Software advice
Best AI Data Catalog Software in 2026
AI data catalog software is a metadata platform that inventories an organization's tables, dashboards and pipelines, then uses machine learning or language models to write descriptions, answer questions in plain language and trace lineage. It is not a data warehouse and does not store the data itself. Buyers use it so analysts can find trusted data and so AI agents can read the business context around it.
Quick answer
Atlan ranks first for teams that want AI agents to write the documentation. Collibra suits formal governance programs. Dataedo fits mid-sized teams, including European ones, that want a published annual price instead of a sales call.
A data catalog is bought when nobody can say which table is correct, and the AI version promises to write the answer for you. The real question is who does the curation work: stewards, engineers, or a model whose drafts a person must still approve. This ranking covers twelve products, from enterprise governance suites to open source projects and cloud-metered services. We read each vendor's pricing, product and company pages in November 2026. We did not run accuracy tests on generated descriptions, so judgments describe design and published terms. Most vendors here publish no price, which is itself a finding.
The 12 best AI data catalog software in 2026, ranked
There is no score. Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | Atlan | Data teams that want AI agents to draft documentation and carry business context across tools | No | Quoted |
| 2 | Collibra | Large organizations that treat the catalog as one part of formal governance, with stewards, policies and AI oversight | No | Quoted |
| 3 | Alation | Analyst-heavy companies that want search and curation to learn from how people actually query their data | No | Quoted |
| 4 | Microsoft Purview | Microsoft-centered estates on Azure, Fabric and Power BI wanting a catalog billed by use | No | Pay as you go per governed asset (Microsoft's example: 200 governed assets cost $100 for 30 days) |
| 5 | Google Knowledge Catalog (formerly Dataplex Universal Catalog) | BigQuery-centered teams that want a metered catalog with Gemini-written enrichment and no sales contract | Yes | $0.06 per DCU-hour for standard processing (Iowa region), with 100 DCU-hours free each month |
| 6 | Dataedo | Mid-sized teams, including European ones, that want a published annual price and AI-written documentation | Trial | $18,000 per year (Essentials, 3 editors minimum) |
| 7 | Informatica Intelligent Data Management Cloud | Enterprises already running Informatica integration and quality that want the catalog and CLAIRE AI on the same platform | No | Quoted |
| 8 | OpenMetadata (Collate) | Engineering-led teams that want an open source catalog, with a managed option and agents when needed | Yes | Free tier with 5 users and 500 data assets |
| 9 | DataHub | Platform teams that want open source metadata plumbing and agent-facing context through MCP and APIs | Yes | Quoted |
| 10 | Actian Data Intelligence Platform (Zeenea) | European and regulated buyers wanting a catalog with knowledge graph, data products and EU AI Act use cases | No | Quoted |
| 11 | OvalEdge | Mid-market teams wanting catalog, lineage, glossary and access governance in one suite with an AI agent | No | Quoted |
| 12 | Databricks Unity Catalog | Teams whose data and models already live in Databricks and want governance without adding another catalog product | No | Quoted |
- 1
Atlan
Atlan leads because its product is built around AI doing the cataloging work: context agents that document tacit knowledge, plus connectors, lineage and a data marketplace on one metadata graph. The vendor sells the result as a context layer for other AI tools to read. It sits above Collibra on how openly it talks about agents, and below on the absence of any published price.
Strong on
- Context agents draft descriptions, glossary entries and metrics so stewards review text instead of typing it
- Connectors, lineage and a data marketplace share one metadata graph, so search and impact analysis read the same facts
Weak on
- No price or plan table on the pages read, so budgeting starts with a sales call
- Heavy emphasis on the context layer for AI may be more than a team that only needs search
Skip it if: Teams that need a flat monthly price they can put on a card, or a tool that runs fully inside their own network. Atlan shows no price list on the pages read; buyers are routed to a sales conversation.
- 2
Collibra
Collibra ranks second because it pairs a catalog with the heaviest governance machinery in this list. Its AI Copilot answers questions about data and business terms, automated curation classifies assets and drafts descriptions, and a separate AI Command Center traces AI agents in production. The cost is weight: expect a program with owners, not a tool a single analyst switches on.
Strong on
- AI Copilot answers natural-language questions about data assets and business term definitions
- Automated curation classifies data and generates descriptions, and the AI Command Center covers agent oversight
Weak on
- Pricing is not published, and the interactive tour is the only self-serve look
- Governance depth means setup effort that small data teams are unlikely to repay
Skip it if: Startups and small analytics teams that want a catalog running in a week without stewards or workflow design. No prices appear on the data catalog page; Collibra offers an interactive tour and a personalized demo instead.
- 3
Alation
Alation takes third place as the catalog most associated with analyst search. Its ALLIE AI recommends metadata descriptions and supports curation, search interprets natural-language questions, and the Open Connector Framework adds sources beyond its 120-plus prebuilt connectors. It trails Atlan and Collibra only on how little the pages read say about agents and AI governance, and it shares the no-price-list problem.
Strong on
- ALLIE AI recommends metadata descriptions and powers an intelligent curation workflow
- Natural-language search plus 120-plus connectors and an Open Connector Framework for custom sources
Weak on
- No published pricing or trial, only a conversation with sales
- Deployment options and data-handling terms were not stated on the pages read
Skip it if: Teams that need to compare list prices before talking to a vendor, or that want an open source core. Alation shows no price list; its pricing page only offers a 30-minute conversation with an Alation expert.
- 4
Microsoft Purview is fourth because it is the one large catalog with a metered price you can calculate. Unified Catalog charges per governed asset, and natural-language discovery uses Security Copilot. The catch is the bill has several meters, since Data Map capacity and scanning cost extra, and its value falls off sharply once the estate is mostly outside Microsoft.
Strong on
- Per-asset Unified Catalog billing, with Microsoft's own worked example of $100 for 200 governed assets over 30 days
- Natural-language discovery with Security Copilot and export of catalog data into OneLake for Fabric analytics
Weak on
- Data Map capacity units and scanning are billed separately, so one asset count does not give the total
- Strongest inside Microsoft services; coverage of other clouds needs checking per source
Skip it if: Companies running mainly on non-Microsoft stacks, or those that want a single flat license fee. Unified Catalog bills only assets linked to governance concepts; Data Map scanning and capacity are metered separately.
- 5
Google Knowledge Catalog (formerly Dataplex Universal Catalog)
Google Knowledge Catalog earns fifth place on pricing transparency and Gemini enrichment. The standard tier covers metadata discovery, Gemini enrichment and data insights bill by data tokens, and technical metadata from BigQuery is stored free. Natural-language exploration is still in preview as of November 2026. It trails Purview because lineage, quality and profiling sit in a pricier premium tier and the fit outside Google Cloud is thin.
Strong on
- Published meters: DCU-hours for processing, data tokens for Gemini enrichment, and free storage for auto-ingested technical metadata
- A monthly free tier of 100 DCU-hours on standard processing
Weak on
- Lineage, data quality and profiling fall under premium processing, which has no free tier
- Natural-language data exploration is in preview, with billing starting at general availability
Skip it if: Organizations whose data mostly lives in Snowflake, Databricks or on-premises systems. Pay as you go; Gemini enrichment is metered separately at $3 and $20 per million data tokens.
- 6
Dataedo
Dataedo is sixth, the only specialist here with a full public price table: $18,000, $24,000 or $32,000 a year with unlimited viewers, connectors and integrations, and a 14-day trial. The Auto AI Documentation feature drafts descriptions. It ranks below the big names because the AI story is narrower than Atlan or Collibra and the vendor is smaller, but few buyers find a clearer price.
Strong on
- Published annual tiers with unlimited viewers, connectors and integrations, plus a 14-day free trial
- Auto AI Documentation drafts descriptions, and the Data Lineage and Data Quality tiers add modules without per-user fees
Weak on
- Three editors minimum and a five-figure annual floor suit few small teams
- Agentic and MCP-style features were not described on the pricing page
Skip it if: Solo analysts and startups, or enterprises that need heavy workflow, policy and AI-agent governance. Three tiers at $18,000, $24,000 and $32,000 a year with unlimited viewers; a 14-day free trial is offered.
- 7
Informatica Intelligent Data Management Cloud
Informatica ranks seventh because its catalog is strongest as an add-on to the rest of the platform. CLAIRE AI is the engine behind its cloud data governance and catalog product, and the consumption-based IPU model lets customers spread spend across services. A buyer who does not already use Informatica pays for breadth they may not use, and the pages read show no catalog-only price.
Strong on
- CLAIRE AI runs across the platform's governance and catalog product
- IPU consumption pricing covers many cloud services, so catalog spend can move between modules
Weak on
- No standalone catalog price, and IPU math needs a vendor model to estimate
- Value depends on adopting more of the platform than the catalog alone
Skip it if: Teams that want a lightweight, catalog-only product without an enterprise data management suite. Informatica sells through Informatica Processing Units, a consumption model shared across its cloud services; no catalog price list is shown.
- 8
OpenMetadata (Collate)
OpenMetadata is eighth, the best route for teams that want open source with a managed escape hatch. Collate runs it as a service and adds AskCollate, a documentation agent, a tiering agent, a quality agent, a SQL agent and MCP support. The free tier is small at 500 assets. Self-hosting the open source version means you carry upgrades, and the AI agents belong to the paid Collate layer.
Strong on
- Free managed tier with unlimited connectors, lineage, quality and governance up to 5 users and 500 assets
- Collate AI agents cover documentation, tiering, quality tests and SQL, and the page lists SOC 2 Type II
Weak on
- The 500-asset free cap is reached quickly on a real warehouse
- Premium and Enterprise prices are not published, and the AI agents are part of the paid layer
Skip it if: Business-led governance programs that expect stewards to work in a polished workflow without engineering support. Collate's free tier covers 5 users and 500 assets; Premium (25 users, 5,000 assets) and Enterprise are quoted via demo.
- 9
DataHub
DataHub is ninth because its strength is engineering depth rather than steward-friendly polish. The vendor describes a context platform for AI agents with Ask DataHub, an MCP context kit, lineage, impact analysis and data contracts, and an open source community it puts at 3,000-plus organizations. It sits below OpenMetadata only because the pages read do not publish a free managed tier or any cloud price.
Strong on
- Ask DataHub and an MCP context kit expose metadata to agents and tools such as Claude and LangChain
- Open source Core alongside the managed DataHub Cloud, with lineage, impact analysis and data contracts
Weak on
- DataHub Cloud pricing and plan limits were not published on the pages read
- Self-hosting needs engineering time, and the Cloud versus Core feature split must be checked
Skip it if: Business teams without engineers, who need hosted onboarding and a published price. DataHub Core is an open source project; DataHub Cloud pricing is not shown and is reached through a demo request.
- 10
Actian Data Intelligence Platform (Zeenea)
Actian's Data Intelligence Platform, which the Zeenea site now presents under the Actian name, comes tenth. It lists a knowledge graph, data products and contracts, an MCP server and an AI Analyst, and names GDPR and the EU AI Act among its regulation pages. SOC 2 Type II and ISO 27001 are stated. It ranks lower because no pricing, plan or trial detail was published.
Strong on
- Knowledge graph, data products and contracts, MCP server and an AI Analyst on one platform
- States SOC 2 Type II and ISO 27001, with use-case pages for GDPR and the EU AI Act
Weak on
- No public pricing; evaluation runs through a tour or demo
- The Zeenea brand now redirects into Actian, so product naming and roadmap need confirming
Skip it if: Teams that want a free tier or a self-serve trial before speaking to anyone. No prices are published; the site offers a product tour and a demo, and Zeenea's own domain now shows Actian.
- 11
OvalEdge
OvalEdge takes eleventh as a unified governance suite: catalog, lineage, glossary, data quality, privacy and access in one product. Its AskEdgi agentic analytics assistant is the AI feature the site leads with, and it advertises 170-plus prebuilt connectors. It is placed below the larger names because the pages read give no pricing, trial or data-handling detail.
Strong on
- Catalog, lineage, glossary, quality and access governance sold as one suite
- AskEdgi agentic analytics across systems, and 170-plus prebuilt connectors
Weak on
- No published pricing or trial, only a demo and a tour
- Data-handling terms were not checked; the vendor's policy was not checked for this
Skip it if: Buyers who need a published price list or an independent open source option. OvalEdge lists no prices on the pages read; buyers book a demo or take a product tour.
- 12
Databricks Unity Catalog
Unity Catalog closes the list because it is a governance layer inside Databricks, not a cross-stack catalog. Databricks describes it as governing data and AI assets, enforcing access control, tracking lineage and logging activity beneath every interaction. That makes it close to free of extra effort for Databricks shops. The pages read do not describe AI-written documentation, so the AI link here is governance of models.
Strong on
- Governs tables and AI models together, with access control, lineage and audit logging in the workspace
- No second product to buy or run for teams already on Databricks
Weak on
- Scope is the Databricks estate, so other warehouses and BI tools need another catalog
- The pages read give no separate price and no AI-written documentation feature
Skip it if: Organizations spread across Snowflake, BigQuery and on-premises systems that need one neutral catalog. Unity Catalog is built into Databricks workspaces; the pages read give no separate catalog price.
How these AI data catalog software were judged
How good are the AI-written descriptions and natural-language answers?
Nobody publishes an accuracy score for generated descriptions, so the useful question is what the model reads and who approves its output. Atlan, Alation, Collibra, Dataedo and Collate each describe AI that drafts descriptions, while Google bills Gemini enrichment by token. Search quality depends on usage signals, lineage and a clean glossary more than on the model. Pick two real tables with confusing columns, let each tool describe them, and have the data owner mark every sentence right, wrong or vague. Count how many edits a steward needs per asset.
What does an AI data catalog cost per year?
Published prices are rare. As of November 2026, Dataedo lists $18,000, $24,000 and $32,000 a year with a three-editor minimum. Google lists $0.06 per DCU-hour for standard processing in Iowa after 100 free hours, and Microsoft's pricing page works an example of 200 governed assets at $100 for 30 days. Collate offers a free tier of 500 assets. Atlan, Collibra, Alation, Informatica, Actian and OvalEdge route you to sales. Ask for a quote based on your asset count, editor count and connector list, then compare it against the metered options.
Does the catalog read your actual data or only metadata?
Most catalogs read schemas, query logs and usage metadata, and some also sample rows for profiling and PII detection, so ask exactly what leaves your environment. Collate states SOC 2 Type II, CCPA and GDPR, Actian states SOC 2 Type II and ISO 27001, and Atlan points to a trust center and data processing agreement. For Collibra, Alation, Dataedo and others, the vendor's policy was not checked for this. Ask whether AI features send metadata to a third-party model, which region hosts it, and whether prompts are retained.
How hard is it to leave a data catalog later?
Leaving costs the curation, not the connectors. Descriptions, glossary terms, owners, certifications and lineage corrections are the asset you built. Open source cores such as OpenMetadata and DataHub keep the metadata model in public schemas, and Atlan, Actian and DataHub mention MCP or open APIs for reading context. Platform-bundled catalogs from Microsoft, Google and Databricks tie that curation to one cloud. Before signing, ask for a full export of glossary, ownership and lineage overrides, and try it on a trial workspace.
Which catalog fits a small data team versus a regulated enterprise?
Match the tool to who will do the stewardship. A team of three analysts with a Databricks or BigQuery stack may get enough from Unity Catalog or Google Knowledge Catalog with no extra vendor. A mid-sized firm that wants a known annual cost can start with Dataedo or the Collate free tier. Regulated enterprises with data owners, policy workflows and audit duties are the audience for Collibra, Alation and Informatica. If AI agents are the main consumers, test Atlan, DataHub and Actian for how they expose context.
How to choose an AI data catalog in three steps
- List the sources you must cover. Write down every warehouse, BI tool, pipeline and SaaS system that needs to appear in the catalog, then check each vendor's connector list against it. Platform-bundled catalogs win when almost everything sits in one cloud and lose when half your estate is elsewhere. Count the sources that need column-level lineage, since that is where gaps hide. A catalog that covers 80 percent of your sources with weak lineage will still leave analysts guessing, so treat missing connectors as a ranking factor, not a footnote.
- Decide who curates and who approves. Name the people who will review AI-written descriptions and own the glossary. If you have stewards and a data governance lead, a governance suite fits. If a few engineers will do it in spare hours, pick a catalog with strong automation and open APIs. Ask each vendor how drafts are approved, whether changes are logged, and what happens to descriptions when the schema changes. A tool nobody curates becomes a stale wiki within a quarter, however good its AI is.
- Run a two-week trial on messy tables. Choose ten real assets, including confusing, undocumented and duplicated ones, and ask each shortlisted tool to describe, classify and trace them. Time the setup, record how many drafts a steward accepts untouched, and ask three business questions in natural language. Request a written quote at the end of the trial based on your actual assets, editors and connectors. Where a vendor offers only a demo, ask for a proof of concept license, as Dataedo does. Keep the same ten assets for every tool so results stay comparable, and ask the data owner, not the vendor, to judge the drafts.
Frequently asked questions
What is the best AI data catalog software in 2026?
Atlan ranks first in this list for teams that want AI agents to draft documentation and carry context to other tools. Collibra is the pick for formal governance programs, and Dataedo is the pick for mid-sized teams that want a published annual price. The ranking rests on published product and pricing pages read in November 2026, not accuracy tests.
Is there a free AI data catalog?
Yes, in limited form. Collate offers a free managed tier with 5 users and 500 data assets, and OpenMetadata and DataHub Core are open source. Google Knowledge Catalog includes 100 free DCU-hours of standard processing each month. Dataedo offers a 14-day free trial. Collibra, Alation, Atlan and Informatica showed demos or tours rather than free plans.
How much does a data catalog cost for a 100-person company?
The pages read give one clear annual figure: Dataedo starts at $18,000 a year with at least three editors and unlimited viewers. Cloud-metered options such as Purview and Google depend on assets and processing hours. Enterprise vendors quote privately. Budget from a written quote based on your sources and editors, not from a headcount.
What is the difference between a data catalog and data governance software?
A data catalog helps people find and understand data, while governance software enforces policies, ownership and approvals around it. Many products here do both. Collibra and Alation lean toward governance workflows, Atlan and OvalEdge market catalog plus governance, and Unity Catalog focuses on access control inside Databricks.
Can an AI data catalog replace a data steward?
No vendor page read here claims that. Atlan, Alation, Collibra and Dataedo describe AI that drafts descriptions and recommends metadata, which a person still reviews. The saving is typing time and coverage. Ownership, certification and policy decisions still need people, and drafts on sensitive columns should be checked before publishing.
Should I choose an open source data catalog?
Choose open source when you have engineers who will run upgrades, write ingestion and customize the model. OpenMetadata and DataHub both publish an open source core. The trade is that managed services, SSO tiers and AI agents often sit in the paid layer, so compare what you get free against what your team needs.
Do data catalogs send my data to AI models?
It depends on the feature and vendor. Google bills Gemini enrichment by data tokens, and Collate, Actian and Atlan publish compliance or trust material. For most vendors here, the vendor's policy was not checked for this. Ask which metadata or sample rows reach a model, where it is hosted and whether prompts are retained.
How long does it take to roll out a data catalog?
Connecting sources can take days, but useful curation takes longer. Cloud-native and bundled catalogs can show lineage quickly once connectors run. Governance suites add stewardship design, policies and training, which adds weeks or months. None of the vendor pages read here publish a rollout time, so ask for a plan and reference customers.