Collate
Managed agentic data platform built on OpenMetadata, with AI agents, data quality tests, observability metrics, column-level lineage, and governance workflows.
Best for data teams that want OpenMetadata fully managed, with enterprise support, security features, and advanced governance workflows.
Ataccama ONE
Agentic data trust platform with data quality rules, observability, a data catalog, lineage, master data management, and a built-in AI agent.
Best for large organizations that want enterprise data quality, master data management, and data governance from one vendor.
Monte Carlo
Data + AI observability platform that monitors data pipelines and AI agents in production, with column-level lineage and AI troubleshooting agents.
Best for large data teams ready to invest in a mature, widely adopted platform that monitors both their data and their AI agents.
Acceldata
Autonomous data and AI platform, born in data observability, with AI agents for data monitoring, lineage, governance, and cost optimization.
Best for large enterprises with hybrid or on-premise data estates that want observability, FinOps, and agentic data management from one vendor.
Anomalo
Autonomous data quality monitoring platform where AI agents monitor, investigate, and report issues across structured and unstructured data.
Best for enterprises that want ML-based monitoring with little rule-writing, including quality checks on unstructured data such as documents.
Bigeye
Enterprise AI trust platform built on data observability, with anomaly detection, end-to-end lineage, and governance of the data AI agents use.
Best for large enterprises with modern and legacy data sources that also need to govern which data their AI agents access.
DQOps
Open-source data quality operations center to profile, automate, and monitor data quality with built-in checks, monitors, and data quality KPI dashboards.
Best for data teams that want to customize built-in checks and track data quality KPIs on dashboards. The open-source version has had no releases since January 2026, so prefer DQOps Cloud.
Datafold
AI-powered data engineering platform with agents for migrations and code reviews, plus data diff, impact analysis, and anomaly detection.
Best for data teams planning a warehouse migration or wanting data diffs in every pull request to catch regressions before merge.
Metaplane by Datadog
End-to-end data observability platform by Datadog that catches silent data quality issues with ML monitors and column-level lineage to BI dashboards.
Best for modern data stack teams that want monitors running in minutes from the UI, especially if they already use Datadog.
DQLabs
AI-native platform combining data observability, data quality checks, and business context, with anomaly detection, end-to-end lineage, and pipeline observability.
Best for enterprises that want data quality and observability in one tool, connected to their data catalog and issue management tools.
SelectZero
Self-hosted data observability platform with value-level data validation, column-level lineage, a data catalog, and a business glossary.
Best for enterprises that cannot let data leave their environment and want one self-hosted tool, deployed with Docker.
Lightup
Enterprise data observability platform with AI anomaly detection, no-code data quality checks, and an agentic interface that works with Claude and Gemini.
Best for enterprises that want business users to set up no-code checks at scale, with integrations for data catalogs and issue management tools.
Telmai
AI-based data observability platform for data lakes and lakehouses, with continuous data validation, anomaly detection, and data health reports.
Best for data teams monitoring data lakes and lakehouses with native support for Apache Iceberg, Hudi, and Delta Lake.
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By Ari Bajo - Data Engineer turned Writer.