Snowflake vs Databricks for AI: Warehouse or Lakehouse?

Snowflake and Databricks began in different places and have converged on the same territory: a single platform for data, analytics, and AI. Snowflake grew from a SQL warehouse toward engineering and AI; Databricks grew from data science and engineering toward the warehouse. Their origins still shape their strengths, and enterprises should decide by workload mix rather than by feature announcements. This comparison covers it, drawing on FISTA Solutions' AI enablement practice. Related decisions are in how to choose a cloud platform for ai and data warehouse cost.

What is Snowflake?

Snowflake is a cloud data warehouse with separated storage and compute, a SQL-first experience, strong governance and data sharing, and an expanding set of capabilities for data engineering, applications, and AI, including functions for calling language models, building retrieval over governed data, and running Python. Its strengths are analytics performance and simplicity, governance, and sharing across organizations.

What is Databricks?

Databricks is a lakehouse platform built on open table formats with roots in data science and engineering: notebooks, distributed processing, streaming, a feature store, experiment tracking, a model registry, model serving, and LLM tooling for retrieval, fine-tuning, evaluation, and agents, unified under a catalog spanning data and AI assets. Its strengths are depth for ML and LLM engineering and openness of formats.

How do they compare?

Dimension	Snowflake	Databricks
Origin and center	SQL warehouse and analytics	Data science and engineering on the lakehouse
Analytics	Excellent, simple	Strong, with SQL warehousing
Data engineering	Growing; SQL and Python	Deep; batch and streaming
Custom ML training	Supported; less central	Deep; native tooling
MLOps: features, experiments, registry, serving	Available; evolving	Mature and integrated
LLM tooling: retrieval, fine-tuning, evaluation, agents	Growing set of functions and services	Extensive
Governance and catalog	Strong; sharing emphasis	Unified catalog across data and AI
Open formats and interoperability	Supported; historically proprietary	Native to open formats
Cost model	Warehouse compute by usage	Cluster and serverless compute by usage
Typical fit	Analytics-heavy organizations adding AI	ML-heavy organizations unifying data

Capabilities change quickly on both platforms; verify current documentation.

When should you choose Snowflake?

Choose Snowflake when the organization's center of gravity is governed analytics and SQL, when data sharing across partners and business units matters, when analytics teams will build AI features close to the data with SQL and functions, and when custom model training is a minority of the work. Adding LLM-powered analytics, grounded question answering over governed data, and SQL-accessible model calls fits well.

When should you choose Databricks?

Choose Databricks when data science and ML engineering are central: custom models, feature stores, experiment tracking, registries, serving, streaming pipelines, and LLM engineering including fine-tuning and evaluation. Organizations unifying data engineering and AI on open formats and wanting one catalog across data and AI assets fit here. MLOps context is in the mlops maturity checklist.

How should LLM application hosting be decided?

Both platforms can host retrieval, model calls, and AI features near governed data, which simplifies permissions and lineage. The question is how much of the AI application should live inside the data platform versus on an independent AI platform with its own gateway, evaluation, and observability. Applications serving product surfaces, integrating many systems, or requiring portability often keep the core independent and use the data platform for governed retrieval and features. Architecture is in the enterprise RAG reference architecture whitepaper and how to build an llm gateway.

How do governance considerations compare?

Both provide catalogs, access control, lineage, and auditing. Databricks emphasizes a unified catalog across tables, features, models, and AI assets on open formats; Snowflake emphasizes governed sharing and SQL-centric controls. For AI, governance must extend to embeddings, prompts, evaluation datasets, and model versions; assess how each platform's catalog handles them and how it integrates with your registry. Governance practice is in ai data governance.

How should cost be compared?

Model your workload mix: analytics query volume and concurrency, engineering and streaming compute, training and serving compute, storage, and AI function usage. The two platforms' consumption units and optimization levers differ, and headline comparisons mislead. Include the operational effort of running each. Cost context is in data warehouse cost and data engineering cost.

When do enterprises run both?

Often: Databricks for engineering, science, and ML; Snowflake for governed analytics and sharing; open formats and interoperability features connecting them. This captures each platform's strengths at the cost of two platforms to govern, secure, and pay for. It suits large organizations with distinct analytics and ML communities; smaller organizations usually benefit from choosing one.

What does the decision look like in practice?

A retailer whose data organization is analytics-centric, with strong SQL skills and partner data sharing, runs Snowflake and adds LLM-powered analytics and grounded assistants over governed data through platform functions, routing model calls through its gateway. A financial technology company with a large data science team training custom risk models and building LLM features runs Databricks for the ML lifecycle on open formats. A global enterprise runs both, connected by open formats, with a central AI platform team owning the gateway and evaluation independent of either.

How FISTA Solutions works with data platforms

FISTA Solutions builds AI systems on Snowflake, Databricks, and other data platforms according to the client's workload mix and estate, keeping the gateway, evaluation, and retrieval service platform-independent so AI applications remain portable while using each platform's governed data and native capabilities where they add value. The AI enablement practice delivers the integration, AI agents consume governed data through it, and forward deployed engineers make the platform decision with client data leaders. The record behind the approach is 150+ projects with 99.9% uptime.

To decide your data platform strategy for AI, message FISTA on WhatsApp, or read mlflow vs weights and biases for the experiment-tracking layer that sits on either.