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Best Data Catalogs mark: an unfinished lineage pathBest Data Catalogs

2026 edition

Best Data Catalogs for AI Agents in 2026 [Ranked & Scored]

Short answer

DataHub ranks first at 8.02 out of 10, followed by Euno at 7.64 and Atlan at 7.25. We scored nine platforms on how fast and how reliably they make enterprise data usable by AI agents: agent access, lineage, automation, governance, business definitions, answer checks, integrations, pricing and deployment. The right pick still depends on your stack, so every score is explained and the weights are yours to change.

By The Lineage Desk · Best Data Catalogs · Published

Desk research from public vendor pages. Editorial assessment.

Key takeaways

  • DataHub ranks first at 8.02; Euno (7.64) and Atlan (7.25) follow. The gap between first and third is 0.77 points.
  • Euno ranks 2 of 9. It is highest in set on Lineage, Automation, Governance and Answer checks and lowest in set on Integrations and Deployment.
  • Context platforms combine lineage, automated context and agent access. Semantic layers focus on metric definitions. Catalog and governance platforms focus on inventory and certification. All three now offer some route for agents.
  • Pricing is rarely public: Cube and Honeydew publish rates, DataHub Cloud has an AWS Marketplace list price, Euno publishes tiers and limits without dollar figures, and the other five publish no prices.
  • Snowflake Horizon and Databricks Unity Catalog are covered as platform-native options, not scored. Stardog, data.world, Modus and Jedify were reviewed but not scored because our sources did not cover enough criteria.

How did we score these platforms?

Each platform gets a whole-number score from 0 to 10 on nine criteria, with a one-line reason linked to the vendor page it comes from. The total is the weighted average, computed in code. We read only public vendor pages, with scores linked to those sources.

CriterionWeightQuestion
Agent access to context15%How do AI agents get the context: an MCP server, context APIs, SDKs, named agent clients?
Cross-stack lineage13%Does lineage run from the warehouse through transformation and BI to the agent, at column level, as the vendor documents it?
Automated context build13%How much of the context is mapped, documented and kept current automatically, and how soon does the vendor say first value arrives?
Governance agents can use13%Can certification, sensitivity and access rules travel with the context, scoped to the agent or the person it acts for?
Business definitions and metrics12%Does it hold or extract metric and business definitions that agents can ask for by name?
Answer quality checks8%Does the vendor describe a way to test agent answers, catch context drift or trace what agents did?
Integration breadth10%How many sources and tools does it connect to, as published, and are there plan limits?
Pricing transparency8%Are prices, plan limits or a free tier published?
Deployment options8%Can you run it as SaaS, self-hosted, open source or inside your own warehouse?

Full scoring modelChange the weights

Which data catalog is best for AI agents?

Agent-readiness score, editorial assessment, 0 to 10
RankPlatformTypeAgent interfaceScoreBest for
1DataHubContext platform (open-source core)MCP, Agent Context Kit, Skills, APIs and SDKs, Ask DataHub
8.02 / 10
Teams that want an open-source core or self-hosting, many agent interfaces and a large connector base
2EunoAI context platformMCP server, Context API, Euno Assistant
7.64 / 10
Data teams that want agents grounded in lineage and governance across warehouse, dbt and BI without finishing a semantic layer first
3AtlanContext layer (metadata platform)Atlan MCP server, SQL, APIs, SDK
7.25 / 10
Large data teams that want a wide connector catalog with AI-drafted context and human certification
4AlationData catalog (AI operating system)Ontologies published as an MCP server (early access); Console natural-language routing (early access)
6.60 / 10
Enterprises already on Alation that want semantic models, ontologies and agent governance in one place
5CollibraGovernance platform (AI control plane)Collibra MCP Server (25+ governance tools)
6.27 / 10
Governance-led organizations that want agents to work only with certified data products
6AtScaleUniversal semantic layerAtScale MCP server (list_models, explore_columns, focus_columns, get_sml_skills, run_query, get_outbound_queries); AtScale Ava for chat users
5.87 / 10
Enterprises that need certified metrics served to many agent clients under per-agent security
7CubeSemantic layer (agentic analytics)MCP server, APIs, Analytics Chat API
5.86 / 10
Teams whose main gap is consistent metrics for agents and BI, and who want published pricing and an open-source core
8Select StarMetadata context platform (part of Snowflake)MCP Server for Data, Ask AI
5.77 / 10
Snowflake-centric teams that want automated documentation and column-level lineage
9HoneydewSemantic layer for AI and BIAI analyst use case; an MCP server was not confirmed on the pages we read
4.98 / 10
Teams that want one semantic model shared by BI and an AI analyst, priced in the open

How does each platform earn its place?

1.DataHub: an open-source context platform with many agent interfaces

Context platform
8.02 / 10
Type
Context platform (open-source core)
Agent interface
MCP, Agent Context Kit, Skills, APIs and SDKs, Ask DataHub
Lineage
Lineage and impact analysis built from query logs, dbt projects and BI dashboards
Deployment
Open source from a single node to cloud-hosted, hybrid and decentralized setups, or DataHub Cloud as managed SaaS.
Pricing
DataHub Core is open source. DataHub Cloud has no list price on datahub.com; an AWS Marketplace listing (Discover & Govern) shows $75,000 for a 12-month contract with up to 20 monthly active users.

Source: AWS Marketplace: DataHub Cloud listing

Entry price
$75,000 for 12 months (AWS Marketplace, up to 20 monthly active users)
Company
A $35M Series B led by Bessemer Venture Partners was announced on 21 May 2025, for $65M in total. Palo Alto, California; CEO Swaroop Jagadish, CTO Shirshanka Das.

Source: DataHub Series B announcement

Customers named
Named on DataHub's pages: Pinterest, Miro and Netflix.

Source: DataHub Cloud context platform

Best for
Teams that want an open-source core or self-hosting, many agent interfaces and a large connector base
DataHub context path: documents 5 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

DataHub pairs the widest set of documented agent interfaces with 100+ connectors, an open-source core and flexible deployment. It scores lower on lineage depth as documented, on governance scoped to agents and on answer testing.

Highest in set on: Agent access, Integrations and Deployment. Lowest in set on: none.

Key features

Pros

  • Many ways for agents to reach context: MCP, an Agent Context Kit, Skills, APIs and SDKs, with Claude, Cursor, Snowflake Cortex, Databricks Genie, CrewAI and LangChain named.
  • An open-source core that runs from a single node to hybrid setups.
  • A marketplace list price exists, which is rare in this category.

Cons

  • Column-level lineage depth and persona-scoped context for agents are not stated on the pages we read.
  • Direct pricing is not published; the only figure is a marketplace listing.
  • Answer checking is described as drift detection and expert routing rather than testing agent answers.

Bottom line

DataHub suits teams that want open source or self-hosting, many agent interfaces and a large connector base. It ranks 1 of 9 at 8.02.

Full DataHub reviewDataHub alternatives

2.Euno: a context graph built for agents

Context platform
7.64 / 10
Type
AI context platform
Agent interface
MCP server, Context API, Euno Assistant
Lineage
Column level, from sources through BI dashboards to AI agents
Deployment
Cloud-native SaaS; reads metadata only. No self-hosted or open-source edition described.
Pricing
Priced by the data assets you manage, with unlimited users and AI agents. Three tiers: Start (up to 150K resources), Scale (up to 1.5M resources) and Enterprise (any scale), each on request. No dollar prices are published.

Source: Euno pricing

Entry price
Not published
Company
Founded 2023 by Sarah Levy (CEO) and Eyal Firstenberg (CTO); based in San Francisco and Tel Aviv with nearly 30 people. A $23M Series A led by N47 was announced on 9 September 2026, for $29M raised in total.

Source: Euno blog: Euno raises Series A

Customers named
Named on Euno's pages: AlphaSense and Zayo Group.

Source: Euno blog: Euno raises Series A

Best for
Data teams that want agents grounded in lineage and governance across warehouse, dbt and BI without finishing a semantic layer first
Euno context path: documents 5 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Euno documents every hop from warehouse to agent, turns governance rules into labels agents can read, and describes how it tests agent answers. It loses points on integration breadth, pricing transparency and deployment options, and its business definitions come from extraction rather than a semantic layer of its own.

Highest in set on: Lineage, Automation, Governance and Answer checks. Lowest in set on: Integrations and Deployment.

Key features

  • Context for AI agents through MCP, assembled from four layers: semantic search and lineage insights, active metadata tags, usage insights, and personas (role, task and access scope).

    Source: Euno: Context for AI Agents

  • Column-level lineage from sources to BI dashboards and AI agents, including business definitions that live inside BI workbooks.

    Source: Euno: Context for AI Agents

  • Automated labeling: custom governance rules mark assets as certified, PII-free or healthy, and the labels update as the stack changes.

    Source: Euno homepage

  • Context Calibration: generates benchmark questions, checks agent answers and suggests missing rules and glossary terms.

    Source: Euno blog: AlphaSense x Euno at dbt Summit

  • dbt and Looker sync, impact analysis with CI integration, PII exposure tracking and a metadata query language (EQL).

    Source: Euno pricing

Pros

  • Lineage is documented at column level from the warehouse through dbt and BI to the agent, including logic kept inside BI workbooks.
  • Governance rules become live labels that travel with the context, so an agent can be told which assets are certified or PII-free.
  • A published loop for testing agent answers and finding missing rules and glossary terms.
  • Pricing by data assets with unlimited users and agents, so adding agents does not add seats.

Cons

  • No dollar prices are published; every tier is quoted on request.
  • SaaS only: no self-hosted or open-source edition is described.
  • Integrations are capped by plan (3, 5 or 10), and the full source list could not be read.
  • No public review-site ratings were found in our reads, and the company is young (founded 2023, nearly 30 people).

Bottom line

Euno is built around the questions an agent needs answered about a specific asset: where it came from, whether it can be trusted and who may use it. It ranks 2 of 9 at 7.64. Teams that need self-hosting, published prices or many connectors on an entry plan should read the cons first.

Full Euno reviewEuno alternatives

3.Atlan: a broad connector catalog rebuilt as a context layer

Context platform
7.25 / 10
Type
Context layer (metadata platform)
Agent interface
Atlan MCP server, SQL, APIs, SDK
Lineage
Relationship-level lineage in the data graph; column level not stated on the connectors page
Deployment
SaaS; Atlan states it never queries the rows in your tables. A self-hosted option is not stated.
Pricing
Not published The pricing page leads to a Talk to Sales form.

Source: Atlan pricing

Entry price
Not published
Company
Launched publicly in 2020. Series C of $105M led by GIC and Meritech Capital in May 2024; total funding over $200 million.

Source: Atlan about

Customers named
Quoted on Atlan's pages: Workday and Fox.

Source: Atlan about

Best for
Large data teams that want a wide connector catalog with AI-drafted context and human certification
Atlan context path: documents 5 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Atlan sits with the leaders on automated context building and agent access, and on integration breadth. It gives up points on lineage depth as documented, on pricing transparency and on deployment options.

Highest in set on: Automation and Integrations. Lowest in set on: Pricing and Deployment.

Key features

  • Bootstrap: AI agents generate descriptions and link terms, with a Metrics Generator, Semantic Views and an Ontology Generator.

    Source: Atlan homepage

  • 110 connectors on the connectors page, plus an open-source App SDK (Apache 2.0) for custom connectors in "days, not weeks".

    Source: Atlan connectors

  • Context served through SQL, APIs, an SDK and the Atlan MCP server, with evals and traces.

    Source: Atlan homepage

  • A Context Lakehouse described as Iceberg-native and open (2024), and Context Agents with a Context Engineering Studio (2026).

    Source: Atlan about

Pros

  • 110 connectors listed across warehouses, BI, ETL, semantic layers, ERP and CRM, plus an open SDK for custom ones.
  • AI drafts the context and people certify it, with a clear human review step.
  • Serves context to agents through an MCP server as well as SQL, APIs and an SDK.

Cons

  • No prices or tiers are published; pricing goes through sales.
  • Column-level lineage is not stated on the connectors page, and agent-scoped access is not described on the pages we read.
  • No self-hosted option is stated.

Bottom line

Atlan is the closest like-for-like alternative to Euno in this set: both build a context graph and serve it over MCP. Atlan brings many more published connectors and analyst placements; compare the two criterion by criterion on the head-to-head page. It ranks 3 of 9 at 7.25.

Full Atlan reviewAtlan alternatives

4.Alation: the catalog pioneer adding ontologies and AI governance

Catalog and governance
6.60 / 10
Type
Data catalog (AI operating system)
Agent interface
Ontologies published as an MCP server (early access); Console natural-language routing (early access)
Lineage
Lineage from SQL queries, scripts and query logs, plus agent lineage tracing
Deployment
Not stated on the pages we read.
Pricing
Not published The pricing page leads to a contact form. Alation says the new AIOS products are part of its all-inclusive pricing with no new contracts required.

Source: Alation pricing

Entry price
Not published
Company
Alation's homepage cites five Leader placements in the Gartner Magic Quadrant for Metadata Management (2017 to 2020 and 2025).

Source: Alation homepage

Customers named
Quoted on Alation's connectors page: VillageCare.

Source: Alation connectors

Best for
Enterprises already on Alation that want semantic models, ontologies and agent governance in one place
Alation context path: documents 4 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Alation scores well on integration breadth, governance and semantic models. It loses ground on agent access and automation because the relevant features were in early access, and on pricing and deployment because neither is published.

Highest in set on: Integrations. Lowest in set on: Deployment.

Key features

  • AI Governance (generally available): agent lineage tracing and registry connectors for Amazon Bedrock, SageMaker, Databricks MLflow, Microsoft Copilot Studio, Microsoft Foundry and Snowflake Cortex.

    Source: Alation press release: AIOS expansion with six products

  • Semantic Model Mastering (generally available): ingests Databricks and Snowflake semantic models, enriches and governs them, and syncs them back.

    Source: Alation press release: AIOS expansion with six products

  • Ontologies (early access): OWL and RDF concepts, SHACL constraints and Turtle export, drafted by agents from policies, glossary and lineage, and publishable as an MCP server.

    Source: Alation AIOS: Ontologies

  • 120+ pre-built connectors, lineage from SQL queries and scripts, query log processing, column-level profiling and the Compose SQL editor.

    Source: Alation connectors

Pros

  • 120+ pre-built connectors and a long track record as a catalog.
  • Semantic Model Mastering keeps Snowflake and Databricks semantic models governed and in sync.
  • AI Governance traces which data agents touched across several agent platforms.

Cons

  • Its MCP path and several new products (Ontologies, Intelligent Feeds, Console, Governed Collections) were in early access as of 17 September 2026.
  • No prices are published, and deployment options are not stated on the pages we read.
  • Testing of agent answers is not described.

Bottom line

Alation is a natural choice for teams already invested in it, now that semantic models and agent governance sit in the same product. For agent context, much of what matters was still in early access when we read its pages. It ranks 4 of 9 at 6.60.

Full Alation reviewAlation alternatives

5.Collibra: governance-first context through certified data products

Catalog and governance
6.27 / 10
Type
Governance platform (AI control plane)
Agent interface
Collibra MCP Server (25+ governance tools)
Lineage
Lineage carried in data products and explorable through the MCP server
Deployment
Not stated on the pages we read; the MCP server is described as deploy anywhere.
Pricing
Not published The pricing URL returned a page-not-found message.

Source: Collibra pricing URL

Entry price
Not published
Company
Collibra's homepage cites 78 Fortune 500 companies, five Forrester Wave Leader placements in data governance, two Gartner Magic Quadrant Leader placements (D&A Governance Platforms) and more than 2B data assets managed.

Source: Collibra homepage

Customers named
Customer stories on Collibra's homepage: SAP, McDonald's, Toyota Motor Europe, HEINEKEN and L'Oréal.

Source: Collibra homepage

Best for
Governance-led organizations that want agents to work only with certified data products
Collibra context path: documents 5 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Collibra is among the strongest on governance and has a capable MCP server. It scores lower on automation, answer checks, integrations as documented and pricing.

Highest in set on: Governance. Lowest in set on: Pricing.

Key features

  • MCP Server with 25+ governance tools out of the box: discover assets, explore lineage, manage classifications.

    Source: Collibra MCP Server

  • Product areas: Context Engineering, AI Command Center, Context Governance and Data Governance.

    Source: Collibra homepage

  • Deasy Labs for unstructured data such as emails, PDFs and SharePoint.

    Source: Collibra homepage

  • Java and REST APIs with accelerators for integration.

    Source: Collibra integrations and APIs

Pros

  • Certified data products carry policy constraints with them, so agents inherit the rules.
  • A ready MCP server with 25+ tools, described as token-efficient.
  • A large enterprise footprint, as Collibra reports it.

Cons

  • No prices are published.
  • Automated mapping and documentation are not described in detail, and the connector list did not render on the integrations page.
  • Testing of agent answers is not described.

Bottom line

Collibra fits organizations where governance leads and agents must work only with certified data products. For speed to agent-ready context it documents less automation than the context platforms. It ranks 5 of 9 at 6.27.

Full Collibra reviewCollibra alternatives

6.AtScale: certified metrics by name, with per-agent security

Semantic layer
5.87 / 10
Type
Universal semantic layer
Agent interface
AtScale MCP server (list_models, explore_columns, focus_columns, get_sml_skills, run_query, get_outbound_queries); AtScale Ava for chat users
Lineage
Not described (a semantic layer, not a catalog)
Deployment
Queries compute inside your data platform; hosting options are not stated on the pages we read.
Pricing
Not published Consumption pricing by deployed semantic objects, in Standard and Enterprise editions, with no per-seat or per-query fees. No prices are published.

Source: AtScale pricing

Entry price
Not published
Company
Not covered in our sources.
Customers named
Named on AtScale's pages: Vodafone Portugal (with Celfocus) and Blue Yonder.

Source: AtScale: semantic context for AI via MCP

Best for
Enterprises that need certified metrics served to many agent clients under per-agent security
AtScale context path: documents 4 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

AtScale is strong on agent access, governance for agents and business definitions. It scores low on lineage and automation because it is a semantic layer, and on pricing because no prices are published.

Highest in set on: Semantics. Lowest in set on: Lineage, Automation and Integrations.

Key features

Pros

  • Per-agent identity with row- and column-level security for agent queries.
  • Many named agent clients.
  • A pricing model without per-seat or per-query fees.

Cons

  • No catalog and no lineage across the stack.
  • No prices are published.
  • The stated path to results is a 30-day workshop, slower than the day-one or same-day starts the context platforms describe.

Bottom line

AtScale fits enterprises that need governed metrics served to many agent clients under strict security. It covers the metric layer, not the wider map of the stack. It ranks 6 of 9 at 5.87.

Full AtScale reviewAtScale alternatives

7.Cube: an open semantic layer with published prices

Semantic layer
5.86 / 10
Type
Semantic layer (agentic analytics)
Agent interface
MCP server, APIs, Analytics Chat API
Lineage
Not described (a semantic layer, not a catalog)
Deployment
Cube Core is open source (cube.dev shows 21,018 GitHub stars); Cube Cloud is the managed platform.
Pricing
Free ("free forever", includes an MCP server for AI agents, analytics chat and a semantic model agent); Starter $40 per developer per month; Premium $80 per developer per month; Enterprise custom.

Source: Cube pricing

Entry price
Free; paid plans from $40 per developer per month
Company
cube.dev shows 21,018 stars on GitHub and 12,830 members on Slack.

Source: Cube homepage

Customers named
Named on Cube's site: Brex, Alcon, Drata and Webflow.

Source: Cube homepage

Best for
Teams whose main gap is consistent metrics for agents and BI, and who want published pricing and an open-source core
Cube context path: documents 4 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Cube is strong on business definitions, pricing transparency and deployment. As a semantic layer it does not document lineage, automated discovery or answer testing, which this ranking weighs heavily.

Highest in set on: Semantics and Pricing. Lowest in set on: Lineage, Governance, Answer checks and Integrations.

Key features

  • A semantic layer that serves BI for people and AI agents from one governed model.

    Source: Cube homepage

  • An MCP server for AI agents, included in the free plan.

    Source: Cube pricing

  • Analytics Chat for natural-language questions against the semantic model; the Analytics Chat API is agent-to-agent capable via MCP.

    Source: Cube homepage

  • Cube + Snowflake CoCo, announced 17 September 2026.

    Source: Cube homepage

Pros

  • Published, low entry pricing, with an MCP server on the free plan.
  • An open-source core you can run yourself.
  • Agents and dashboards answer from the same metric definitions.

Cons

  • No catalog and no cross-stack lineage.
  • Answer testing and automated discovery are not described.
  • Governance rules for agents are not detailed on the pages we read.

Bottom line

Cube is the pick when the main problem is consistent metrics for agents and the team is ready to build and maintain the semantic model. It does not map or govern the rest of the stack. It ranks 7 of 9 at 5.86.

Full Cube reviewCube alternatives

8.Select Star: automated docs and lineage, now part of Snowflake

Catalog and governance
5.77 / 10
Type
Metadata context platform (part of Snowflake)
Agent interface
MCP Server for Data, Ask AI
Lineage
Column level, across platforms
Deployment
SaaS; now part of Snowflake. Plans outside Snowflake are not stated.
Pricing
Not published The pricing URL redirects to the homepage.

Source: Select Star pricing URL

Entry price
Not published
Company
Select Star's site states it is now a part of Snowflake.

Source: Select Star homepage

Customers named
Customer stories on Select Star's homepage include Pitney Bowes, Handshake and Xometry.

Source: Select Star homepage

Best for
Snowflake-centric teams that want automated documentation and column-level lineage
Select Star context path: documents 3 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Select Star scores well on lineage and automation. It falls back on answer checks, pricing, deployment and integrations as documented, and its future as a standalone product is not stated after the Snowflake deal.

Highest in set on: none. Lowest in set on: Semantics, Answer checks, Integrations, Pricing and Deployment.

Key features

Pros

  • Column-level lineage across platforms with a short stated setup.
  • Generates semantic models for Snowflake Cortex Analyst.

Cons

  • Now part of Snowflake; its plans outside Snowflake are not stated.
  • No prices are published.
  • Answer testing and governance rules for agents are not described.

Bottom line

Select Star remains a quick route to documentation and column-level lineage, and its move into Snowflake makes it most relevant to Snowflake-centric teams. It ranks 8 of 9 at 5.77.

Full Select Star reviewSelect Star alternatives

9.Honeydew: a warehouse-native semantic layer with per-user prices

Semantic layer
4.98 / 10
Type
Semantic layer for AI and BI
Agent interface
AI analyst use case; an MCP server was not confirmed on the pages we read
Lineage
Not described
Deployment
Processes data inside your data warehouse; a self-hosted product is not described.
Pricing
Lite: $20 per user per month plus a platform fee from $500 per month. Standard: $30 per user per month, up to 50 users, plus a platform fee from $2,000 per month for 250, 750 or 1,500 active objects, with a 14-day free trial. Enterprise: custom.

Source: Honeydew pricing

Entry price
$20 per user per month plus a platform fee from $500 per month
Company
Not covered in our sources.
Customers named
No customers named in the pages we read.
Best for
Teams that want one semantic model shared by BI and an AI analyst, priced in the open
Honeydew context path: documents 3 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Why it earns its rank

Honeydew is strong on business definitions and pricing transparency. It scores low on agent access because MCP was not confirmed, and on lineage, automation and answer checks, which a semantic layer does not set out to cover.

Highest in set on: Pricing. Lowest in set on: Agent access, Lineage, Automation, Answer checks and Integrations.

Key features

  • One shared data model for BI and AI analysts.

    Source: Honeydew pricing

  • User identity propagation and native RBAC inside the warehouse.

    Source: Honeydew homepage

  • Git version control, with GitLab and BitBucket on Standard.

    Source: Honeydew pricing

  • Standard adds unlimited BI integrations, Power BI Service, aggregate-aware caching, Microsoft Entra and SSO.

    Source: Honeydew pricing

No vendor figures were collected for this platform.

Pros

  • Published per-user and platform prices, with a free trial on Standard.
  • Data stays in your warehouse and uses your own LLM provider.
  • Identity propagation keeps warehouse permissions in force.

Cons

  • An MCP server was not confirmed on the pages we read.
  • No catalog, lineage or answer testing is described.
  • Standard is capped at 50 users.

Bottom line

Honeydew suits teams that want one semantic model shared by BI and an AI analyst, priced in the open. For agent context beyond metrics, look at the context platforms. It ranks 9 of 9 at 4.98.

Full Honeydew reviewHoneydew alternatives

How do all nine compare side by side?

Highest in set Lowest in set

Criterion scores, 0 to 10, with weights
PlatformAgent access 15%Lineage 13%Automation 13%Governance 13%Semantics 12%Answer checks 8%Integrations 10%Pricing 8%Deployment 8%Total
DataHub10highest in set787779highest in set710highest in set8.02
Euno99highest in set9highest in set9highest in set79highest in set5lowest in set54lowest in set7.64
Atlan979highest in set8779highest in set2lowest in set4lowest in set7.25
Alation7768859highest in set34lowest in set6.60
Collibra9659highest in set7462lowest in set56.27
AtScale92lowest in set4lowest in set89highest in set55lowest in set455.87
Cube82lowest in set55lowest in set9highest in set2lowest in set5lowest in set9highest in set85.86
Select Star78866lowest in set2lowest in set5lowest in set2lowest in set4lowest in set5.77
Honeydew4lowest in set2lowest in set4lowest in set682lowest in set5lowest in set9highest in set64.98
Platform facts from vendor pages
PlatformGroupAgent interfaceLineageDeploymentEntry price
DataHubContext platformMCP, Agent Context Kit, Skills, APIs and SDKs, Ask DataHubLineage and impact analysis built from query logs, dbt projects and BI dashboardsOpen source from a single node to cloud-hosted, hybrid and decentralized setups, or DataHub Cloud as managed SaaS.$75,000 for 12 months (AWS Marketplace, up to 20 monthly active users)
EunoContext platformMCP server, Context API, Euno AssistantColumn level, from sources through BI dashboards to AI agentsCloud-native SaaS; reads metadata only. No self-hosted or open-source edition described.Not published
AtlanContext platformAtlan MCP server, SQL, APIs, SDKRelationship-level lineage in the data graph; column level not stated on the connectors pageSaaS; Atlan states it never queries the rows in your tables. A self-hosted option is not stated.Not published
AlationCatalog and governanceOntologies published as an MCP server (early access); Console natural-language routing (early access)Lineage from SQL queries, scripts and query logs, plus agent lineage tracingNot stated on the pages we read.Not published
CollibraCatalog and governanceCollibra MCP Server (25+ governance tools)Lineage carried in data products and explorable through the MCP serverNot stated on the pages we read; the MCP server is described as deploy anywhere.Not published
AtScaleSemantic layerAtScale MCP server (list_models, explore_columns, focus_columns, get_sml_skills, run_query, get_outbound_queries); AtScale Ava for chat usersNot described (a semantic layer, not a catalog)Queries compute inside your data platform; hosting options are not stated on the pages we read.Not published
CubeSemantic layerMCP server, APIs, Analytics Chat APINot described (a semantic layer, not a catalog)Cube Core is open source (cube.dev shows 21,018 GitHub stars); Cube Cloud is the managed platform.Free; paid plans from $40 per developer per month
Select StarCatalog and governanceMCP Server for Data, Ask AIColumn level, across platformsSaaS; now part of Snowflake. Plans outside Snowflake are not stated.Not published
HoneydewSemantic layerAI analyst use case; an MCP server was not confirmed on the pages we readNot describedProcesses data inside your data warehouse; a self-hosted product is not described.$20 per user per month plus a platform fee from $500 per month

Which head-to-head comparisons are available?

Looking to switch from one of these tools?

What about Snowflake Horizon and Databricks Unity Catalog?

If your data, definitions and agents stay inside one platform, start with what that platform already includes. A third-party catalog or context platform earns its place when lineage, definitions and policies span several warehouses, transformation tools and BI tools. Facts below come from Snowflake and Databricks pages as summarized in search results; confirm them in each vendor's documentation.

Snowflake Horizon

Choose it when: Most of your data, semantic definitions and agents (for example Cortex) live inside Snowflake.

Watch for: Coverage of other warehouses, transformation and BI tools depends on what Snowflake connects; check the current documentation for your stack.

Databricks Unity Catalog

Choose it when: Your data, metrics and agents (for example Genie) live inside Databricks.

Watch for: Cross-platform lineage and definitions outside Databricks are not stated on the pages we read.

Platform-native options explained

Which tools did we review but not score?

Stardog

Knowledge-graph semantic layer

Our reads did not cover lineage, metadata automation or BI coverage, and Stardog's MCP server appears only in a secondary source, so too many criteria would be guesses.

Stardog context path: documents 1 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

data.world (ServiceNow Data Catalog)

Data catalog (part of ServiceNow)

data.world is now sold as ServiceNow Data Catalog with the ServiceNow AI Platform. Its own site could not be opened, and agent delivery, lineage and time to value were not stated on the ServiceNow page we read.

data.world (ServiceNow Data Catalog) context path: documents 2 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Modus

Context warehouse

Modus is early-stage. Integrations, customers, lineage depth and pricing were not stated in the page text we read, so most criteria could not be scored from facts.

Modus context path: documents 2 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Jedify

Autonomous context graph

jedify.com could not be opened, so product pages, pricing and agent delivery were not read; MCP is not mentioned in the funding release we read.

Jedify context path: documents 3 of 5 stages.1Warehouse and lakehouse2Transformation (dbt, ETL)3BI and semantic logic4Governance and access rules5Delivery to agents (MCP or API)

Reviewed, not scored: the details

Frequently asked questions

What is the best data catalog for AI agents in 2026?

On our published weights DataHub ranks first at 8.02, followed by Euno at 7.64 and Atlan at 7.25. The best fit depends on your stack: the weight-your-own tool re-ranks the list for your priorities.

What is the difference between a data catalog, a semantic layer and an AI context layer?

A data catalog inventories and documents data assets for people. A semantic layer defines metrics and business logic once so BI tools and agents compute them the same way. An AI context layer assembles lineage, definitions, trust signals and access rules into context an agent can request at query time, usually over MCP.

Do I need a third-party catalog if I use Snowflake Horizon or Databricks Unity Catalog?

Not always. If your data, definitions and agents stay inside one platform, its native catalog may be enough. Third-party tools add value when lineage and definitions span several warehouses, transformation tools and BI tools.

Which of these tools publish prices?

Cube and Honeydew publish rates on their pricing pages. DataHub Cloud has an AWS Marketplace listing at $75,000 for 12 months with up to 20 monthly active users. Euno publishes tiers and limits but no dollar prices. Atlan, Alation, Collibra, Select Star and AtScale publish no prices.

What is MCP and why does it matter for data catalogs?

The Model Context Protocol is an open protocol that lets AI agents call external tools and data sources in a standard way. Catalogs and context platforms run an MCP server so agents such as Claude or Cursor can ask for definitions, lineage and trust signals before they write a query.