Great Expectations vs LangSmith

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

	Great Expectations Evaluation	LangSmith Evaluation
Tagline	Open-source data quality framework for validating the datasets that feed your ML and analytics pipelines.	LangChain's eval + observability platform.
Category	Evaluation	Evaluation
Pricing	Freemium· GX Core free (Apache 2.0); GX Cloud paid tiers, contact sales	Freemium· Free starter; Plus $39/mo per seat
Model	—	Platform (any LLM)
Editorial score	—	8.7 / 10
Use cases	data-validationpipeline-testingschema-drift-detectionml-data-qualitywarehouse-monitoring	LLM tracingevalsLangChain integration
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	Tight LangChain integration Strong tracing UX Mature dataset/eval flows Reasonable per-seat pricing
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	Best value if you're on LangChain UI can feel dense
Website	greatexpectations.io	www.langchain.com

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 LangSmith if