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Data as of May 29, 2026
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Evidently AI
Open-source ML monitoring and observability framework
AI InfrastructureFounded 2020#33 of 55 in AI Infrastructure
Updated May 29, 2026
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Company profile
What is Evidently AI?
Evidently AI develops open-source tools and a managed platform for machine learning model monitoring and observability. Their primary offering, the Evidently AI open-source library, helps data scientists and MLOps teams detect data drift, model performance degradation, and data quality issues in production. By providing transparency into ML models, they enable reliable and responsible AI deployment, addressing critical challenges in maintaining model integrity and performance over time.
Latest company data
Primary data point
Downloads
1.2M/30d
Tracked package: PyPI evidently
▲ +11%Updated 1d ago
Other data points
Dependent projects
19
Projects depending on tracked package: PyPI evidently
Updated 10h ago
GitHub stars
7.6K
Main repository stars
Updated 10h ago
Hacker News
2/30d
Position #36 in category discussion
Updated 10h ago
Repository health
Maintenance data from the main open-source repository.
OpenSSF ScorecardSource: OpenSSF ScorecardUpdates: WeeklyCaveat: Score 0–10 across security practices; not all checks apply to every repoMethodology →
4.5
Releases (30d)Source: GitHub release dataUpdates: WeeklyCaveat: Published releases in the last 30 daysMethodology →
0
Repository usage
Public repositories and source files importing packages tied to Evidently AI.
Repos importingMetric: Public GitHub repos using company packagesSource: ecosyste.ms Packages APIScope: UTC daily snapshotUpdates: DailyFreshness: 48h target; 48h expected source lagConfidence: HighCaveat: Public GitHub dependency data only; private repos and direct API use outside those packages are not visible.Methodology →
3400%
About Evidently AI
Evidently AI develops open-source tools and a managed platform for machine learning model monitoring and observability. Their primary offering, the Evidently AI open-source library, helps data scientists and MLOps teams detect data drift, model performance degradation, and data quality issues in production. By providing transparency into ML models, they enable reliable and responsible AI deployment, addressing critical challenges in maintaining model integrity and performance over time.
FoundersElena Samuylova, Emeli Dral, Dmitry Vorontsov
Funding
$2M · 1 roundRaised $2M total. Category position #33 of 55 in AI Infrastructure.
Seed2021
$2MSequoia Capital India
Investors
Sequoia Capital India
Tracked packages (1)
1 PyPI
evidently
PyPIMain PyPI packageevidently
Open-source ML monitoring and observability framework