--- title: DVC Reviews meta_title: 'DVC Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 11 reviews by the users' company size, role or industry to find out how DVC works for a business like yours. aggregate_rating: rating_value: 4.7 review_count: 11 scale: '5' date_modified: '2026-06-04' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # DVC Reviews **Vendor:** Iterative **Category:** [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) **Average Rating:** 4.7/5.0 **Total Reviews:** 11 ## About DVC DVC is an open-source, Git-based data science tool. Apply version control to machine learning development, make your repo the backbone of your project, and instill best practices across your team. Find more info at: https://dvc.org DVC has introduced a new open source tool for AI Data Analytics and Actionable GenAI Insights, that can curate vast amounts of unstructured data. Find out more at https://github.com/iterative/datachain or https:/datachain.ai DVC Studio is our SaaS tool that provides seamless data and model management, experiment tracking, visualization, and automation, with Git and DVC by as your single source of truth. Find more info at: https://studio.iterative.ai We have an ever-growing community of practitioners! Join the community in our Discord server at the link below to get your questions answered or take our free online course: https://learn.iterative.ai ## DVC Reviews ### 1. Great support from DVC team; flexible and very helpful tool **Rating:** 4.5/5.0 stars **Reviewed by:** Marcelle S. | Small-Business (50 or fewer emp.) **Reviewed Date:** September 13, 2023 **What do you like best about DVC?** DVC allowed me to have an overview of my results, with plots and tracking the metadata. This improves and speeds up the research process, allowing reproducibility of the results and better team work. **What do you dislike about DVC?** The tool needs some basic knowledge of coding (python), so can be a bit challenging to start. Also, some conflicts with previous versions may cause errors - which are rapidly solved by the DVC support team. **What problems is DVC solving and how is that benefiting you?** I use DVC to track my molecular simulations. DVC allows me to track the parameters I used to create the simulation, analyze and compare results quickly, not to mention the ability to restart the simulation procedure from a specific point in time, without the need to re-run all steps. It improves my research by allowing reproducibility and I can easily share my results with co-workers via Github. ### 2. If you like the unix and open source philosophy, then with dvc you will feel home **Rating:** 5.0/5.0 stars **Reviewed by:** Francesco C. | Co-Founder, Enterprise (> 1000 emp.) **Reviewed Date:** August 09, 2023 **What do you like best about DVC?** I like that they follow the UNIX philosophy quite closely, they have an amazing comunity, always there to answer your questions. I also find amazing the open source culture they cultivate, and the active role they play in the ML community, sometimes even supporting economically Data Science events. Finally, I like how dvc plays together with git, making GitOps for ML a reality. For me personally, I especially like the modularity of their products, that allow me to "hack them together" to my will. **What do you dislike about DVC?** I encountered some occasional problems. Most of the time was actually my fault, didn't read the documentation carefully enough. Sometimes I had more serious issues that needed a resolution, but the dev team was very quick to fix them and provide me with solutions in short time. **What problems is DVC solving and how is that benefiting you?** I use dvc to track the execution of ML pipelines and dvc+gto to store the artifacts in a registry. I use the VSCode extension to explore the experiments we run. ### 3. DVC is an essential tool for anyone who wants to develop structured ML projects **Rating:** 4.5/5.0 stars **Reviewed by:** Michael G. | Small-Business (50 or fewer emp.) **Reviewed Date:** August 10, 2023 **What do you like best about DVC?** What I like most is that it fills a gap that no other tool out there does. It provides a way to version machine learning projects. **What do you dislike about DVC?** The learning curve of mixing Git and DVC can be a bit hard. **What problems is DVC solving and how is that benefiting you?** DVC has enabled organized collaboration and reproducibility between my (remote) team members. ### 4. A great tool for data modeling **Rating:** 4.0/5.0 stars **Reviewed by:** Sami J. | Mid-Market (51-1000 emp.) **Reviewed Date:** July 31, 2023 **What do you like best about DVC?** A great framework for structuring pipelines that are meant to be run locally and quickly: lightweight, local-friendly and GitOps philosophy, can get a lot of value with zero code instrumentation. **What do you dislike about DVC?** Considerations of data processing at scale (parallel, distributed, remote, etc.) mostly absent **What problems is DVC solving and how is that benefiting you?** Versioning of data and pipelines for reproducible data analysis and modeling ### 5. Manage your data like a pro! **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** April 11, 2022 **What do you like best about DVC?** It makes the models I create using python so much more accessible and sharable , It also intuitively tracks ML model evolution beautifully. Also, Data management is on another level. **What do you dislike about DVC?** Trial Period is inadequate for learning the walkthrough for the app and then using and applying it further. Sometimes connecting to Git showed some error but was fixed almost in 5 minutes. **What problems is DVC solving and how is that benefiting you?** Version Control of Multiple ML models and creating asynchronous data timeline for the models individually. ML experiment management is also another major of my personal uses ### 6. A gamechanger for managing huge amounts of data using Git best practises. **Rating:** 5.0/5.0 stars **Reviewed by:** Akash D. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** October 01, 2021 **What do you like best about DVC?** 1. It is the best open-source tool to manage data. 2. It allows us to version our data 3. Incorporates all git best practices 4. Supports all major cloud provider's storage solutions (eg azs s3, azure blob storage, gcp bucket). 5. Supports almost all data types. **What do you dislike about DVC?** 1. Default settings/config is good but to design the best/optimal config have to do some work. 2. Initial onboarding and initial learning is required. 3. Does not have a UI or plig n play kind of functionality. **Recommendations to others considering DVC:** If you have to manage a humongous amount of data and are also looking to version control them, then there is no better tool than DVC. It also has excellent pipeline management capabilities. **What problems is DVC solving and how is that benefiting you?** 1. Version control for all the data. 2. Cental control over the data. 3. Manage data pipeline. ### 7. How to version control for your ML experiment? **Rating:** 5.0/5.0 stars **Reviewed by:** KIM D. | Autonomous Driving Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** December 30, 2021 **What do you like best about DVC?** Git like operation via command line. Staged manageable ML pipeline. **What do you dislike about DVC?** Command line was not fully integrated with Git yet. Unclear 1 to 1 correspondence across files and stored formats **Recommendations to others considering DVC:** If you wanna start quick ML pipeline tasks, DVC might be a good fit for now! **What problems is DVC solving and how is that benefiting you?** ML pipeline/test management via programmable scripts for automated way. ### 8. DVC tool **Rating:** 5.0/5.0 stars **Reviewed by:** Amar M. | Principal Architect, Enterprise (> 1000 emp.) **Reviewed Date:** November 30, 2021 **What do you like best about DVC?** Model versioning for MLOps. its a great tool for model life cycle management **What do you dislike about DVC?** less number of User Interfaces and low adoption as compared to MLFlow and other DevOps tools of hyper scalers **Recommendations to others considering DVC:** model version tracking and also dataops which is data used in models **What problems is DVC solving and how is that benefiting you?** MLOps and Machine learning life cycle management and models version tracking and control ### 9. A nifty tool to keep track of models and data **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Logistics and Supply Chain | Small-Business (50 or fewer emp.) **Reviewed Date:** October 29, 2021 **What do you like best about DVC?** I like that that model and data versioning can be done along with code versioning on git. **What do you dislike about DVC?** There's not enough visibility. Tutorials on how to use more advanced functionalities could be useful. **What problems is DVC solving and how is that benefiting you?** Through the model development and improvement cycle, older models and the data they were trained with tend to get lost. DVC provides an excellent way to store your models and data in a git repository along with your code. ### 10. Great and easy way to version multiple kinds of data **Rating:** 4.5/5.0 stars **Reviewed by:** Sahil M. | Senior Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** September 23, 2021 **What do you like best about DVC?** The way in which we can version data at different kinds of sources **What do you dislike about DVC?** It's inability to deal with what exactly has changed, which although is a difficult and complicated task on its own. **Recommendations to others considering DVC:** While using DVC you might require to combine it with another tool, that tracks ML experiments, like MLflow. **What problems is DVC solving and how is that benefiting you?** Versioning our data for every ML pipeline, easy reproducibility, data lineage, etc. ### 11. Very useful for data tracing in ml **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Research | Mid-Market (51-1000 emp.) **Reviewed Date:** January 19, 2022 **What do you like best about DVC?** Allows to correctly track the data used to train ml/dl algorithms and to link a data version to a git commit **What do you dislike about DVC?** It is note very intuitive to understand hashes **What problems is DVC solving and how is that benefiting you?** Tracking thee data used for mle experiments, linked to a certain code version ## DVC Discussions - [What is DVC used for?](https://www.g2.com/discussions/what-is-dvc-used-for) - [1. What is the best way to cache data?](https://www.g2.com/discussions/1-what-is-the-best-way-to-cache-data) - 1 upvote - [View DVC pricing details and edition comparison](https://www.g2.com/products/dvc/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-26+16%3A52%3A36+-0500&secure%5Bsession_id%5D=0176a1d3-352a-490d-914a-82f29dffd59a&secure%5Btoken%5D=7b2d9b1fe22d5649e38601f70064c5cd6a70b32b2d95b2584f8ef840a98fc40a&format=llm_user) ## DVC Features **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Management** - Cataloging - Monitoring - Governing - Model Registry **Operations** - Metrics - Infrastructure management - Collaboration **Management** - Cataloging - Monitoring - Governing **Generative AI** - AI Text Generation - AI Text Summarization ## Top DVC Alternatives - [Weights & Biases](https://www.g2.com/products/weights-biases/reviews) - 4.7/5.0 (43 reviews) - [ClearML](https://www.g2.com/products/clearml/reviews) - 4.7/5.0 (13 reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (1,284 reviews)