose a data labeling vendor. Also, don’t forget to check our data annotation services list.

Top data versioning

Data versioning (also called data version control) tools enable managing different versions of datasets and storing them in an accessible and well-organized way. This allows data science teams to gain insights such as identifying how data changes impact model performance and understanding how datasets are evolving.

Some popular data versioning tools are:

Modeling solutions

Top feature engineering tools

Feature engineering tools automate the process of extracting useful features from raw datasets to create better training data for machine learning models. These tools can accelerate the process of feature engineering for common applications and generic problems. However, it may be necessary to improve machine-generated feature engineering results using domain knowledge. Some feature engineering tools include:

Top experiment tracking tools

Developing machine learning projects involves running multiple experiments with different models, model parameters, or training data. Experiment tracking tools save all the necessary information about different experiments during model training. This allows tracking the versions of experiment components and the results and allows comparison between different experiments. Some examples of experiment tracking tools are:

Top hyperparameter optimization tools

Hyperparameters are the parameters of the machine learning models such as the size of a neural network or types of regularization that model developers can adjust to achieve different results. Hyperparameter tuning or optimization tools automate the process of searching and selecting hyperparameters that give optimal performance for machine learning models. Popular hyperparameter tuning tools include:

Top model versioning tools

Model versioning tools help data scientists manage different versions of ML models. Information such as model configuration, provenance data, hyperparameters, validation loss scores, and other metadata is stored in an easily accessible model registry. This metadata store helps data scientists quickly identify the configuration they used to build a particular model, ensuring that they don’t inadvertently use an incorrect or outdated model.

Model versioning systems also have mechanisms for capturing model outputs during training, providing a snapshot of how well a given model performed for each iteration. Versioning helps promote reproducibility, ensuring that published results can be verified in future iterations or investigations.

Some tools that enable model versioning are:

Operationalization solutions

Top model deployment / serving tools

Machine learning model deployment tools facilitate integrating ML models into a production environment to make predictions. Some tools in this category are:

Top model monitoring

Machine learning model monitoring is crucial for the success of ML projects, as model performance can decay over time due to changes in input data. Monitoring tools detect data and model drifts, or other anomalies, in real-time and trigger alerts based on performance metrics. This allows data scientists and ML engineers to take action, such as model retraining, to maintain its effectiveness.

Model monitoring tools include:

Shortlisted MLOps platforms

As mentioned above, there are also tools that cover the machine learning lifecycle end-to-end. These platforms are often provided by startups or tech giants but there’re also open-source platforms. Popular MLOps platforms include:

Explore leading MLOps platforms in our curated, data-backed selection to find the best fit for your ML needs.

Other categories related to MLOps

MLOps assistant tools

These tools are used to assist MLOps and LLMOps developers in specific aspects of MLOps and LLMOps deployment. These tools include:

• Feature stores:Feature stores serve as a centralized hub for storing, managing, and delivering ML features. They facilitate the discovery and sharing of feature values, supporting both model training and serving. Key features include the ability to create feature engineering pipelines, efficient feature serving, scalability, versioning, validation, metadata management, and integration with ML workflows for reproducibility.
• Integration frameworks: These frameworks help developing LLM applications such as document analyzers, code analyzers, chatbots etc.
• Vector databases (VD): Vector databases store complex, multi-dimensional data like patient records that combine symptoms, lab results, and behavioral patterns. VDs can search and retrieval unstructured data (like images, video, text, and audio) by content rather than labels or tags. VDs can help with model versioning and management in MLOps and LLMOps.

LLMOps

Large Language Models Operations is a specialized subset of machine learning operations (MLOps) tailored for the efficient development and deployment of Large Language Models (LLMs).

LLMOps ensures that the model quality remains high and that the data quality maintained throughout data science projects by providing infrastructure and tools.

LLMOps encompasses platforms and utilities for managing LLMs, from fine-tuning and evaluation to deployment and monitoring. Explore more on other LLMOps tools by checking out our data-driven market guide.

AI governance

AI governance establishes the frameworks and policies that shape how AI technologies are developed, deployed, and regulated. The key aim is to promote ethical AI practices and societal benefit while reducing risks like bias and unintended consequences.

AI governance is a crucial aspect of ML projects, which is why end-to-end MLOPs platforms offer AI governance capabilities. Discover other AI governance tools by reading our comprehensive market guide.

Expand Image

FAQs

If you still have questions about MLOps tools and vendors or artificial intelligence in general, we would like to help:

Principal Analyst

Cem Dilmegani

Principal Analyst

Follow On

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

View Full Profile

Comments 0

Share Your Thoughts

Your email address will not be published. All fields are required.

Name

Email Address

Comment

0/450

Post Comment