DagsHub vs Weights & Biases
DagsHub
dagshub.comWeights & Biases
wandb.aiSide by side
| Figure | DagsHub | Weights & Biases |
|---|---|---|
| Price from | $8.25/mo | $60/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 5 |
| Spec score · Data Version Control Tools | 9.3 (#1) | 8.6 (#7) |
| Spec score · ML Experiment Tracking Software | 9.4 (#2) | 8.6 (#5) |
| Spec score · Model registries | 9.4 (#4) | 9.5 (#3) |
| API | DagsHub publishes an API with interactive endpoint testing, token authentication and Basic Authentication using a username and token. | W&B provides a full Data API for exporting data logged to the service. |
| Company headquarters | LinkedIn lists San Francisco as headquarters, while Crunchbase lists Tel Aviv, Israel. | LinkedIn lists headquarters at 400 Alabama Street, San Francisco, California 94110, United States. |
| Company size | LinkedIn lists DagsHub as privately held with 11–50 employees. | LinkedIn lists Weights & Biases as a privately held software-development company with 201–500 employees. |
| Experiment tracking | Experiments can be tracked with Git, DVC experiments or MLflow, including metrics, parameters and artifacts. | The platform automatically records machine-learning pipeline details and provides contextual visual analysis. |
| Integrations | Documented integrations include GitHub, GitLab, Bitbucket, DVC, MLflow, Label Studio, Jenkins, Slack, Discord, New Relic and Amazon S3. | W&B is designed to integrate with customers' ML stacks and tools and offers managed or self-managed deployment choices. |
| Rating on Capterra | 0.0 out of 5 from 0 reviews | 0.0 out of 5 from 0 reviews |
| Rating on G2 | 4.8 out of 5 from 14 reviews | 4.5 out of 5 from 72 reviews |
| Self-hosting | Enterprise customers can install DagsHub on their own infrastructure, including on-premises, VPC, air-gapped and OpenShift environments. | Self-hosting requires Docker and Python and can run on Linux, macOS or Windows; customers manage their own storage and backups. |
| What it does | DagsHub is a web platform for hosting, versioning and collaborating on machine-learning code, data, experiments, models and annotations. | Weights & Biases is an AI developer platform for building, training, fine-tuning, evaluating and managing AI models and applications. |
Plans
DagsHub
- IndividualFree
- IndividualFree
- Team$99 a year
- Team$119 a month
- EnterprisePriced on request
Weights & Biases
- FreeFree
- PersonalFree
- Pro$60 a month
- Advanced EnterprisePriced on request
- EnterprisePriced on request
In their own words
DagsHub
DagsHub helps developers and teams manage machine-learning projects, from data collection and dataset curation through annotation, experimentation, and model management. A project workspace can hold code, data, experiments, models, pipelines, and annotations together. The platform is designed for text, images, audio, video, documents, medical imaging, and binary files. Experiment tracking supports run comparison, metric and parameter visualization, real-time progress monitoring, and reproducibility. Data Engine provides curation, visualization, quality review, lineage, hard-example mining, labeling, auto-labeling, and data streaming. Official integrations include tools such as GitHub, Google Colab, DVC, MLflow, Jenkins, Label Studio, Google Drive, Weights & Biases, and Hugging Face; hosted projects also have support for the MLflow and Label Studio APIs. DagsHub hosts data but does not provide compute, so projects rely on local, cloud, or edge infrastructure. Enterprise installations can run on-premises, in a VPC, in air-gapped environments, and in OpenShift environments. The Individual plan is free, with stated private-repository limits; paid pricing starts at $99/user/mo billed annually, and a 14-day trial is available.
Weights & Biases
Weights & Biases has no write-up on Specifiction yet; its figures are above.