7 Best Alternatives to DVC (Data Version Control) in 2026

Why look beyond DVC (Data Version Control)

DVC (Data Version Control) is an open-source tool that extends Git's capabilities to manage large datasets and machine learning models, promoting reproducibility in ML projects. It achieves this by storing data and model files externally (e.g., S3, Google Cloud Storage) and tracking pointers to these files within Git repositories, using .dvc files to link code and data versions [source]. DVC's strength lies in its Git-native approach, which can be intuitive for developers familiar with version control.

However, DVC's design might not align with every MLOps strategy. Teams requiring advanced experiment tracking UIs, integrated model registries, or comprehensive data governance features may find DVC's core CLI-centric approach less suitable. While DVC Studio offers a cloud platform for experiment tracking [source], some organizations might prefer fully integrated platforms that provide a broader suite of MLOps tools out-of-the-box, or solutions specifically designed for data lake versioning rather than individual file tracking. The need for a centralized platform for collaboration, advanced reporting, or specific compliance requirements can also lead teams to explore alternatives that offer different feature sets or deployment models.

Top alternatives ranked

1. 1. MLflow — An open-source platform for the machine learning lifecycle

   MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle, encompassing experiment tracking, reproducible runs, model packaging, and model serving. It provides APIs and a UI for logging parameters, code versions, metrics, and output files when running machine learning code, which helps in comparing and analyzing experiment results [source]. Unlike DVC, which focuses primarily on data and model versioning with Git, MLflow offers a broader scope that includes a centralized Model Registry for collaborative model management and versioning, as well as tools for deploying models to various serving platforms.

   MLflow's modular design allows users to adopt specific components as needed, making it flexible for integration into existing ML stacks. Its tracking server provides a central repository for experiment metadata, facilitating collaboration among data scientists. While DVC integrates with Git for metadata, MLflow maintains its own database for tracking. This can be beneficial for teams seeking a dedicated system for experiment management that is decoupled from code version control but still provides links to it.

   Best for:

   • Comprehensive experiment tracking and management
   • Centralized model registry and versioning
   • Teams seeking an open-source, modular MLOps platform
   • Reproducible ML runs and deployment

2. 2. Weights & Biases — A developer-first platform for ML experiment tracking and MLOps

   Weights & Biases (W&B) is a proprietary platform that provides tools for experiment tracking, data versioning, and model management. It offers a rich user interface for visualizing and comparing experiment results, logging hyperparameter sweeps, and tracking system metrics during model training [source]. W&B's focus on developer experience includes integrations with popular ML frameworks and a collaborative environment for teams. While DVC provides data and model versioning through Git, W&B offers its own data versioning capabilities, W&B Artifacts, which can track datasets, models, and other files used in experiments, ensuring reproducibility.

   W&B's strength lies in its advanced visualization and reporting features, making it easier for teams to analyze model performance, identify bottlenecks, and share insights. It also provides tools for hyperparameter optimization and model lineage. For organizations prioritizing a polished UI, extensive reporting, and a fully managed service for their MLOps needs, W&B can be a strong contender. Its enterprise offerings include on-premise deployment options for strict data governance requirements.

   Best for:

   • Advanced experiment visualization and reporting
   • Hyperparameter optimization and model lineage tracking
   • Teams requiring a comprehensive, managed MLOps platform
   • Collaborative ML development with strong UI support

3. 3. LakeFS — Git-like version control for data lakes

   LakeFS is an open-source platform that brings Git-like version control capabilities directly to data lakes and data warehouses. It enables atomic commits, branching, and merging operations on large datasets stored in object storage (e.g., S3, Azure Blob Storage, Google Cloud Storage) [source]. This allows data engineers and data scientists to manage data pipelines and experiments with the same rigor and reproducibility as code. Unlike DVC, which tracks pointers to data files within a Git repository, LakeFS operates at the storage layer, providing version control directly on the data itself.

   LakeFS is particularly well-suited for scenarios where data quality, reproducibility of data pipelines, and atomic operations on large data volumes are critical. It enables isolated development environments for data, facilitates rollback to previous data states, and supports continuous integration/continuous delivery (CI/CD) practices for data. While DVC helps version models and datasets alongside code, LakeFS focuses on providing a robust versioning layer for the entire data lake, making it an excellent choice for organizations with complex data governance and pipeline requirements.

   Best for:

   • Git-like version control for entire data lakes
   • Atomic commits, branching, and merging of large datasets
   • Ensuring data quality and reproducibility in data pipelines
   • Organizations with complex data governance needs

4. 4. Databricks MLflow — Managed MLflow with enterprise features

   Databricks MLflow is a managed service version of the open-source MLflow platform, integrated within the Databricks Lakehouse Platform. It offers all the functionalities of open-source MLflow, including experiment tracking, model registry, and project packaging, but with added enterprise-grade features such as enhanced security, scalability, and seamless integration with other Databricks services [source]. For teams already operating within the Databricks ecosystem, this provides a tightly integrated and fully managed solution for their MLOps needs, reducing operational overhead.

   The key differentiator for Databricks MLflow is its tight integration with Databricks notebooks, Delta Lake, and other components of the Lakehouse Platform. This allows for a unified experience from data ingestion and preparation to model training, deployment, and monitoring. While DVC can be integrated with various cloud storage solutions, Databricks MLflow offers a more opinionated and integrated environment for those committed to the Databricks ecosystem. It caters to organizations seeking a comprehensive platform that handles both data and ML workflows.

   Best for:

   • Teams already using or planning to use Databricks
   • Managed MLflow with enterprise-grade security and scalability
   • Seamless integration with Delta Lake and Databricks notebooks
   • Organizations seeking a unified data and ML platform

5. 5. Vertex AI ML Metadata — Metadata management for ML workflows on Google Cloud

   Vertex AI ML Metadata is a component of Google Cloud's Vertex AI platform that allows users to track and analyze metadata generated during machine learning workflows. It provides a centralized repository for recording information about ML artifacts (datasets, models), executions (training runs, evaluations), and their relationships [source]. This enables lineage tracking, reproducibility, and better understanding of ML experiments. While DVC focuses on versioning data and models with Git, Vertex AI ML Metadata provides a more holistic approach to tracking the entire ML workflow within a cloud-native environment.

   For organizations heavily invested in Google Cloud, Vertex AI ML Metadata offers deep integration with other Vertex AI services, such as Vertex AI Workbench, Training, and Pipelines. This allows for automated metadata capture and comprehensive visibility into complex ML pipelines. It's particularly useful for enterprise environments that need to comply with regulatory requirements or maintain strict auditing trails for their ML models. The service provides programmatic access via APIs, allowing for custom integrations and automation.

   Best for:

   • Organizations operating within the Google Cloud ecosystem
   • Comprehensive metadata tracking and lineage for ML workflows
   • Ensuring reproducibility and auditability of ML experiments
   • Automated metadata capture in complex ML pipelines

6. 6. Azure Machine Learning — A cloud-based platform for end-to-end ML lifecycle management

   Azure Machine Learning is Microsoft's cloud-based platform for building, deploying, and managing machine learning models. It offers a broad suite of tools for experiment tracking, model management, data versioning, and MLOps automation [source]. The platform includes capabilities for tracking metrics, parameters, and artifacts from ML experiments, similar to DVC's experiment tracking features, but within an integrated cloud environment. It also provides a model registry for versioning and managing models throughout their lifecycle.

   For enterprises using Azure, Azure Machine Learning offers deep integration with other Azure services like Azure Data Lake Storage, Azure DevOps, and Azure Kubernetes Service. This enables a seamless experience for data scientists and MLOps engineers, from data preparation to model deployment and monitoring. Its comprehensive MLOps capabilities, including CI/CD for ML, make it suitable for organizations looking for an all-in-one cloud-native platform that handles the entire ML lifecycle, rather than a standalone data versioning tool.

   Best for:

   • Organizations with existing investments in Azure services
   • End-to-end MLOps automation and management
   • Integrated experiment tracking, model registry, and data versioning
   • Scalable and secure cloud-native ML development

7. 7. H2O.ai MLOps — Enterprise MLOps platform for managing ML models

   H2O.ai MLOps is part of the H2O.ai Hybrid Cloud platform, designed to manage the entire lifecycle of machine learning models from development to deployment and monitoring. It provides capabilities for model versioning, experiment tracking, model deployment, and performance monitoring [source]. While DVC focuses on versioning data and models with Git, H2O.ai MLOps offers a comprehensive platform for managing models developed using various frameworks, including those created with H2O.ai's own tools like H2O-3 and Driverless AI.

   The platform is geared towards enterprise clients needing robust governance, auditing, and scalability for their ML initiatives. It supports multi-cloud and on-premise deployments, offering flexibility for organizations with diverse infrastructure requirements. H2O.ai MLOps provides a centralized hub for managing a portfolio of models, ensuring compliance and enabling efficient collaboration across teams. Its strength lies in providing an integrated solution for managing high-scale, production-grade ML systems.

   Best for:

   • Enterprise-grade model management and operations
   • Organizations using H2O.ai's ML development tools
   • Robust governance, auditing, and compliance for ML models
   • Multi-cloud and on-premise deployment flexibility

Side-by-side

How to pick

Selecting the right alternative to DVC involves evaluating your team's specific MLOps requirements, existing infrastructure, and long-term strategy. Consider the following factors to guide your decision:

• For comprehensive ML lifecycle management: If your team needs a unified platform that covers experiment tracking, model registry, and reproducible runs beyond just data versioning, MLflow (open-source) or Databricks MLflow (managed) are strong candidates. MLflow provides a broad set of tools that can be integrated into diverse environments, while Databricks MLflow offers a fully managed experience within the Databricks ecosystem.
• For advanced experiment visualization and collaboration: If a rich user interface, detailed reporting, and collaborative features for analyzing model performance are paramount, Weights & Biases is a highly capable proprietary platform. It excels in providing deep insights into experiments and streamlining team collaboration.
• For Git-like version control of entire data lakes: If your primary challenge is versioning large datasets directly within your data lake, enabling atomic commits, branching, and merging on data, then LakeFS is purpose-built for this. It provides a robust data versioning layer distinct from code versioning.
• For deep integration with specific cloud providers: Organizations heavily invested in a particular cloud ecosystem should consider the native MLOps offerings. Vertex AI ML Metadata is ideal for Google Cloud users needing comprehensive metadata tracking, while Azure Machine Learning provides an end-to-end platform for those on Azure. These services offer seamless integration with other cloud resources and managed infrastructure.
• For enterprise-grade model governance and operations: If your organization requires robust governance, auditing, and scalability for a portfolio of ML models, especially in regulated industries, H2O.ai MLOps offers an enterprise-focused solution with multi-cloud and on-premise deployment options.
• For budget and open-source preference: If cost-effectiveness and control over your MLOps stack are key, open-source options like MLflow and LakeFS offer powerful capabilities without vendor lock-in, though they may require more effort for self-hosting and maintenance.

By assessing these factors, you can align the capabilities of each alternative with your team's operational needs and strategic goals, ensuring you select a tool that enhances your MLOps workflow effectively.