Whylogs vs Great Expectations

Side-by-side — pricing, strengths, tradeoffs, and fit.

Whylogs

Open source standard for data logging and ML monitoring.

Great Expectations

A Python data validation framework for testing datasets.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0

Overview

One-liner

Open source standard for data logging and ML monitoring.

One-liner

A Python data validation framework for testing datasets.

Maturity

Declining

Maturity

Not enough data

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams needing to monitor and log their ML models' performance
Projects where maintaining model reliability is crucial due to changing data distributions
Developers looking for a lightweight, easy-to-integrate solution for logging

✓ Best for

Teams needing to validate large datasets for consistency and quality
Organizations implementing automated testing within their CI/CD pipelines
Data science teams requiring robust documentation of data expectations

Not ideal for

✕ Not for

Real-time monitoring of streaming data (Whylogs focuses on batch processing)
Teams requiring complex, real-time alerting systems beyond basic logging capabilities

✕ Not for

Projects that require real-time data validation (Great Expectations is batch-oriented)
Use cases where a graphical user interface is preferred over command-line or library integration

Strengths

↑ Strengths

Data logging for ML models
Monitoring of data distribution changes
Integration with various storage backends
Lightweight and easy to integrate into existing pipelines

↑ Strengths

Data validation and testing
Automated documentation of data expectations
Integration with CI/CD pipelines
Support for various data sources (SQL, CSV, etc.)

Weaknesses

↓ Weaknesses

Performance overhead in large-scale data logging

↓ Weaknesses

Limited language support beyond Python
Performance issues with large datasets
Full Whylogs profile →Full Great Expectations profile →