About
DagsHub is a comprehensive MLOps platform built for data scientists and AI teams who need to manage the full lifecycle of AI development—from raw data to deployed models. Its Curation & Annotation module lets teams connect multiple data sources, then query, visualize, and annotate multimodal datasets covering vision, audio, and LLM data, transforming raw petabytes into high-quality golden datasets. The Experiment Tracking feature is MLflow-compatible, allowing teams to log, compare, and understand trends across all their training runs. Model Management enables versioning, deployment, and full lineage tracing from a trained model back to its source data. DagsHub integrates easily with popular ML frameworks, open-source formats, and secure cloud storage providers, fitting naturally into existing workflows. It offers data versioning and lineage, notebook versioning and diffing, CI/CD/CT integration, and interactive pipelines. The platform scales from individual developers on the free tier up to enterprise teams needing petabyte-scale data management, VPC/air-gapped on-premise deployment, SSO/LDAP/OIDC, and organizational RBAC. With over 65,000 data scientists using DagsHub, it is trusted by teams that need to move fast without sacrificing rigor in data quality or reproducibility.
Key Features
- Multimodal Dataset Curation & Annotation: Connect multiple data sources and enrich, query, visualize, and annotate vision, audio, and LLM datasets—including auto-labeling support—to create high-quality training data.
- MLflow-Compatible Experiment Tracking: Log and track all your training runs, compare results across experiments, and identify trends—fully compatible with the MLflow ecosystem.
- Model Management & Lineage: Version, manage, and deploy models with a complete lineage tracing each model back to its source data and training experiments.
- Data Versioning & Lineage: Track every change to datasets and pipelines with built-in versioning, lineage, and notebook diffing for full reproducibility.
- Enterprise-Grade Deployment Options: Supports VPC, air-gapped on-premise, OpenShift, SSO/LDAP/OIDC, and organizational RBAC for secure, large-scale AI operations.
Use Cases
- Data science teams curating and annotating large-scale vision or audio datasets to build high-quality training sets for computer vision or speech models
- ML engineers tracking and comparing hundreds of training experiments across model architectures to identify optimal configurations
- Enterprise AI teams managing model versioning and deployments with full lineage tracing from production model back to source data
- Research organizations collaborating on shared datasets and notebooks with version control, diffing, and reproducibility built in
- Organizations transitioning from fragmented MLOps tooling to a single unified platform to accelerate experimentation and reduce overhead
Pros
- Unified AI Development Platform: Combines data annotation, experiment tracking, and model management in one place, eliminating the need to stitch together multiple tools.
- MLflow Compatibility: Teams using MLflow can adopt DagsHub without changing their existing experiment logging workflows.
- Flexible for All Scales: A generous free tier for individuals and scalable paid plans make DagsHub accessible from solo projects to petabyte-scale enterprise workloads.
- Strong Collaboration Features: Supports team RBAC, shared repositories, and collaborative annotation workspaces, making it easy for distributed data science teams to work together.
Cons
- Team Plan Pricing Can Be High: At $99–$119 per user per month, the Team plan may be cost-prohibitive for smaller startups or early-stage teams.
- Enterprise Features Require Significant Setup: VPC, air-gapped, and on-premise deployment options are powerful but require DevOps expertise and planning to configure correctly.
- Private Repository Experiment Limits on Free Tier: The free plan caps tracked experiments in private repositories at 100, which may be limiting for active research projects.
Frequently Asked Questions
What is DagsHub and who is it for?
DagsHub is an all-in-one AI data management platform for data scientists, ML engineers, and AI teams. It covers the full lifecycle from dataset curation and annotation through experiment tracking and model deployment.
Is DagsHub compatible with MLflow?
Yes. DagsHub's experiment tracking module is fully compatible with MLflow, so teams can continue using their existing MLflow logging code while gaining DagsHub's additional collaboration and data management features.
What types of data does DagsHub support?
DagsHub supports multimodal datasets including vision (images, video), audio, and LLM/text data, and is designed to handle petabyte-scale workloads for enterprise teams.
Can I use DagsHub for free?
Yes. DagsHub offers a free Individual plan with unlimited public repositories, up to 2 collaborators in private projects, 20GB of storage, and up to 100 tracked experiments in private repositories.
Does DagsHub support on-premise deployment?
Yes. DagsHub Enterprise supports full VPC, air-gapped, and on-premise installation including OpenShift compatibility, SSO/LDAP/OIDC, and organizational resource controls for regulated or security-sensitive environments.
