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Databricks vs Snowflake
The two platforms have converged on each other's ground. Choose on where your workloads actually sit and which team will own the platform.
Side by side
| Databricks | Snowflake | |||||||
|---|---|---|---|---|---|---|---|---|
| Vendor | Databricks, Inc. | Snowflake Inc. | ||||||
| Pricing model | Usage-based | Usage-based | ||||||
| Free tier | Yes | No | ||||||
| Deployment | Cloud | Cloud | ||||||
| Open source | No | No | ||||||
| Best for | Organizations wanting a single managed platform spanning data engineering, SQL analytics and machine learning on Spark. | Teams wanting a fully managed, multi-cloud SQL warehouse with strong concurrency isolation and native data sharing. | ||||||
| Pricing | Pay-as-you-go pricing metered in Databricks Units (DBUs) per second, varying by workload type and tier, plus separate underlying cloud infrastructure costs; committed-use contracts offer discounts. A limited free Community Edition exists. Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted. | Billed in per-second Snowflake credits whose price depends on edition (Standard, Enterprise, Business Critical, VPS), plus separate storage charges; a free trial is offered but there is no ongoing free tier.
Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget. | ||||||
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Verdict
These started at opposite ends and grew towards each other. Databricks began as managed Spark for data engineering and machine learning, then added SQL warehousing and governance. Snowflake began as a SQL data warehouse that separated storage from compute, then added Python, streaming and ML. Ten years on, either can serve most of what a data team needs, and a shortlist that treats them as opposites is working from an out-of-date picture.
What still differs is the centre of gravity. Databricks assumes engineers who write code, work in notebooks, and care about the open data lakehouse formats it helped create. Snowflake assumes analysts who write SQL and a platform team who would rather not tune clusters. That shapes who can be productive on day one without retraining.
Both bill by consumption rather than per seat, which makes a like-for-like quote hard. Neither one is reliably cheaper: the cost follows how disciplined you are about idle compute, and both punish carelessness.
Choose Databricks if
- Machine learning and data engineering are first-class work, not something bolted on beside reporting.
- You want table data in open formats you could read with another engine.
- Your team is comfortable in notebooks and Python or Scala as well as SQL.
- You process large volumes of semi-structured or streaming data.
Choose Snowflake if
- SQL is the shared language and most users are analysts.
- You want the smallest possible operational burden — no cluster sizing to reason about.
- Data sharing with partners or across business units is a real requirement.
- You would rather buy a managed product than assemble a platform.
Before you shortlist either
Run the same three workloads on both, with your data and your concurrency: a heavy transformation, a dashboard refresh at peak, and whatever your worst query is. Watch what each costs over a fortnight rather than what the calculator predicts. Then ask who will own the platform, because that answer decides more than the feature comparison does. Whichever you pick, the semantic layer and data modeling work above it is the same job — see how to choose a BI tool for the layer that sits on top.
Last reviewed September 21, 2026