Buying snapshot
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Decision point | What Secoda states |
|---|---|
Ownership | Secoda's website announces: "Atlassian acquires Secoda to turn data into knowledge." |
What it reads | Metadata, not your data. For most integrations that means resource names, popularity, lineage, queries, descriptions and frequent usage. |
Deployment | Single-tenant deployment and VPC peering are in the Premium plan list; self-hosted deployment is in the Enterprise plan list. |
Governance controls | The Govern feature list includes AI personas, automated governance workflows, audit logs, customizable role-based access controls (RBAC), groups and SIEM logging. |
Security | SOC 2 compliant; SAML, SSO and MFA; SSH tunneling; data encryption. Permissions can be managed from Okta or Active Directory. |
Warehouses and BI tools | Snowflake, BigQuery, Redshift, Databricks, Postgres, Oracle, Microsoft SQL, MySQL and S3; Tableau, Looker, Metabase, Redash, Mode, Sigma, Power BI and Google Data Studio. |
Collaboration and work tools | Slack and Microsoft Teams; productivity extensions for Confluence, Git, Jira, GitHub, Linear and PagerDuty. |
Our take
Choose Secoda if your problem is scattered data knowledge: many business users who need to find and trust data, and a small group who document and govern it. It is a weaker fit when only a few engineers would ever use the catalog, because much of its value comes from unlimited viewers searching and asking questions on their own.
Evaluate Secoda
Comparisons
Where it stands out
One product for several jobs. Secoda says it consolidates monitoring and observability, catalog, lineage and documentation in one central platform. Fewer tools to buy and connect, and one place where readers search.
Automated lineage, including dbt. Column- and table-level lineage is automated and also brings in tests, events and ETL. Secoda works with dbt Cloud and Core and imports dbt tests, tags, metrics and column-level lineage. Users can add lineage by hand through the API.
AI built on the metadata layer. Secoda describes a "metadata control plane" with named agents for analysis, automation, search, memory, observability, governance, documentation, visualization and cataloging, plus an AI documentation generator, a Chrome extension and AI chat in Slack.
Metadata changes under version control. Secoda keeps a Git version of metadata changes in GitHub or GitLab, so a team can review, merge and roll back changes to its workspace.
Fewer repeat questions. A data request manager and a searchable questions repository aim to answer each question once instead of in ad-hoc threads.
Product screenshot
Trade-offs to weigh
Change of owner. With Atlassian acquiring Secoda, roadmap, packaging and contract terms may change. Raise this in any multi-year contract talk.
Compliance controls sit in higher plan lists. Policies and PII scanning first appear in the Premium list; SIEM logging, custom roles and access request management in the Enterprise list. Map your compliance needs to these lists and confirm with Secoda which plan covers each one before you compare offers.
Check which plan includes the API. Core's plan list includes API access, but the pricing FAQ says the API is available on "Business and Enterprise plans", names that differ from the current plans. Get the answer in writing.
Breadth over depth. Monitoring is one of several product areas. Its feature list covers no-code data monitoring, schema change alerts, data profiling, usage insights, impact and root cause analysis, Data CI/CD, and cost and performance monitoring. A team whose main pain is pipeline incidents should test these monitors on its own pipelines before replacing a dedicated observability tool.
Integration allowance. Unlimited integrations is an Enterprise plan item. If you connect many warehouses and BI tools, ask what allowance applies on Core or Premium.
Pricing and plan conditions
Secoda sells three plans: Core, Premium and Enterprise. On all three, viewers and teams are Unlimited, while editors and admins, and workspaces, are Custom. So readers do not add to the bill; the number of people who write documentation and manage metadata, and how many workspaces you need, are the terms to settle with Secoda. Know those numbers, and the integrations you need, before you ask for an offer.
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Plan | Positioned for | Items the plan lists |
|---|---|---|
Core | Essential tools for data governance, lineage, monitoring and AI-powered workflows | Column and table-level lineage, full data catalog, automations, API access, home page and inbox, workspace analytics, SAML, SSH / reverse SSH, RBAC |
Premium | Growing teams advancing data governance with automation, policies and quality scoring | Data Quality Score, guest accounts, policies, single-tenant deployment, PII scanning, VPC peering, premium support |
Enterprise | Large teams with sensitive data, high security needs, access controls and custom deployment | Custom roles, self-hosted deployment, priority support and dedicated account manager, access request management, SIEM logging, unlimited integrations, disaster recovery support, professional services |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
Not available Google Trends search interest0 Hacker News matching stories, 90d
See all signals from 3 sources
Three-month score against stable baseline terms—not search volume or adoption.
Alternatives to Secoda
The reviewed substitutes for Secoda among the data catalogs, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Alation
- For data search, documentation and governance, Alation and Secoda cover the same ground. Alation leans toward enterprise stewardship, with search that learns from how people query data, while Secoda keeps the catalog, lineage, a data dictionary and data requests in one AI-assisted workspace that also serves revenue and business teams.Applies to: Choosing between these two for the data catalog governance decision.
- Atlan
- Atlan and Secoda are collaborative data catalogs that put search, lineage and documentation in one place. Atlan puts the weight on a shared layer of business logic and institutional knowledge that people and AI tools can read, while Secoda adds a data dictionary, analysis and data requests to the same searchable workspace.Applies to: Choosing between these two for the data catalog governance decision.
- DataHub
- A team setting up data discovery and documentation can choose DataHub or Secoda. DataHub suits engineering-led teams that want open-source software they can host and extend, while Secoda is a commercial, AI-assisted workspace that also handles a data dictionary and data requests for business users.Applies to: Choosing between these two for the data catalog governance decision.
- OpenMetadata
- OpenMetadata and Secoda each offer data discovery, lineage, documentation and quality in one platform. OpenMetadata is open-source software a team can host itself, built on standard metadata schemas and APIs, while Secoda is a commercial workspace with AI help and data requests that reaches business users as well as the data team.Applies to: Choosing between these two for the data catalog governance decision.
- Castor (now Coalesce Catalog)
- Secoda and Castor are hosted data catalogs that make company data as easy to search as the web. Secoda adds lineage, a data dictionary, analysis and data requests to the catalog, with AI help, while Castor keeps its focus on automated documentation that gives every data user the context of a table before using it.Applies to: Choosing between these two for the data catalog governance decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Monte Carlo
- A catalog organises assets for discovery and governance; an observability platform detects incidents and monitors reliability. Lineage and metadata overlap enough that vendors on both sides publish guidance on whether one covers the other, and teams with a trust problem rather than a discovery problem do buy the observability platform instead of a catalog. The decision is which capability the organisation needs first, and whether one product covers both.Applies to: Whether discovery and reliability need two products, or one platform can carry both.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Soda
- A validation framework defines and runs checks; a catalog stores and displays the results beside lineage, ownership and glossary. Catalogs integrate the check tools rather than replacing them, so the pair is deployed together and the reader's question is which job each one does.Applies to: Whether a data catalog removes the need for a separate checks tool, or reports what it found.
- Great Expectations
- A validation framework defines and runs checks; a catalog stores and displays the results beside lineage, ownership and glossary. Catalogs integrate the check tools rather than replacing them, so the pair is deployed together and the reader's question is which job each one does.Applies to: Whether a data catalog removes the need for a separate checks tool, or reports what it found.
See detailed alternatives analysis
If you are evaluating Secoda alternatives, you are likely looking for a platform that combines data cataloging, data quality monitoring, and governance in a way that fits your team's specific workflow and budget. Secoda positions itself as an AI-powered data enablement platform with catalog, lineage, documentation, and observability features. However, depending on your organization's size, technical maturity, and primary use case, other tools in the data quality and metadata management space may be a stronger fit.
Below is a practical breakdown of the leading Secoda alternatives, how they differ in architecture and pricing, and when it makes sense to switch.
Top Alternatives Overview
Alation is an enterprise data intelligence platform that unifies cataloging, governance, lineage, and data quality into a single hub. It uses a Behavioral Analysis Engine and supports over 120 pre-built connectors for data sources, BI tools, and cloud platforms. Alation is recognized as a Gartner Magic Quadrant Leader for Metadata Management Solutions and is used by large enterprises that need robust governance workflows and proven scalability. Its platform includes features like data stewardship, natural-language search, a SQL editor called Compose, and support for both cloud and on-premises deployment.
Atlan describes itself as "The Context Layer for AI" and functions as an active metadata platform. Rather than serving as a passive catalog, Atlan connects to your data stack through 80+ connectors and propagates governance context automatically across lineage paths. It is recognized as both a Gartner Magic Quadrant Leader and a Forrester Wave Leader for Data and Analytics Governance. Atlan emphasizes rapid deployment and a developer-friendly experience, with bidirectional metadata sync for platforms like Snowflake and Databricks.
Datafold is a data observability platform focused on preventing data incidents. It offers AI-powered code translation, automated data validation, and proactive issue detection. Datafold's open-source data-diff project accumulated over 2,900 GitHub stars, but Datafold archived it in May 2024 and no longer supports it.
Soda is an AI-native data quality platform built to catch, explain, and resolve data quality issues the moment they appear. Soda provides automated detection-to-resolution workflows and supports both a free tier and a paid Team tier. Its open-source library, Soda Core, has over 2,300 GitHub stars, reflecting a healthy community of contributors and users.
Elementary is a dbt-native data observability tool that provides automated anomaly detection, data lineage, and test results visualization directly within dbt projects. With over 2,300 GitHub stars and both a free self-hosted option and a cloud service, Elementary is designed for data and analytics engineers who already use dbt as their transformation layer.
Great Expectations is the most established open-source data quality framework in the space, with over 11,000 GitHub stars. It enables teams to define, execute, and document expectations about their data using a code-first approach. Paid upgrades are available for teams that want managed capabilities beyond the open-source core.
Architecture and Approach Comparison
The alternatives to Secoda fall into distinct architectural categories that reflect fundamentally different philosophies about how data teams should manage quality and governance.
All-in-one platforms (Secoda, Alation, Atlan) aim to centralize catalog, lineage, governance, and observability into a single product. Secoda bundles AI-powered search, documentation, automations, monitoring, and data quality scoring along with specialized AI agents for analysis, governance, and cataloging tasks. Alation takes a similar comprehensive approach but adds a SQL editor (Compose), data stewardship workflows, and an extensive connector library built over a longer market history. Its Behavioral Analysis Engine learns from user interactions to surface the most relevant data. Atlan differentiates by treating metadata as an active, bidirectional layer that pushes context back into source systems like Snowflake and Databricks, rather than requiring users to come to the catalog.
Observability-focused tools (Datafold, Soda, Elementary, Anomalo, Bigeye, Metaplane, Validio) concentrate on detecting and resolving data quality issues in pipelines. These tools typically integrate into CI/CD workflows or run alongside transformation tools like dbt. They prioritize alerting, root cause analysis, and automated monitoring over documentation and search. Teams that already have a catalog but need deeper pipeline monitoring often pair one of these tools with their existing catalog rather than replacing it entirely.
Code-first frameworks (Great Expectations) give engineering teams full control over data validation logic through programmatic expectation definitions. This approach trades the convenience of a managed UI for maximum flexibility, transparency, and version control. Great Expectations integrates naturally into existing testing pipelines and CI/CD systems, and its expectations can be stored alongside application code in Git repositories.
Enterprise governance platforms (Alation, Validio, Anomalo, Bigeye) emphasize compliance, policy enforcement, and audit trails. Alation in particular has deep roots in enterprise environments, with features like data stewardship, access controls, RBAC, and detailed lineage tracking designed for organizations in regulated industries that need comprehensive documentation of their data handling practices.
A key architectural distinction is deployment flexibility. Secoda offers Core, Premium, and Enterprise tiers with options including single-tenant and self-hosted deployment. Alation supports both cloud and on-premises deployment. Elementary and Great Expectations can be fully self-hosted. Datafold also offers a self-hosted deployment. This variety means teams with strict data residency or security requirements can find options across most price points.
Pricing Comparison
Secoda’s pricing page presents three plan names: Core, Premium, and Enterprise. It describes Core as covering modern data-governance, lineage, monitoring, and AI-powered workflow tools; Premium as adding governance automation, policies, and data-quality scoring; and Enterprise as targeting larger teams with security, access-control, and custom-deployment needs.
The pricing page does not list public monetary prices, billing terms, or a per-user, per-seat, workspace, storage, or usage basis for any of those plans. Buyers evaluating Secoda should therefore confirm the price, contract term, included editor/admin and workspace allowances, implementation or professional-services scope, and which plan is required for features such as PII scanning, single-tenant deployment, self-hosting, custom roles, SIEM logging, and premium or priority support.
A like-for-like price comparison with other tools is not reliable from the public documentation because it provides no verified competitor pricing or licensing terms. Use the confirmed feature scope and the missing commercial details above as the questions to resolve during procurement.
When to Consider Switching
Switching from Secoda makes sense in several specific scenarios, each driven by a gap between what Secoda offers and what your team actually needs.
You need deeper pipeline observability. If your primary pain point is detecting and resolving data quality issues in production pipelines rather than cataloging and documentation, tools like Soda, Datafold, Elementary, or Metaplane are purpose-built for this use case. They integrate tightly with dbt, Airflow, and CI/CD workflows to catch issues before they reach downstream consumers. Secoda includes monitoring features, but dedicated observability tools typically offer more granular alerting, deeper root cause analysis, and faster incident resolution.
You need enterprise-grade governance at scale. For organizations in regulated industries that require comprehensive data stewardship, policy enforcement, audit trails, and compliance documentation, Alation offers deeper governance capabilities backed by years of enterprise deployments. Its 120+ pre-built connectors and Behavioral Analysis Engine are designed for complex, multi-source enterprise environments where governance is not optional but mandated.
You want an active metadata platform. If your goal is to have governance context flow automatically across your data stack rather than requiring manual documentation, Atlan's bidirectional metadata sync with Snowflake, Databricks, Looker, and other tools provides a fundamentally different approach than a traditional catalog. Instead of relying on human stewards to maintain documentation, Atlan propagates tags and context automatically across lineage paths.
You prefer open-source control. Teams that want full visibility into validation logic, the ability to customize rules, and freedom from vendor lock-in may find Great Expectations or the self-hosted versions of Elementary and Datafold more aligned with their engineering culture. Code-first approaches also mean that data quality rules live in version control alongside application code.
Your team is dbt-native. If dbt is the center of your transformation workflow, Elementary's native integration with dbt projects provides anomaly detection and observability without adding another separate platform to manage. This reduces context switching and keeps observability within the same tooling your data engineers already use daily.
Budget constraints are significant. If Secoda's paid tiers exceed your budget but you still need data quality tooling, the open-source options and lower-cost entry points from Elementary, Metaplane, or Atlan's free tier may provide the functionality you need. Combining Great Expectations (free) with Elementary ($10/month) can deliver substantial coverage at minimal cost.
Migration Considerations
Moving from Secoda to another platform requires careful planning around several dimensions.
Metadata and documentation export. Secoda stores catalog entries, documentation, data dictionary terms, and lineage information. Before migrating, inventory which assets have been manually documented versus auto-generated. Manual documentation represents the highest-value content to preserve during migration. Secoda provides API access on paid tiers, which can simplify automated extraction of your documented metadata.
Integration overlap. Map your current Secoda integrations (data warehouses, BI tools, orchestrators) against the connector library of your target platform. Alation offers 120+ connectors, Atlan provides 80+, and observability-focused tools typically cover fewer sources but with deeper pipeline integration. Verify that your critical data sources are supported before committing to a migration.
Team workflow disruption. If your team has adopted Secoda's AI search, question-and-answer features, or automation workflows, switching to a tool with different interaction patterns will require a transition period. Plan for onboarding time and training, especially if moving from an all-in-one platform to a more specialized tool that covers only a subset of your current workflow.
Governance policy migration. If you have established RBAC configurations, access policies, or PII scanning rules in Secoda, document these thoroughly before migrating. Enterprise platforms like Alation and Atlan support similar governance features, but the configuration details will differ and each platform has its own model for roles, permissions, and policy enforcement.
Cost of parallel operation. Running two platforms simultaneously during migration is common but adds temporary cost. Factor in the overlap period when budgeting for the transition, and establish clear milestones for when the old platform can be decommissioned. For teams migrating to specialized tools, a phased approach where you move one capability at a time (observability first, then cataloging) can reduce risk.
Deployment model compatibility. If you are on Secoda's self-hosted or single-tenant deployment, ensure your target platform supports an equivalent deployment model. This is particularly important for organizations with strict data residency or security requirements. Alation, Elementary, Great Expectations, and Datafold all offer self-hosted options.