Hugging Face leads in npm + PyPI downloads at 2x; Hugging Face leads in active contributors with 2 (30d); Supabase leads in npm dependents with 3.0K; Hugging Face leads in code adoption with 33.0K repos importing; Supabase leads in job demand with 5 mentions. Updated daily with live metrics.
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Hugging Face: 4.7M npm + PyPI downloads/mo (-1% MoM), 158.3K GitHub stars, 2 active contributors, 284 dependents, 33.0K repos importing, $394M funded vs
Supabase: 2.1M npm + PyPI downloads/mo, 99.5K GitHub stars, 1 active contributors, 3.0K dependents, 16.6K repos importing
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
Hugging Face leads across 6 of 7 key metrics
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
HN Mentions (30d)
Momentum (0-100)
For production adoption: Hugging Face is depended on by 9.1K packages versus 3.4K for Supabase. Hugging Face leads in package downloads with 4.7M per month compared to 2.1M for Supabase. Hugging Face appears in 33.0K GitHub repositories compared to 16.6K for Supabase.
For growth trajectory: Hugging Face has 158.3K GitHub stars versus 99.5K for Supabase. Hugging Face received 8 Hacker News mentions in the last 30 days compared to 3 for Supabase.
For longevity/risk: Hugging Face has raised $394M in total disclosed funding. Hugging Face's most recent round was Series D. Hugging Face attracted 2 active contributors in the last 30 days compared to 1 for Supabase. Hugging Face operates in Foundation Models while Supabase focuses on Developer Tools.
Hugging Face leads on developer adoption with 4.7M monthly package downloads. Supabase has 2.1M monthly downloads and is growing its developer footprint. For a deeper look, visit each company's full profile for trend charts, funding rounds, and community sentiment data.
← Scroll to compare →
| Metric | Updated Mar 23 | Supabase Updated Mar 23 |
|---|---|---|
| Website | huggingface.co → | supabase.com → |
| Description | Open-source ML platform hosting models, datasets, and Spaces | Open-source Firebase alternative built on Postgres |
| Data Confidence Source: AI-BuzzUpdates: DailyNote: Composite score (0-100) based on field completeness, metric freshness, and identifier coverage. Higher = more reliable data.Methodology → | Excellent (97%) | Excellent (93%) |
| Signal Coverage Source: AI-BuzzUpdates: DailyNote: Number of non-null adoption metrics tracked. Companies with more signals have more reliable composite scores.Methodology → | 7/7 metrics | 7/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | $394M | - |
| Last Funding | Series D Aug 2023 | - |
| Developer Adoption | ||
| Momentum | 39Moderate | 60Moderate✓ |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 35.3K | 1.7M✓ |
| npm Trend (30d) | ||
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 4.7M✓ | 442.4K |
| PyPI Trend (30d) | ||
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | 4.7M-1%✓ | 2.1M |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 2✓ | 1 |
| Contributors Trend | +1 contributors | - |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | 284 | 3.0K✓ |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | 8.8K✓ | 392 |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 33.0K✓ | 16.6K |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | 2.8% of HN mentions | 60% of HN mentions |
| Status | Private | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 8✓ | 3 |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | 15✓ | 1 |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | 2 | 5✓ |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 158.3K✓ | 99.5K |
| Founded | 2016 | 2020 |
| Primary Category | ||
| Data Last Updated | ||
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Licensed CC BY 4.0. Free to cite with attribution.
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Which AI companies are developers actually adopting? We track npm and PyPI downloads for hundreds of companies. Get the biggest shifts delivered weekly.
Compare metric trends across companies over time
Comparing npm Downloads over 30 days: Supabase (1.7M), Hugging Face (35.3K).
Source: npm registry | Methodology | CC BY 4.0
| Date | Hugging Face | Supabase |
|---|---|---|
| Mar 1 | 54.7K | - |
| Mar 8 | 0 | - |
| Mar 15 | 0 | - |
| Mar 22 | 37.1K | 1.6M |
| Mar 23 | 35.3K | 1.7M |
