Semantic & metrics layers · Honeydew

Honeydew

Snowflake-native semantic layer that compiles governed metric and dimension definitions for BI tools and AI agents.

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Honeydew is a semantic layer built specifically for Snowflake, where it runs as a native application rather than a separate service pulling data out. Analysts and engineers model business concepts, metrics and relationships once through a modeling UI or code, and Honeydew compiles that layer into governed SQL for downstream consumers: BI tools like Tableau and Power BI, notebooks, and increasingly AI agents querying data through MCP or chat interfaces. The pitch is a single, deterministic source of truth that prevents both BI dashboards and text-to-SQL agents from silently reinventing metric logic. Because it depends on Snowflake's compute and Snowflake Ventures has invested in the company, it suits teams already standardized on Snowflake rather than warehouse-agnostic shops. It is a commercial, cloud-hosted product with no free, permanently-available tier beyond a trial.

At a glance

Vendor Honeydew
Pricing model Subscription
Free tier No
Deployment Cloud
Open source No
Best for Snowflake-centric teams wanting one semantic layer shared by BI dashboards and AI analyst agents.

Pricing

Per-user monthly pricing plus a platform fee that scales with the number of active semantic objects; a 14-day trial is available.

Plan Price Notes
Lite $20/user/month Up to 50 users; platform fee from $500/month for 100 active objects, for teams building AI analysts or shared BI models
Standard $30/user/month Up to 50 users; platform fee from $2,000/month for 250 active objects, for organizations establishing a shared source of truth
Enterprise Custom pricing Tailored to scope and requirements, for organizations needing scale or specialized functionality

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Semantic modeling layer native to Snowflake
  • No-code and code-based metric and dimension definitions
  • Governed SQL compilation for BI tools
  • MCP-compatible access for AI agents and chatbots
  • Lineage across metrics, models and consumers
  • Query consistency across BI and AI channels
  • Role-based access control on semantic objects

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

MetricFlow Open-source engine that compiles YAML metric definitions into SQL, and powers the dbt Semantic Layer. Cube Open-source headless semantic layer that defines metrics once and serves them via SQL, REST, and GraphQL APIs to any BI tool or app. AtScale Universal semantic layer that models metrics once on top of a cloud data warehouse and exposes them to any BI tool without data movement.

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Terms to know

Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Natural language query Asking a data question in plain language and getting an answer without writing code or a query language. Business intelligence (BI) The practice of turning company data into dashboards, reports and metrics that guide business decisions. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually. Text-to-SQL Technology that translates a plain-language question directly into a runnable SQL query against a database.

Related guides

How to choose a semantic layer Semantic layers differ by where they sit relative to the warehouse and who they serve — one BI tool, every BI tool, an app, or an AI agent.