Individualper month
$0/month
- 200GB managed storage, up to 100 tracked experiments in private repositories
Finding your next stop…
Loading the latest directory information.
Loading the latest directory information.
Start here for the decision-making essentials: what DagsHub does, who it is for, how it is accessed, and the first-party sources behind this profile.
$0/month
DagsHub is an AI platform for managing the entire ML lifecycle—from data collection and multimodal dataset annotation through experiment tracking (model training and prompt engineering) to model management—built on open source tools like Git, DVC, MLflow, and Label Studio.
Read-only public captures of DagsHub’s homepage. Screenshots are dated, never live embeds, and open full-screen.
Short answers to the questions buyers and builders commonly ask about DagsHub. Each answer cites the shared ledger below, where every source is listed once.
Curate and annotate multimodal datasets including vision, audio, and LLM data on a single · Track experiment progress and compare results · Manage model versions and deploy easily · DagsHub lets teams host code, data, experiments and models in one place to manage ML proje
Curate and annotate vision, audio, and LLM datasets, track experiments, and manage models on a single platform
AI teams and data scientists; over 65,000 data scientists reportedly use DagsHub · Developers and teams managing the full ML lifecycle from data collection to model manageme · Data scientists and ML practitioners building AI collaboratively across teams
Over 65,000 data scientists build AI with DagsHub
Built for unstructured and multimodal data including text, images, audio, video, documents · Reproducing any ML model version by rebuilding the model and reproducing any task in the M · Sharing models and datasets across teams so ML practitioners from different teams can use · Sharing and collaborating on data and models across an organization
DagsHub was particularly designed for unstructured and multimodal data types – e.g. text, images, audio, video, documents, medical imaging, and binary files.
Individual | $0/month | 200GB managed storage, up to 100 tracked experiments in private re · Team | free trial listed | Up to 1TB of data or up to 2 million files, up to 10 team membe · Enterprise | Custom | Petabyte-scale data management, deploy models to your cluster, full
| IndividualStart Free
Supports CI/CD and Git Flow for machine learning via Jenkins, GitHub Actions and webhooks · MLflow (open-source experiment tracker) compatibility · DVC integration for versioning data, code, and experiment components · Includes CI/CD/CT integration
Integrate with tools you already use – Jenkins, GitHub Actions and Webhooks CI/CD and Git Flow in Machine Learning
Enterprise supports installation on VPC, On-Prem, or Air-Gapped infrastructure · Hosted SaaS with optional DagsHub Storage for data, but no compute is provided — compute r · Can be installed on-prem, VPC, air-gapped, and OpenShift environments
Install on VPC, On-Prem, Air-Gapped
This profile connects the jobs DagsHub is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
HTTP 200 verified twice
DagsHub: Everything you need to manage multimodal AI
Built on open source tools including Git, DVC, MLflow, and Label Studio · Supports curation and annotation of vision, audio, and LLM datasets · Designed for unstructured and multimodal data types
Curate and annotate multimodal datasets including vision, audio, and LLM data on a single platform
Track experiment progress and compare results
Manage model versions and deploy easily
Individual | $0/month | 200GB managed storage, up to 100 tracked experiments in private repositories
Team | free trial listed | Up to 1TB of data or up to 2 million files, up to 10 team members
Enterprise | Custom | Petabyte-scale data management, deploy models to your cluster, full VPC/Air-gapped on-premise installation, SSO/LDAP/OIDC RBAC
Built on open source tools and formats including Git, DVC, MLflow, and Label Studio
Enterprise supports installation on VPC, On-Prem, or Air-Gapped infrastructure
Team and Enterprise tiers offer SSO, LDAP, and OIDC authentication plus Audit Logs
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
This profile is being checked for dated releases and material product changes. Nothing appears here until the exact date and event can be verified from a recorded source.
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
These tools share a workflow, capability or audience with DagsHub. They may complement it rather than replace it, and are not presented as integrations or endorsements.
AI teams and data scientists; over 65,000 data scientists reportedly use DagsHub
DagsHub lets teams host code, data, experiments and models in one place to manage ML projects end-to-end
Versioned pipelines with pipeline visualizations for high-level project navigation
Data and code versioning, diffs, annotations and visualizations side by side
Experiment tracking with comparison, metrics/parameters visualizations and real-time monitoring
Data curation and annotation for multimodal datasets plus model management with versioning and deployment
Built for unstructured and multimodal data including text, images, audio, video, documents, medical imaging, and binary files
Developers and teams managing the full ML lifecycle from data collection to model management
Web-based MLOps platform combining open source infrastructure including Git, DVC, MLflow, and Label Studio
Hosted SaaS with optional DagsHub Storage for data, but no compute is provided — compute runs locally, in the cloud, or on edge devices
Supports CI/CD and Git Flow for machine learning via Jenkins, GitHub Actions and webhooks
Official social profiles at https://x.com/TheRealDagsHub and https://www.linkedin.com/company/dagshub
Model version management, registries, and lineage tracking with full model-to-source-data traceability
Experiment tracking with MLflow compatibility, allowing one line of code to send experiments and results
Multimodal data curation and annotation for vision, audio, and LLM datasets
AI-powered Human-in-the-Loop workflows for labeling and review of images, videos, audio, text, and LLM data
Reproducing any ML model version by rebuilding the model and reproducing any task in the ML lifecycle
Sharing models and datasets across teams so ML practitioners from different teams can use published artifacts in their experiments and CI/CD pipelines
Data scientists and ML practitioners building AI collaboratively across teams
MLflow (open-source experiment tracker) compatibility
DVC integration for versioning data, code, and experiment components
Images, video, audio, text, and LLM data
Over 65,000 data scientists build AI with DagsHub
DagsHub, with active presence on Twitter (@TheRealDagsHub) and LinkedIn (linkedin.com/company/dagshub)
Build, evaluate, and deploy high-quality AI models and applications on a single platform
Experiment Tracking to track experiment progress and compare results
Model Management to manage model versions and deploy easily
Curation & Annotation to query, visualize, and annotate multimodal datasets
