Snowflake vs Databricks: How to Choose a Data Platform

If your company is outgrowing spreadsheets and a single database, 2 names come up in almost every conversation: Snowflake and Databricks. Both are cloud data platforms, both run on the major clouds and both now market themselves as places to run analytics and AI. On the surface, they look like the same product.

They aren’t, at least not in where they started and what they’re easiest at. This guide compares Snowflake and Databricks by workload, required skills, pricing structure and lock-in, then gives you a checklist to choose. It’s a neutral comparison: the right answer depends on your team and your data.

The short answer

• Snowflake started as a cloud data warehouse. It’s SQL-first, highly managed and known for making analytics, BI and data sharing straightforward for analyst-heavy teams.
• Databricks started from Apache Spark and the data lakehouse model. Its own documentation calls it a “unified, open analytics platform” for data, analytics and AI. It’s code-first and strong for data engineering, streaming and machine learning.

Each has spent years moving into the other’s territory. Snowflake added Python through Snowpark, AI features through Cortex and support for open Apache Iceberg tables. Databricks added SQL warehouses, BI dashboards and natural-language data exploration. So today the question is less “which can do X?” and more “which does X most naturally for my team?”

Snowflake vs Databricks comparison table

Key takeaway: Snowflake is usually the easier path for SQL-heavy analytics teams. Databricks is usually the more natural fit for engineering-heavy teams doing large-scale processing and machine learning. Overlap is now large, so team skills often decide it.

Workloads: what each platform is built for

BI and SQL analytics

Snowflake’s core strength. Analysts write SQL, connect tools like Power BI or Tableau and get consistent performance with little tuning. Virtual warehouses can be sized up or down and paused when idle. If dashboards and reports are your main use, Snowflake is designed around that. See our Power BI vs Tableau comparison for the BI layer.

Databricks covers this with SQL warehouses, a query editor and AI/BI dashboards. Many companies run BI on Databricks successfully, especially when the data is already there for engineering or ML.

Data engineering and pipelines

Databricks’ home ground. Apache Spark, Auto Loader for ingestion, Lakeflow pipelines for ETL and jobs for orchestration are built for complex, high-volume data pipelines and streaming data.

Snowflake handles most ELT work in SQL, with serverless features like Snowpipe for continuous loading and Snowpark for Python-based transformation that runs inside Snowflake.

Machine learning and AI

Databricks has long been the default for data science teams: notebooks, MLflow for tracking experiments and models, and an ML-optimized runtime.

Snowflake has caught up quickly for business AI use cases. Cortex offers LLM-powered functions inside SQL, Cortex Analyst for natural-language questions over structured data, Cortex Search and Cortex Agents. For teams that want AI on governed data without building ML infrastructure, that’s appealing.

Data sharing

Both support sharing data with partners and customers. Snowflake built its reputation on secure data sharing and a marketplace. Databricks offers sharing through Unity Catalog and the open Delta Sharing protocol.

Skills your team needs

This is often the deciding factor.

If you’re hiring, think about which skills are easiest to find in your market. Our guides on how to become a data engineer and how to become a data analyst explain what each role typically knows.

Pricing structure compared

Both platforms are usage-based, and neither is “cheaper” in general. Your bill depends on workload, configuration and discipline. Here’s how each charges.

How Snowflake charges

According to Snowflake’s documentation, your total cost is the sum of 3 parts:

• Compute, billed in credits. Virtual warehouses are billed per second, with a 60-second minimum each time a warehouse starts. Serverless features such as Snowpipe also consume credits.
• Storage, a flat rate per terabyte per month, based on average daily storage. The rate depends on region and whether you’re on demand or have a capacity contract.
• Data transfer, charged when data moves out to another region or cloud. Loading data in is free.

The cloud services layer is only billed when its daily use exceeds 10% of daily warehouse use. The price per credit depends on your edition, cloud and region.

How Databricks charges

Databricks pricing is based on Databricks Units (DBUs), a normalized measure of processing power, metered per second on pay-as-you-go plans. DBU rates differ by product, such as jobs, SQL warehouses or serverless compute, and by cloud and region.

The big structural difference: with classic compute running in your own cloud account, your cloud provider separately bills the virtual machines, storage and networking. Serverless options bundle compute into the Databricks charge. Both vendors offer discounts for committed usage contracts.

Because the models differ, the only reliable comparison is a proof of concept: run the same representative workload on both and compare total cost, including cloud charges. Our guides to how cloud pricing works and FinOps cover cost controls that apply to either.

Openness and lock-in

Databricks stores data by default in open Delta Lake format in your own cloud storage, which many engines can read. Snowflake’s native tables use its own managed format, but it now supports Apache Iceberg tables that can live in your cloud storage. Databricks, in turn, offers Iceberg interoperability.

If avoiding lock-in is a priority, look at where your data physically lives, what format it’s in and which engines can read it. See how to avoid cloud vendor lock-in.

How to choose: a decision checklist

Lean toward Snowflake if:

• Most users are SQL analysts and BI consumers
• You want minimal infrastructure to manage
• Data sharing with partners or customers is central
• Your data is mostly structured and semi-structured

Lean toward Databricks if:

• You run heavy data engineering, streaming or large-scale processing
• Data science and custom ML are core to the business
• Your team is comfortable with Python, Spark and notebooks
• You want your primary data in open formats in your own cloud account

Consider both or either if you have a large, mixed team. Many enterprises use both, with Databricks for engineering and ML and Snowflake for BI, though that adds cost and complexity.

Where to start

1. List your top 5 workloads and who runs them.
2. Map your team’s skills: SQL-first or code-first?
3. Check existing commitments: your cloud provider, BI tool and any marketplace credits.
4. Run a 2 to 4 week proof of concept on both with real data and real queries.
5. Measure 4 things: query performance, total cost including cloud charges, ease of use for your actual users and governance fit.
6. Set cost guardrails from day one: auto-suspend, budgets and usage alerts.

If you’re still deciding between a warehouse and a lakehouse architecture at all, read data lake vs data warehouse first.

Conclusion

Snowflake and Databricks have converged, but their roots still shape what feels easiest. Snowflake suits SQL-first analytics teams that want a managed experience. Databricks suits engineering and data science teams working at scale on open formats. Pricing models differ enough that only a hands-on test with your workloads will show the real cost. Choose based on your people and your use cases, not on marketing.

For definitions of terms used here, see our glossary.

Sources

• Snowflake documentation: Understanding overall cost — credits, per-second billing, storage and data transfer charges
• Snowflake documentation: Snowpark — Python, Java and Scala support
• Snowflake documentation: Cortex AI features — Cortex AI functions, Analyst, Search and Agents
• Snowflake documentation: Apache Iceberg tables — open table format support and storage options
• Databricks documentation: What is Databricks? — platform description, Delta Lake, Unity Catalog, languages and features
• Databricks pricing — DBUs, pay-as-you-go, committed use and separate cloud costs