Secoda.
Founded 2021 · Toronto, Ontario, Canada
Status · ● acquired
AI-native data catalog, lineage, and observability from Toronto — acquired by Atlassian in December 2025 to power Rovo AI.
Where it fits — and where it doesn't.
Mid-market and scaleup data teams that want one AI-native tool covering catalog, search, lineage, documentation, and basic observability rather than running separate catalog, lineage, and monitoring tools — especially teams that value a natural-language assistant for self-serve data questions and broad business-user adoption. A strong fit for organisations on Snowflake, BigQuery, or Databricks plus dbt and a modern BI tool who want fast time-to-value and lighter governance overhead than enterprise suites like Atlan or Collibra.
You require open-source or self-host portability of the metadata graph (it is proprietary), OpenLineage conformance, or deep code-first data-quality testing with circuit breakers — pair or replace with Soda, Great Expectations, Monte Carlo, or Anomalo for serious quality engineering. Acquired by Atlassian (Dec 2025); planned migration into the Atlassian Cloud Platform.
The honest scorecard.
- AI-native search and assistant as the primary interface — natural-language data questions across the catalog, plus purpose-built agents for search, documentation, observability, and governance
- Consolidated — catalog, data dictionary/glossary, column- and table-level lineage, governance, and no-code monitoring in one workspace
- Strong automated lineage including column-level, BI-tool coverage, impact analysis, and downstream/upstream owner notifications
- Fast time-to-value and broad business-user adoption relative to heavyweight enterprise catalogs, with 50+ no-code connectors
- Python SDK and REST API for custom integrations and pushing lineage/metadata
- No public pricing — all tiers are contact-sales, making evaluation harder for smaller teams
- Proprietary with no free open-source or self-host path; self-hosting is a paid Enterprise option only
- No OpenLineage support, and lighter-weight quality monitoring than dedicated tools (no circuit breaker, no code-first assertion DSL; anomaly detection is largely baseline/threshold-driven)
- The Atlassian acquisition (Dec 2025) introduces roadmap and platform-migration uncertainty for buyers wanting a standalone product
- Data contracts and deep column profiling are not first-class capabilities
What Secoda actually is.
What Secoda is
Secoda is an AI-native data catalog, search, lineage, and observability platform that consolidates metadata from across the stack into one workspace, with a natural-language assistant (and a set of AI agents) layered on top for discovery, documentation, and analytics. It bundles cataloging, a data dictionary and glossary, column- and table-level lineage, no-code monitoring, and governance (policies, access requests, RBAC) into a single tool. Founded in Toronto in 2021 (Y Combinator), it was acquired by Atlassian in December 2025, which intends to integrate it into the Atlassian Cloud Platform and Rovo.
Where it fits
Secoda cross-shops most directly with atlan, datahub, and openmetadata as a catalog/discovery plane, and with unity-catalog for teams already on Databricks. Against Atlan it positions as faster-to-value, lighter-weight, and more self-serve for mid-market teams; against the OSS catalogs it trades open-source portability for a polished AI-first UX and bundled observability. It is not a dedicated data-quality engine like monte-carlo, anomalo, or soda — its monitoring is consolidated and metadata-driven rather than deep test authoring, which is why we score the quality-testing cluster zero.
On the Atlassian acquisition
Atlassian announced the deal in December 2025, framing Secoda as the semantic-cataloging layer for its Teamwork Graph and Rovo AI. As of mid-2026 Secoda still runs under its own brand with the founders aboard, and the company says the near-term customer experience won’t change. The product is strong today and the team is intact. The open question is the multi-year horizon: as Secoda migrates into the Atlassian Cloud Platform and Rovo, standalone availability and pricing are the things to confirm in a sales conversation.
How to evaluate it
Because pricing is contact-sales, scope a trial around the AI-native experience that differentiates it: connect a representative slice of your stack and judge whether the natural-language assistant answers real, self-serve data questions accurately, and whether the automatic column-level lineage reaches across dbt and your BI tool. Treat the bundled monitoring as a convenience layer, not a replacement for a dedicated quality engine — and ask directly about the Atlassian-platform roadmap.
All capabilities by cluster.
Catalog & discovery
Primary · strength 3/3Lineage & metadata
Secondary · strength 3/3Where it plugs in.
Native warehouse support
The honest pricing breakdown.
Sales-only tier Core / Premium / Enterprise — all routed to contact-sales with no listed numbers; PII scanning and self-hosted deployment are Premium/Enterprise-gated
What it doesn't do.
Emits and consumes OpenLineage events as a first-class citizen rather than via a plugin or adapter. Signals commitment to interoperability with other metadata tooling — Marquez, OpenMetadata, Astronomer, and others can consume the same event stream. Increasingly the differentiator between "open" and "proprietary metadata model" observability platforms.
Data Contracts →Explicit, versioned agreements between data producers and consumers specifying schema, semantics, SLAs, and breaking-change policy. Enforced in CI for producers and at consumption time for consumers. Distinct from schema validation alone — a contract captures intent, not just structure. Implementations vary wildly; many tools claiming "data contracts" offer only schema checks.
Circuit Breaker →Halts downstream execution when a test fails — preventing bad data from propagating into marts, ML features, or BI dashboards. Requires tight integration with the orchestrator (Airflow, Dagster, dbt Cloud). Distinct from alerting-only tools which notify after damage is done.
ML Anomaly Detection →Uses machine learning models trained on historical data to detect values, volumes, or distributions outside expected bounds — without requiring the user to write explicit assertions. Reduces the "I didn't know to test for that" class of incident. Trade-off: requires a training window (typically two to four weeks), can produce false positives on seasonal data, and doesn't replace assertions for business-rule validation.
Drill into one capability.
Other key features
If not Secoda, then what?
Common alternatives
Quick answers.
- Is Secoda open source?
- No. Secoda is a proprietary product.
- How much does Secoda cost?
- Secoda does not publish list pricing — it is sales-led, so you request a quote. There is no free tier.
- How is Secoda deployed?
- Secoda can run as managed SaaS or be self-hosted.
- Does Secoda work with dbt and my warehouse?
- It has a native dbt integration. Secoda supports snowflake, bigquery, redshift, databricks, postgres, plus 3 more.
More catalog & discovery tools
Provenance.
Last verified 2026·05·30 against vendor documentation and, where possible, hands-on trial. Spot something off? Send a correction →