Head to head
Cube vs Euno for AI agents
Short answer
On our default weights Euno scores 7.64 and Cube scores 5.86. Euno scores higher on Agent access, Lineage, Automation, Governance and Answer checks; Cube scores higher on Semantics, Pricing and Deployment. They tie on Integrations.
By The Lineage Desk · Best Data Catalogs · Published
Desk research from public vendor pages. Editorial assessment.
How do Cube and Euno compare on each criterion?
| Criterion | Weight | Cube | Euno | Higher score |
|---|---|---|---|---|
| Agent access to context | 15% | 8 | 9 | Euno |
| Cross-stack lineage | 13% | 2 | 9 | Euno |
| Automated context build | 13% | 5 | 9 | Euno |
| Governance agents can use | 13% | 5 | 9 | Euno |
| Business definitions and metrics | 12% | 9 | 7 | Cube |
| Answer quality checks | 8% | 2 | 9 | Euno |
| Integration breadth | 10% | 5 | 5 | Tie |
| Pricing transparency | 8% | 9 | 5 | Cube |
| Deployment options | 8% | 8 | 4 | Cube |
Why: Agent access to context
Cube: Governed data reaches agents through MCP and APIs; the free plan includes an MCP server.
Source: Cube pricing
Euno: Serves context to agents through an MCP server and a Context API (up to 500K or 1M calls by plan), scoped by persona; Claude and Cursor are named.
Source: Euno: Context for AI Agents
Why: Cross-stack lineage
Cube: A semantic layer and BI platform, not a catalog; cross-stack lineage is not described.
Source: Cube homepage
Euno: Column-level lineage from sources through BI dashboards to AI agents, including business logic inside BI workbooks.
Source: Euno: Context for AI Agents
Why: Automated context build
Cube: A semantic model agent is listed; automated discovery and documentation across the stack are not described.
Source: Cube pricing
Euno: Maps assets automatically from day one and precomputes the context graph from APIs and query logs; Euno states zero onboarding lift.
Source: Euno homepage
Why: Governance agents can use
Cube: Describes governed data for agents; certification and sensitivity rules are not detailed.
Source: Cube homepage
Euno: Governance rules become live labels (for example certified or PII-free) that propagate through the graph, with persona-based access to context.
Why: Business definitions and metrics
Cube: Built on a semantic layer that people and agents query alike.
Source: Cube homepage
Euno: Extracts definitions from BI workbooks and SQL and syncs dbt with Looker; it does not offer a semantic layer of its own.
Why: Answer quality checks
Cube: Checks on agent answers are not described on the pages read.
Source: Cube homepage
Euno: Context Calibration generates benchmark questions, checks agent answers and suggests missing rules and glossary terms.
Why: Integration breadth
Cube: Works on cloud data platforms and names agent clients; no connector count was read.
Source: Cube homepage
Euno: Plans cap integrations at 3, 5 or 10; the full source list could not be read.
Source: Euno pricing
Why: Pricing transparency
Cube: Published: Free, Starter $40 and Premium $80 per developer per month, Enterprise custom.
Source: Cube pricing
Euno: Tiers, resource limits and a feature table are published, priced by data assets with unlimited users and agents, but no dollar prices.
Source: Euno pricing
Why: Deployment options
Cube: Open-source core (21,018 GitHub stars shown on cube.dev) alongside managed Cube Cloud.
Source: Cube homepage
Euno: Cloud-native SaaS only, reading metadata only; no self-hosted or open-source option is described.
Source: Euno homepage
Which hops from warehouse to agent does each document?
Cube: documents 4 of 5 stages
Euno: documents 5 of 5 stages
How do pricing and deployment compare?
| Cube | Euno | |
|---|---|---|
| Type | Semantic layer (agentic analytics) | AI context platform |
| Agent interface | MCP server, APIs, Analytics Chat API | MCP server, Context API, Euno Assistant |
| Lineage | Not described (a semantic layer, not a catalog) | Column level, from sources through BI dashboards to AI agents |
| Deployment | Cube Core is open source (cube.dev shows 21,018 GitHub stars); Cube Cloud is the managed platform. | Cloud-native SaaS; reads metadata only. No self-hosted or open-source edition described. |
| 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 | 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 | Free; paid plans from $40 per developer per month | Not published |
Which should you choose?
Choose Cube if
- consistent metric definitions are the main gap
- you want published prices before talking to sales
- Teams whose main gap is consistent metrics for agents and BI, and who want published pricing and an open-source core
Choose Euno if
- you need many ways for agents to reach context
- you need lineage from the warehouse through BI to the agent
- Data teams that want agents grounded in lineage and governance across warehouse, dbt and BI without finishing a semantic layer first
Frequently asked questions
Which is better for AI agents, Cube or Euno?
On our default weights Euno scores 7.64 and Cube scores 5.86. The better fit depends on which criteria matter most to you; the weight-your-own page lets you change the weights.
Which costs less, Cube or Euno?
Cube: 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. Euno: 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. We do not compare total cost because most of these prices are not published.
Can Cube and Euno be used together?
They cover different layers (Semantic layer and Context platform), so they can sit side by side. The pages we read do not describe a direct integration between them.