Anyscale vs Hugging Face (Gradio)
Anyscale leads in package downloads at 5x; Hugging Face (Gradio) has raised more funding at $394M; Anyscale leads in active contributors with 11 (30d); both compete in AI Infrastructure. Updated daily with live metrics.
Anyscale: 58.2M downloads/mo (+34%), 41.8K GitHub stars, 11 active contributors, $260M funded vs
Hugging Face (Gradio): 12.7M downloads/mo (-7%), 42.0K GitHub stars, 3 active contributors, 66 dependents, $394M funded
Source: AI-Buzz. Updated daily from npm, PyPI, GitHub, and Hacker News.
Key Differences
For production adoption: Hugging Face (Gradio) is depended on by 1.4K packages versus 759 for Anyscale. Anyscale leads in package downloads with 58.2M per month compared to 12.7M for Hugging Face (Gradio) (Anyscale +34% MoM, Hugging Face (Gradio) -7% MoM). Hugging Face (Gradio) appears in 11.9K GitHub repositories compared to 3.6K for Anyscale.
For growth trajectory: Hugging Face (Gradio) has 42.0K GitHub stars versus 41.8K for Anyscale.
For longevity/risk: Hugging Face (Gradio) has raised $394M in total disclosed funding, while Anyscale has raised $260M. Anyscale's most recent round was Strategic Investment, while Hugging Face (Gradio)'s most recent round was Series D. Anyscale attracted 11 active contributors in the last 30 days compared to 3 for Hugging Face (Gradio). both companies operate in the AI Infrastructure category.
Bottom Line
Hugging Face (Gradio) leads on developer adoption with 12.7M monthly package downloads. Anyscale counters with 41.8K GitHub stars, reflecting strong developer awareness. Both compete in the AI Infrastructure space. For a deeper look, visit each company's full profile for trend charts, funding rounds, and community sentiment data.
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
Momentum (0-100)
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| Metric | Updated Mar 17 | Updated Mar 17 |
|---|---|---|
| Website | anyscale.com → | gradio.app → |
| Description | Ray framework company. Distributed computing for ML workloads. | Open-source Python library for building ML demos |
| Total Disclosed Funding Source: Public records / manual researchUpdates: Weekly | $260M | $394M✓ |
| Last Funding | Strategic Investment Aug 2022 | Series D Aug 2023 |
| Developer Adoption | ||
| Momentum | 33Moderate✓ | N/A |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrors | 0 | 64.3K✓ |
| npm Trend (30d) | - | |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrors | 58.2M✓ | 12.6M |
| PyPI Trend (30d) | ||
| Total Downloads (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managers | 58.2M+34%✓ | 12.7M-7% |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activity | 11✓ | 3 |
| Contributors Trend | +175% | +200% |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependents | N/A | 66✓ |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPI | 759 | 1.4K✓ |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forks | 3.6K | 11.9K✓ |
| Status | Private | Unknown |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: Daily | 0 | 0 |
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by name | 1 | 2✓ |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technology | 1✓ | N/A |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicator | 41.8K | 42.0K✓ |
| Founded | 2019 | 2016 |
| Primary Category | ||
| Data Last Updated | ||
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