Databricks vs Snowflake: which is better for AI over company data?

Summary

• Both Databricks and Snowflake let business users ask natural-language questions of governed company data and let developers build agents on it. The better fit depends on where your data, governance and AI development already live.
• On Databricks, Genie Agents answer business questions from Unity Catalog tables, views and metric views, and they always evaluate data access with each end user's own Unity Catalog permissions.
• On Snowflake, Cortex Analyst answers business questions over structured data using semantic views, and Cortex Agents plan, call tools such as Cortex Analyst and Cortex Search, and reflect on the results.
• Both define business semantics as governed objects: Unity Catalog metric views on Databricks and semantic views on Snowflake.
• Databricks can query Snowflake data in place through Lakehouse Federation, with a Unity Catalog foreign catalog governing access.

Databricks vs Snowflake: which is better for AI over company data?

Neither platform is the right answer in every case. Both give business users natural-language answers over governed data and give developers a managed way to build agents, so the deciding factor is where your data and governance already live. Databricks keeps AI on Unity Catalog-governed lakehouse data, with Genie Agents for business questions and Agent Bricks for custom agents. Snowflake keeps AI inside its governed environment with Cortex Analyst, Cortex Agents and semantic views. If your data is split across both, Databricks can query Snowflake tables without moving them.

What does AI over company data involve?

It means grounding answers in your own data instead of a model's training data. That takes four parts: a semantic layer that defines metrics and business terms, a natural-language interface that turns questions into governed queries, retrieval over documents and other unstructured content, and agents that combine those tools for multi-step questions. Governance has to hold at every step, so a user only gets answers built from data they're allowed to read.

How do Databricks and Snowflake compare for AI over company data?

Capability	Databricks	Snowflake
Natural-language analytics	Genie Agents, a chat interface where business users ask questions of their data	Cortex Analyst, a fully managed, LLM-powered feature for answering business questions over structured data
Semantic layer	Metric views, which separate measure definitions from dimensions and support multi-level joins	Semantic views, schema-level objects with logical tables, relationships, facts, dimensions and metrics
Shared business context	Genie Ontology (Public Preview), combining Unity Catalog semantics with snippets inferred from dashboards, queries and Genie Agents	Semantic views supply business definitions to Cortex Analyst and Cortex Agents
Agents	Agent Bricks, the agent developer platform, and Genie Agents for agents built without code	Cortex Agents, a fully managed agentic platform that plans, uses tools and reflects
Unstructured retrieval	Databricks AI Search, hybrid keyword and vector search with indexes governed by Unity Catalog	Cortex Search, hybrid vector and keyword search with semantic reranking
LLM functions in SQL	AI Functions such as ai_query, ai_extract and ai_classify	Cortex AI Functions such as AI_COMPLETE, AI_EXTRACT and AI_CLASSIFY
Access control	Unity Catalog; Genie Agents evaluate each end user's own permissions	DAC, RBAC and UBAC; Cortex features need roles such as SNOWFLAKE.CORTEX_USER

How does each platform answer a business question?

On Databricks, a business user asks a Genie Agent, which works from the tables, views or metric views configured for it. Data access is evaluated with that user's own Unity Catalog permissions, and each query is attributed to the user in query history. Publishing an AI/BI dashboard with Genie enabled generates a Genie Agent from the dashboard's datasets and visualizations.
On Snowflake, an application sends the question to Cortex Analyst through its REST API, and Cortex Analyst uses semantic views to answer it, including multi-turn follow-ups. Cortex Agents can combine Cortex Analyst with Cortex Search, and threads keep conversation context across turns. Data access is governed by Snowflake privileges and the execution context of each configured tool.

How does Databricks approach AI over company data?

Databricks builds its AI surfaces on one governance layer. Unity Catalog governs tables, views, volumes, functions, models and services. Metric views are the core of Unity Catalog semantics and can be queried from SQL editors, notebooks, dashboards, Genie Agents and alerts. Genie One and Genie Code search the same Genie Ontology, and its snippets are gated by Unity Catalog permissions.
For custom agents, Agent Bricks connects agents to Databricks-managed, custom and external MCP servers and tools, and Unity Gateway governs access to models, MCP servers and skills with guardrails, rate limits and usage tracking. Model Serving serves Databricks-hosted foundation models and models hosted outside Databricks behind REST APIs. AI Search indexes can sync automatically from Delta tables. And Lakehouse Federation mirrors a Snowflake database as a Unity Catalog foreign catalog, so Databricks users query it in place under Unity Catalog permissions.

FAQs

Can Snowflake run agents, or only SQL functions?

Snowflake runs agents. Cortex Agents is a fully managed agentic platform that plans, calls tools such as Cortex Analyst and Cortex Search, and reflects on the results, with threads for multi-turn conversations.

Do I need a semantic layer for AI over company data?

Both platforms build on one. Genie Agents can use metric views as data sources, and Cortex Analyst uses semantic views to answer questions over structured data.

Can Databricks use data that lives in Snowflake?

Yes. Lakehouse Federation uses a Unity Catalog connection and a foreign catalog that mirrors the Snowflake database, so you manage Databricks user access with Unity Catalog permissions.

Is Genie Ontology generally available?

No. Genie Ontology is in Public Preview.