Databricks leads in active contributors with 2 (30d); Supabase leads in code adoption with 16.6K 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 →
Databricks: 2.9M npm + PyPI downloads/mo (+29% MoM), 533 GitHub stars, 2 active contributors, 93 repos importing, $20.0B 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 →
Supabase leads across 5 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: Supabase is depended on by 3.4K packages versus 240 for Databricks. Databricks leads in package downloads with 2.9M per month compared to 2.1M for Supabase (Databricks +29% MoM). Supabase appears in 16.6K GitHub repositories compared to 93 for Databricks.
For growth trajectory: Supabase has 99.5K GitHub stars versus 533 for Databricks. Supabase received 3 Hacker News mentions in the last 30 days.
For longevity/risk: Databricks has raised $20.0B in total disclosed funding. Databricks's most recent round was Venture. Databricks attracted 2 active contributors in the last 30 days compared to 1 for Supabase. Databricks operates in AI Infrastructure while Supabase focuses on Developer Tools.
Databricks leads on developer adoption with 2.9M monthly package downloads. Supabase counters with 99.5K GitHub stars. 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 | databricks.com → | supabase.com → |
| Description | Data and AI platform, creator of MLflow and Dolly | 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 (94%) | 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 → | 4/7 metrics | 7/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | $20.0B | - |
| Last Funding | Venture Feb 2026 | - |
| Developer Adoption | ||
| Momentum | 55Moderate | 60Moderate✓ |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 0 | 1.7M✓ |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 2.9M✓ | 442.4K |
| PyPI Trend (30d) | ||
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | 2.9M+29%✓ | 2.1M |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 2✓ | 1 |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | - | 3.0K |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | 240 | 392✓ |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 93 | 16.6K✓ |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | - | 60% of HN mentions |
| Status | Private | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 0 | 3✓ |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | 0 | 1✓ |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | 1 | 5✓ |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 533 | 99.5K✓ |
| Founded | 2013 | 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.
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).
Source: npm registry | Methodology | CC BY 4.0
| Date | Databricks | Supabase |
|---|---|---|
| Mar 22 | - | 1.6M |
| Mar 23 | - | 1.7M |
