Great Expectations vs MLflow
both compete in AI Infrastructure. Updated daily with live metrics.
Great Expectations: 28.0M downloads/mo (+12%), 11.3K GitHub stars, 1 active contributors, $61M funded vs
MLflow
Source: AI-Buzz. Updated daily from npm, PyPI, GitHub, and Hacker News.
Key Differences
For production adoption: Great Expectations is depended on by 104 packages. Great Expectations has 28.0M package downloads per month (+12% MoM). Great Expectations appears in 284 GitHub repositories.
For growth trajectory: Great Expectations has 11.3K GitHub stars. Great Expectations received 2 Hacker News mentions in the last 30 days.
For longevity/risk: Great Expectations has raised $61M in total disclosed funding. Great Expectations's most recent round was Series A. both companies operate in the AI Infrastructure category.
Bottom Line
Great Expectations leads on developer adoption with 28.0M monthly package downloads. 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)
HN Mentions (30d)
Momentum (0-100)
Data freshness notice: MLflow: no data freshness timestamp available.
← Scroll to compare →
| Metric | Updated Mar 17 | MLflow |
|---|---|---|
| Website | greatexpectations.io → | mlflow.org → |
| Description | Data quality framework for validating and profiling data pipelines | Open-source platform for the ML lifecycle including experiment tracking and model registry |
| Total Disclosed Funding Source: Public records / manual researchUpdates: Weekly | $61M✓ | N/A |
| Last Funding | Series A Feb 2022 | Not available |
| Developer Adoption | ||
| Momentum | 43Moderate✓ | N/A |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrors | 0 | N/A |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrors | 28.0M✓ | N/A |
| PyPI Trend (30d) | - | |
| Total Downloads (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managers | 28.0M+12%✓ | Not tracked |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activity | 1✓ | N/A |
| Contributors Trend | 0% | - |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPI | 104✓ | N/A |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forks | 284✓ | N/A |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary category | 0.1% of HN mentions | - |
| Status | Private | Unknown |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: Daily | 2✓ | N/A |
| 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 | 11.3K✓ | N/A |
| Founded | 2018 | Unknown |
| Primary Category | ||
| Data Last Updated | Unknown | |
* MLflow has no npm/PyPI packages tracked - download metrics unavailable. Signal score based on GitHub and community data only.
Select Companies to Compare
Related Comparisons
Weekly AI Intelligence
Which AI companies are developers actually adopting? We track npm and PyPI downloads for hundreds of companies. Get the biggest shifts delivered weekly.