Great Expectations vs Weights & Biases

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Great Expectations Evaluation	Weights & Biases Evaluation
Tagline	Open-source data quality framework for validating the datasets that feed your ML and analytics pipelines.	The ML experiment tracker, now with LLM eval features.
Category	Evaluation	Evaluation
Pricing	Freemium· GX Core free (Apache 2.0); GX Cloud paid tiers, contact sales	Freemium· Free personal; team from $50/mo per seat
Model	—	Platform (any LLM)
Editorial score	—	8.4 / 10
Use cases	data-validationpipeline-testingschema-drift-detectionml-data-qualitywarehouse-monitoring	ML experimentsLLM evalWeave
Pros	Apache 2.0 open source with a mature 11k+ practitioner community Declarative Expectations read like tests and version-control cleanly Broad connectors: Snowflake, BigQuery, Databricks, Postgres, S3, Spark, pandas Auto-generated Data Docs give non-engineers a readable quality report Slots into Airflow/Dagster/Prefect for scheduled validation	Industry-standard for ML tracking Weave adds LLM-native eval Mature, reliable Strong enterprise features
Cons	Not an LLM-output or model-quality evaluator — it grades data, not predictions Initial setup (Data Context, suites, checkpoints) has a real learning curve Cloud tier pricing is opaque and gated behind sales	Heavier UX than LLM-native tools LLM features still catching up
Website	greatexpectations.io	wandb.ai

Pick Great Expectations if

✅ Apache 2.0 open source with a mature 11k+ practitioner community
✅ Declarative Expectations read like tests and version-control cleanly
✅ Broad connectors: Snowflake, BigQuery, Databricks, Postgres, S3, Spark, pandas
✅ Auto-generated Data Docs give non-engineers a readable quality report

Pick Weights & Biases if