Unstructured leads in npm + PyPI downloads at 2x; Unstructured leads in job demand with 2 mentions. Updated daily with live metrics.
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
DeepSeek: 86.1K npm + PyPI downloads/mo, 102.3K GitHub stars, 148 dependents vs
Unstructured: 190.9K npm + PyPI downloads/mo (+3% MoM), 14.3K GitHub stars, 2 active contributors, 3.4K repos importing, $105M funded
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
Unstructured leads across 4 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: Unstructured is depended on by 271 packages versus 148 for DeepSeek. Unstructured leads in package downloads with 190.9K per month compared to 86.1K for DeepSeek. Unstructured appears in 3.4K GitHub repositories.
For growth trajectory: DeepSeek has 102.3K GitHub stars versus 14.3K for Unstructured. DeepSeek received 11 Hacker News mentions in the last 30 days compared to 1 for Unstructured.
For longevity/risk: Unstructured has raised $105M in total disclosed funding. Unstructured's most recent round was Series A. DeepSeek operates in Foundation Models while Unstructured focuses on AI Infrastructure.
Unstructured leads on developer adoption with 190.9K monthly package downloads. DeepSeek counters with 102.3K 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 21 | Updated Mar 21 |
|---|---|---|
| Website | deepseek.com → | unstructured.io → |
| Description | AI research lab building open-source reasoning and code models | ETL for unstructured data preprocessing |
| 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 (91%) | Excellent (83%) |
| 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 | 6/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | - | $105M |
| Last Funding | - | Series A Jan 2024 |
| Developer Adoption | ||
| Momentum | 48Moderate✓ | 39Moderate |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 86.1K✓ | 0 |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 0 | 190.9K✓ |
| PyPI Trend (30d) | - | |
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | 86.1K | 190.9K+3%✓ |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 0 | 2✓ |
| Contributors Trend | - | -1 contributors |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | 148 | - |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | - | 271 |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 0 | 3.4K✓ |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | 2.9% of HN mentions | 6.5% of HN mentions |
| Status | Private | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 11✓ | 1 |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | 8✓ | 1 |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | 1 | 2✓ |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 102.3K✓ | 14.3K |
| Founded | 2023 | 2022 |
| 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: DeepSeek (86.1K).
Source: npm registry | Methodology | CC BY 4.0
| Date | DeepSeek | Unstructured |
|---|---|---|
| Mar 13 | 51.8K | - |
| Mar 20 | 0 | - |
| Mar 21 | 86.1K | - |
