SemanticFed vs Cube
The leading independent semantic layer. See how SemanticFed's governed data federation platform compares on features, pricing, and the use cases that matter to your team.
What Cube does well
Cube is the leading independent semantic layer, with a strong code-as-model experience, 200+ connectors, and AI-native analytics. It is not a federated query engine, however — queries run inside the customer's warehouse.
Company
Cube
Founded
2019
Headquarters
San Francisco, California, USA
Website
https://cube.dev
Ideal for
- Startups and mid-size companies building customer-facing analytics
- Data teams that need consistent metric definitions across BI tools
- Teams that already have a cloud warehouse and want a semantic layer on top
- SaaS companies embedding governed analytics into their products
Feature comparison
Side-by-side across the capabilities that matter for governed data federation.
| Capability | SemanticFed | Cube |
|---|---|---|
| Federated query (in-place, push-down) | Core shipped: cost-aware planner with predicate/projection/aggregation push-down across Postgres + REST sources; broader connector coverage rolling out | No:Relies on customer warehouse; not a federation engine |
| Typed semantic layer | No:Shipped: models, entities, dimensions, metrics + relationships CRUD with DRAFT→PUBLISHED versioning, now consumed live by the query engine to resolve entity/dimension/metric references | Best-in-class code-as-model + Visual Modeler |
| Source breadth | Roadmap: operational DBs, warehouses, lakes, SaaS APIs as first-class | Warehouse-centric; 200+ connectors |
| Governance at the edge | Partial: policy model + CRUD shipped; lineage and query-time enforcement pending | Dynamic RLS; limited masking/lineage |
| AI agent surface | No:Shipped (initial): native MCP server exposes the governed model — models, metrics, policies, saved queries — as agent tools; live query results now flow through the shipped engine for Postgres + REST sources | Proprietary Analytics Chat / AI API |
| Pricing transparency | Yes:Yes: Free → $99 → $399 → $999 → Enterprise | Per-seat + consumption credits |
Pricing comparison
How SemanticFed's flat-fee model stacks up against Cube.
SemanticFed
- Model
- Flat monthly fee per tier
- Free tier
- 3 sources, 1 model, forever free
- Paid tiers
- Starter $99 → Growth $399 → Business $999 → Enterprise
Cube
- Model
- Per-developer seats + consumption credits
- Entry point
- Cube Core free (MIT OSS, self-hosted)
- First paid tier
- ~$40/developer/month + credits
- Notes
- Typical mid-market Cube Cloud spend $40K–$100K/yr.
Cube strengths
- Best-in-class independent semantic layer (YAML/JS + Visual Modeler)
- SQL, REST, GraphQL, MDX, and AI APIs from one model
- AI-native analytics: Analytics Chat, AI Data Analyst, Cube D3
- MIT OSS core (Cube Core) reduces lock-in fear
- Strong embedded analytics for SaaS products
Cube weaknesses
- Not a federated query engine; execution relies on the warehouse
- Operational DBs and SaaS APIs require separate pipelines
- No open MCP server; AI API is proprietary
- Governance less mature than enterprise stacks
- No regional/INR pricing; median Cloud spend $40K–$100K/yr
Why SemanticFed wins
The advantages that matter when you need one governed way to reach every source.
True federation, not just a warehouse layer
Query operational DBs, SaaS APIs, warehouses, and lakes in place. Cube runs queries inside your warehouse.
No ETL required
Connect sources directly; no pipeline needed before modeling or querying.
Flat monthly pricing
Predictable $99–$999 tiers vs. per-developer seats plus consumption credits.
Cross-source governance at the edge
Row/column policy and masking enforced across every source, not delegated to the warehouse.
Compare SemanticFed with others
See how we stack up against the rest of the federation and semantic-layer landscape.
See why teams choose SemanticFed over Cube
SemanticFed launches in 2026. Join the waitlist to be the first to query across every source without moving the data.