Databricks vs Snowflake: Key Differences and Verdict

On this page

Quick verdict
Key differences
Specifications
What are Databricks and Snowflake?
How to evaluate
FAQ
Sources

Quick verdict

Choose Snowflake if you want a fully managed SQL data platform where Snowflake runs the infrastructure and you size virtual warehouses per workload; choose Databricks if engineering, streaming and machine learning teams need to share one lakehouse built on Apache Spark and open Delta Lake tables in your own cloud account. Both bill on consumption: Snowflake in credits with per-second warehouse billing, Databricks pay-as-you-go per second plus your cloud provider's charges. The deciding factors are whether SQL analytics or code-first data engineering and ML dominate, and how much control you want over storage formats.

Choose Databricks if

Shortlist Databricks when data engineers and data scientists share pipelines, streaming and ML work in Python or Scala on open Delta Lake tables stored in your own cloud account.

Choose Snowflake if

Shortlist Snowflake when analysts work mainly in SQL, you want the vendor to run all infrastructure, and you need per-workload warehouses or account-to-account data sharing without copying data.

Key differences

Key differences between Databricks vs Snowflake
Aspect	Databricks	Snowflake
Platform architecture	Databricks is a lakehouse that combines data lakes and data warehouses, built on Apache Spark with compute decoupled from storage.	Snowflake combines a central data repository with massively parallel compute clusters, a hybrid of shared-disk and shared-nothing designs, running on public cloud infrastructure.
Operating model	The Databricks platform integrates with cloud storage and security in your cloud account, and manages and deploys the cloud infrastructure for you.	Snowflake manages software updates and infrastructure; there is no hardware to install or manage, and it cannot run on-premises or on private cloud infrastructure.
Storage format	All Databricks tables are Delta Lake tables by default; Delta Lake is open source and extends Parquet files with a transaction log for ACID transactions.	Snowflake stores data in its own managed storage, and Apache Iceberg tables let it query data kept in external cloud storage that you manage.
Compute model	Databricks SQL runs on SQL warehouses over the data lake with ANSI SQL, while notebooks can run SQL alongside Python, Scala or R.	Snowflake virtual warehouses are required for queries and DML, come as Standard or Snowpark-optimized, and can be resized at any time, even while running.
Billing model	Databricks is pay-as-you-go with no up-front costs, billed per second on compute usage, while storage and networking costs depend on your cloud provider.	Snowflake cost is the sum of compute, storage and data transfer; compute consumes credits billed per second with a 60-second minimum, and the edition sets unit prices.
Data science and ML	Databricks adds MLflow and Databricks Runtime for Machine Learning for data scientists and ML engineers, plus real-time streaming data processing.	Snowflake offers Snowpark-optimized warehouses, which its documentation recommends for workloads with large memory requirements such as ML training.
Governance and sharing	Unity Catalog is the governance layer built into Databricks and is also available as an open source implementation; Delta Sharing provides an open way to share live data.	Snowflake Secure Data Sharing shares selected database objects with other Snowflake accounts without copying or transferring data between accounts.
Regulated workloads	Databricks runs within your own cloud account's storage and security, with Unity Catalog enforcing access control, tracking lineage and logging activity for audits.	Snowflake Business Critical Edition adds higher data protection for sensitive data, including PHI that must comply with HIPAA and HITRUST CSF regulations.

More specifications

From the iThub profiles. “Not recorded” means the profile does not confirm it. Confirm prices and editions with each vendor.

Databricks vs Snowflake specifications
Attribute	Databricks	Snowflake
Starting price	See vendor pricing ↗	See vendor pricing ↗
API	Yes	Yes
Founded	2013	2012
Headquarters	San Francisco, USA	Bozeman, Montana, USA
GitHub stars	Not recorded	Not recorded
Documentation	Official docs ↗	Official docs ↗

What are Databricks and Snowflake?

Databricks

Databricks describes itself as the data and AI company. Its Data + AI Platform is built on an open lakehouse architecture that provides a unified foundation for data and governance. A lakehouse combines the best elements of data lakes and data warehouses in one architecture, so structured analytics and raw or unstructured data do not need separate systems.

Databricks says the lakehouse is underpinned by the open source projects Apache Spark, Delta Lake and MLflow, and that Delta Sharing provides an open way to share live data. The platform supports data sharing, data engineering, data governance, data warehousing, artificial intelligence, data science, real-time streaming and a marketplace. A free trial is available.

Snowflake

Snowflake is a cloud data platform that it markets as the AI Data Cloud. Snowflake says thousands of organizations use it to unite siloed data, discover and securely share data, and run diverse workloads. It is sold as a fully managed service, so customers do not install or operate the software themselves.

Compute is provided by virtual warehouses. A warehouse is a cluster of compute resources, and it is required for queries and for all DML operations, including loading data into tables. Warehouses come as Standard or Snowpark-optimized, and the documentation recommends Snowpark-optimized warehouses for workloads with large memory requirements such as ML training. Multi-cluster warehouses scale compute to match changing user and query concurrency.

How to evaluate Databricks and Snowflake

Snowflake and Databricks both run analytics in the cloud on consumption billing, but start from different designs. Snowflake is a fully managed SQL data platform with its own storage and virtual warehouses that it operates for you. Databricks is a lakehouse built on Apache Spark and open Delta Lake tables, deployed into your cloud account and aimed at data engineering, streaming and machine learning as well as SQL.

Before choosing, list the workloads you will run in the first year, split by SQL reporting, pipelines, streaming and model training. Decide who owns cost monitoring, which storage format your data must stay in, and which compliance requirements apply, then ask each vendor to estimate cost for the same workload mix.

Criteria to verify

Share of SQL versus code-first workloads
Open table format and storage ownership
Cost per workload under realistic concurrency
Governance, lineage and access control depth
Compliance editions and regional availability

Proof-of-concept plan

Load the same month of production data into both platforms. Build one ingestion pipeline, ten representative reporting queries and one model training job, run them under the concurrency you expect, then share a dataset with a second account or workspace. Compare runtime, total billed cost, setup effort and how clearly each platform attributes cost per workload.

Frequently asked questions

What is the main difference between Snowflake and Databricks?

Snowflake is a fully managed cloud data platform centred on SQL, where Snowflake runs the infrastructure and virtual warehouses provide compute. Databricks is a lakehouse built on Apache Spark and open Delta Lake tables that runs in your cloud account and spans data engineering, streaming, SQL warehousing and machine learning with MLflow.

How are Snowflake and Databricks priced?

Both use consumption pricing. Snowflake compute consumes credits billed per second with a 60-second minimum each time a warehouse starts, storage and data transfer are billed separately, and the edition sets the credit price. Databricks is pay-as-you-go with no up-front costs and per-second billing on compute, while your cloud provider charges for storage and networking.

Can Snowflake or Databricks be self-hosted?

Neither is self-hosted software. Snowflake states that you cannot install and run it locally or on private cloud infrastructure, whether on-premises or hosted. Databricks integrates with storage and security in your own cloud account and deploys cloud infrastructure there for you, but the platform itself is still a managed service.

Does Snowflake support open table formats like Databricks?

Yes, through Apache Iceberg tables. Snowflake Iceberg tables combine Snowflake query semantics with external cloud storage that you manage. Databricks uses Delta Lake for all tables by default; Delta Lake is open source, stores data in Parquet files, and its transaction log follows an open protocol that any system can read.

Which is better for machine learning, Snowflake or Databricks?

Databricks is built around data science work: it includes MLflow and Databricks Runtime for Machine Learning, and notebooks run Python, Scala, R and SQL. Snowflake offers Snowpark-optimized warehouses, which its documentation recommends for memory-heavy workloads such as ML training. Run your own training workload on both before deciding.

How do Snowflake and Databricks share data?

Snowflake Secure Data Sharing lets an account share selected database objects with other Snowflake accounts, and no data is copied or transferred between accounts. Databricks uses Delta Sharing, described as an open way to share live data, and governs shared assets through Unity Catalog, which is also available as an open source implementation.

Sources

Facts on this page come from these official pages, checked on November 6, 2026, and from the iThub profiles. Vendors change plans and features: confirm with them before buying.