Databricks or Snowflake? A Mid-Size Guide

Databricks and Snowflake are both cloud data platforms that charge for what you use, and in 2026 they overlap more than ever. Snowflake began as a SQL data warehouse; Databricks began as a platform for data engineering and data science that it calls a lakehouse. For a mid-size company, the deciding question is what your team will do with the data every day, not which platform has the louder launch.

What is the difference between Databricks and Snowflake?

The short version is where each one started. Snowflake was built for people who write SQL against structured data: finance, operations and sales reporting. Databricks was built on Apache Spark for engineers and data scientists working in Python, often with large or messy data. Each has spent years moving toward the other, so the gap is now one of emphasis rather than capability.

	Databricks	Snowflake
How it describes itself	A unified platform for data engineering, analytics and AI, built on the lakehouse idea: a data lake's flexibility with a warehouse's reliability	The "AI Data Cloud": one platform for data engineering, analytics, applications and AI
Home ground	Python, Spark, streaming, machine learning, mixed and unstructured data	SQL analytics on structured data, data sharing
How you pay for compute	DBUs (Databricks Units), billed per second	Credits, billed per second with a 60-second minimum each time a warehouse starts
Governance catalog	Unity Catalog	Horizon Catalog, built on Apache Polaris
Open table formats	Delta Lake, plus managed Apache Iceberg tables	Its own tables, plus Apache Iceberg tables

That summary follows each vendor's own platform and documentation pages, read in October 2026.

How do Databricks and Snowflake pricing work?

Both bill by consumption, so the monthly number depends on how much compute runs and for how long. The mechanics differ in ways that matter for a budget.

Databricks measures processing in DBUs, billed per second, with discounts for committed use. On its classic compute, you also pay your cloud provider for the underlying servers. On serverless compute, that infrastructure cost is folded into the Databricks bill, and idle capacity isn't charged. Storage is measured separately.
Snowflake bills compute in credits. A virtual warehouse bills per second with a 60-second minimum each time it starts, resumes or is resized, and a suspended warehouse uses no credits. Storage is a flat rate per terabyte per month, and moving data out of Snowflake is charged.

In practice, the bill on either platform is driven less by the list price than by habits: compute left running, oversized warehouses or clusters, and jobs that reprocess everything every night. Ask whoever will run it how idle compute gets shut off before you sign anything.

Have Databricks and Snowflake converged?

Largely, yes. A few examples from the last two years:

Databricks runs serverless SQL warehouses for analysts, and recommends them wherever they're available.
Snowflake offers Snowpark, so Python, Java and Scala code can run inside Snowflake, plus Cortex AI and Snowflake ML for models.
Both now support Apache Iceberg. Databricks agreed in June 2024 to buy Tabular, the company founded by Iceberg's original creators, and its Unity Catalog now manages Iceberg tables that other engines can read and write.
Both added Postgres databases for applications. Databricks Lakebase became generally available on AWS in February 2026, and Snowflake Postgres, built on its 2025 purchase of Crunchy Data, became generally available the same month.

Neither is a risky bet on a small vendor. Databricks reported a revenue run-rate above $7 billion in August 2026, and Snowflake reported 828 customers each spending more than $1 million a year as of July 2026.

Does it matter which one connects to Salesforce?

Less than it used to. Salesforce Data 360 (formerly Data Cloud) Zero Copy works with Snowflake, Databricks, Amazon Redshift and Google BigQuery, so Salesforce data can be queried without being copied. Databricks also has a managed Lakeflow Connect connector that ingests Salesforce data incrementally.

The real choice is which way the data needs to flow and how fresh it must be. Our free Salesforce to Databricks route finder walks through that in six questions.

Which should a mid-size company choose?

Microsoft's decision guide for its own Fabric platform puts the logic well, and it carries over: pick a warehouse when the team is SQL-first and the data is structured; pick a lakehouse when the team works in Spark or Python, when the data is a mix of structured and unstructured, or when you're not sure yet. Applied to these two, that becomes five questions:

Who uses it every day? Analysts writing SQL lean Snowflake. Engineers and data scientists writing Python lean Databricks.
What does the data look like? ERP, CRM and finance tables lean Snowflake. Sensor data, documents, logs and images lean Databricks.
Does anything need to happen in real time? Streaming and event processing are Databricks' home ground.
What's already in place? If you run on Azure, Azure Databricks is billed through Microsoft, which can simplify buying. Existing skills on the team count for more than feature lists.
How much do you care about leaving later? Keeping data in an open format such as Iceberg makes a future move cheaper on either platform.

Plenty of mid-size companies need neither yet. If the numbers disagree because they come from several systems, the first job is connecting and cleaning those systems, and that work comes before any platform decision. Our guide to what data engineering is covers that step.

Where does MSquare fit?

MSquare Technology, our data engineering partner, lists both Databricks and Snowflake in its technology stack, so the recommendation can follow your data rather than a single vendor relationship. MSquare funds the discovery and a proof of concept, so you see the platform working on your own data before you commit. See our data engineering page for how that work runs.

Databricks or Snowflake: how should a mid-size company choose?