Data Observability and Quality Platforms in AI answers

Data as of Oct 22, 2026Based on 291 AI responsesReviewed by Dimitry Apollonsky

AI summary

Soda and Monte Carlo Data are recommended in nearly equal measure for automated anomaly detection and warehouse monitoring. Cleanlab becomes the usual answer when teams need to find errors and outliers in machine learning training datasets.

What’s changing · since July

Since July, Great Expectationsrising has become the usual answer for tracking quality in machine learning pipelines, while Sodarising has emerged as the go-to platform for preventing data issues before production. Monte Carlo Datafalling has seen fewer mentions across the market.

Most-mentioned brands

Rank and Mentioned in · since Jul 5

#	Brand	Recommended for	Mentioned in
1	Monte Carlo Data 65% end-to-end data observability and automated machine learning anomaly detection	end-to-end data observability and automated machine learning anomaly detection	65%
1	Soda 64% developer-friendly YAML tests and data contract checks across pipelines	developer-friendly YAML tests and data contract checks across pipelines	64%
3	Bigeye 48% automated data metrics and tracking upstream lineage issues	automated data metrics and tracking upstream lineage issues	48%
3	Great Expectations 47% open-source rule definitions and validating tabular pipeline data	open-source rule definitions and validating tabular pipeline data	47%
3	Anomalo 46% automated machine learning data quality monitoring without manual rule creation	automated machine learning data quality monitoring without manual rule creation	46%
6	Metaplane 27% lightweight automated anomaly detection and warehouse column-level lineage	lightweight automated anomaly detection and warehouse column-level lineage	27%
7	Acceldata 22% data reliability scoring and tracing lineage across enterprise environments	data reliability scoring and tracing lineage across enterprise environments	22%
7	Cleanlab 18% detecting label errors and outliers in machine learning datasets	detecting label errors and outliers in machine learning datasets	18%
9	WhyLabs 16% privacy-preserving statistical profiling using whylogs for sensitive data	privacy-preserving statistical profiling using whylogs for sensitive data	16%
9	EvidentlyEvidently AI product line 15% evaluating model performance and data quality in machine learning pipelines	evaluating model performance and data quality in machine learning pipelines	15%
9	Datadog 13%		13%
9	dbt 12%		12%
9	Elementary Data 12%		12%
14	Evidently AI 10%		10%
14	whylogs · whylabs.github.io 10%		10%
14	Arize AI 8%		8%
17	Datafold 7%		7%
17	Cleanlab StudioCleanlab product line 6%		6%
17	Atlan 6%		6%
17	Deepchecks 6%		6%
17	Sifflet Data 4%		4%
22	SlackSalesforce product line 3%		3%
22	Amazon RedshiftAmazon product line 3%		3%
22	Collibra Data QualityCollibra product line 3%		3%
22	IBM DatabandIBM product line 2%		2%
22	Fiddler AI 2%		2%
22	Prometheus 2%		2%
22	TensorFlow Data ValidationGoogle Gemini product line 2%		2%
22	DataHub · datahub.com 2%		2%
22	Deequ 2%		2%
22	Grafana 2%		2%
22	MLflow 2%		2%
33	Datalab 1%		1%
33	FiftyOneVoxel51 product line 1%		1%
33	Granica 1%		1%
33	Collibra 1%		1%
33	whylogs · whylogs.readthedocs.io 1%		1%
33	Ataccama 1%		1%
33	Databricks 1%		1%
33	Snowflake 1%		1%
33	Labelbox 1%		1%
33	Arize PhoenixArize AI product line 1%		1%
33	Label Studio 1%		1%
33	Dynatrace 1%		1%
33	YData 1%		1%
33	DQLabs 1%		1%
33	Databricks Data Intelligence PlatformDatabricks product line 1%		1%
33	SigNoz 1%		1%
33	New Relic 1%		1%
33	Grafana CloudGrafana product line 1%		1%
33	Snowflake AISnowflake product line 1%		1%
33	Honeycomb 1%		1%
33	SQLMesh · sqlmesh.readthedocs.io 1%		1%
33	Giskard 1%		1%
33	PyOD 1%		1%
33	Voxel51 1%		1%
33	Pandera 1%		1%
33	SYNQ 1%		1%
33	IBM 1%		1%
33	Argilla 1%		1%
33	Validio 1%		1%
33	Amazon SageMakerAmazon product line 1%		1%
33	Informatica 1%		1%
33	Apache SparkApache Software Foundation product line 1%		1%
33	Databricks Lakehouse PlatformDatabricks product line 1%		1%
33	AWSAmazon product line 1%		1%
33	Scikit-learn <1%		<1%
33	DataQualityReport <1%		<1%
33	Weights & Biases <1%		<1%
33	Jenga <1%		<1%
33	Datadog WatchdogDatadog product line <1%		<1%
33	SQLMesh · tobikodata.comTobiko Data product line <1%		<1%
33	Ataccama ONEAtaccama product line <1%		<1%
33	Coalesce <1%		<1%
33	Snorkel FlowSnorkel AI product line <1%		<1%
33	DataHub · datahub.io <1%		<1%
33	GX Cloud <1%		<1%
33	RAPIDS <1%		<1%
33	WhyLabs LangKit <1%		<1%
33	PagerDuty <1%		<1%
33	Lightup <1%		<1%
33	Aporia <1%		<1%
33	Integrate.io <1%		<1%
33	Neptune.ai <1%		<1%
33	Datacoral <1%		<1%
33	Matplotlib <1%		<1%
33	Apache AirflowApache Software Foundation product line <1%		<1%
33	Seemore Data <1%		<1%
33	PyDeequ <1%		<1%
33	SeabornPydata product line <1%		<1%
33	Google Cloud BigQueryAlphabet product line <1%		<1%
33	Alteryx <1%		<1%
33	Azure Synapse AnalyticsMicrosoft product line <1%		<1%
33	AWS SageMaker Model Monitor <1%		<1%
33	Jira SoftwareAtlassian product line <1%		<1%
33	Dynatrace DavisDynatrace product line <1%		<1%
33	Snowflake Native AppSnowflake product line <1%		<1%
33	Google Cloud Gemini Enterprise Agent PlatformAlphabet product line <1%		<1%
33	OpenTelemetry <1%		<1%
33	Grafana LokiGrafana product line <1%		<1%

Who leads each AI engine

ChatGPT SearchChatGPT Search: Soda
Google AI ModeGoogle AI Mode: Monte Carlo Data

Mentioned in on each engineChatGPT SearchGoogle AI Mode

Widest split: Great Expectations, 60% on ChatGPT Search and 34% on Google AI Mode.

Mentioned in on each AI engine, by brand
Brand	ChatGPT Search	Google AI Mode
Soda	76%	53%
Monte Carlo Data	65%	65%
Great Expectations	60%	34%
Anomalo	47%	44%
Bigeye	54%	42%
Metaplane	21%	34%
Acceldata	26%	17%
Cleanlab	18%	17%
Datadog	3%	24%
WhyLabs	13%	19%

Latest insights

Week of Aug 31–Sep 6 · Since Jul 5

Soda Gap between platforms: ChatGPT Search now mentions Soda 22.4 points more often than Google AI Mode in Data Observability and Quality Platforms, a narrowed gap
Bigeye Gap between platforms: ChatGPT Search now mentions Bigeye 11.7 points more often than Google AI Mode in Data Observability and Quality Platforms, a narrowed gap
Great Expectations Gap between platforms: The platform gap for Great Expectations in Data Observability and Quality Platforms widened to 27.8 points, ChatGPT Search ahead
Metaplane Gap between platforms: Metaplane's platform gap in Data Observability and Quality Platforms narrowed: Google AI Mode 33.8%, ChatGPT Search 21.7%
Acceldata Gap between platforms: Acceldata's platform gap in Data Observability and Quality Platforms widened: ChatGPT Search 26.8%, Google AI Mode 16.9%

and 7 more insightssee the ranking

Market map

Mentioned in · since Jul 5

Leading brands by topic

ML dataset validationLed byGreat Expectations

Questions by topic2

ML dataset validation
- My goal is to find errors and outliers in our training data. What's the best data quality tool specifically designed for ML datasets?
- Can you recommend some data quality monitoring solutions designed for machine learning pipelines?
- We need to clean up our training data by catching outliers and errors. Can you recommend a data quality tool built for machine learning?
- I need to track data quality for our ML models. What's the best data quality monitoring tool for ML pipelines?
Data observability platform for enterprise scale
- The problem is, our data is untrustworthy. What is the best data observability platform for monitoring data quality at scale?
- What are the top-rated data observability platforms for monitoring data quality at a large scale?
- Our data quality is inconsistent, and we need a solution to fix it. What data observability tools are built to monitor quality across large-scale systems?

Which data observability platforms can trace a failed quality check through upstream lineage and alert the owning team?
Which data quality monitoring platform is the best for running checks directly on our data warehouse and alerting on anomalies?
What's the best data quality and observability platform to catch issues before they hit production?

Sources AI cites100

41% of citations point at the ranked brands’ own sites. montecarlo.ai, youtube.com, ciopages.com are the most-cited sites outside the ranked brands’ own domains.

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