Snowflake vs Databricks: Warehouse or Lakehouse? (2026)

Quick verdict

Short answer

Choose Snowflake if your main workload is SQL analytics and BI, you want a service where almost everything (storage layout, tuning, infrastructure) is managed for you, and you prefer one bill in one unit, the credit. Choose Databricks if data engineering, Spark, streaming or machine learning are central, your team works in notebooks and Python as much as in SQL, or you want the data to live as Delta or Iceberg tables in your own cloud storage. The overlap is now large: Snowflake has Iceberg tables, Snowpark and Cortex AI, and Databricks has serverless SQL warehouses and Unity Catalog, so many teams could run either. Price both on a realistic workload before committing.

How we know: This comparison is research-based: architecture, editions and tiers, billing units, open table format support, governance and AI features were checked against Snowflake and Databricks documentation, Snowflake's Service Consumption Table and both pricing pages in October 2026. We have not run either platform or any benchmark, so nothing here is a measured result.

Snowflake is a fully managed cloud data platform that runs on AWS, Microsoft Azure and Google Cloud. Storage and compute are separate: data sits in Snowflake-managed storage (or, for Iceberg tables, optionally in your own cloud storage), and queries run on virtual warehouses that you size, start and suspend. Snowflake sells four editions (Standard, Enterprise, Business Critical and Virtual Private Snowflake) and bills compute in credits. Over the past few years it has added Snowpark (Python, Java and Scala code that runs inside Snowflake), Iceberg tables, the Cortex AI features and, after acquiring Crunchy Data in 2025, Snowflake Postgres for operational workloads.

Databricks grew out of Apache Spark and describes its product as a lakehouse platform: data is stored as open table format files (Delta Lake, and now Apache Iceberg) in cloud object storage, and different engines (Spark, Databricks SQL, streaming, machine learning) work on the same tables. It runs on AWS, Azure (as Azure Databricks) and Google Cloud. Governance is handled by Unity Catalog, and compute is billed in Databricks Units (DBUs). Databricks acquired Tabular, the company founded by Apache Iceberg's original creators, in 2024, and Neon, a serverless Postgres company, in 2025; Neon now underpins its Lakebase Postgres offering.

Warehouse versus lakehouse is now a matter of emphasis. Snowflake still leads with managed SQL warehousing and Databricks with open-format data engineering and ML, but each has added the other's core features. For the concepts behind both, see Data warehouse vs database.

Side by side

Aspect	Snowflake	Databricks
Origin and emphasis	Managed cloud data warehouse, extended to data engineering, apps and AI	Managed Spark platform, extended into a lakehouse with SQL warehousing and AI
Clouds	AWS, Azure, Google Cloud	AWS, Azure (Azure Databricks), Google Cloud
Billing unit	Credits per second of warehouse uptime (60-second minimum); price per credit depends on edition, cloud and region	DBUs per second; rate depends on product and compute type, tier, cloud and region; classic compute VMs are billed separately by your cloud provider
Where compute runs	In Snowflake's service	Serverless: Databricks' serverless compute plane. Classic and pro: VMs in your own cloud account
Default storage	Snowflake-managed storage, billed per TB per month; Iceberg tables can use your own storage	Delta Lake (or Iceberg) tables in cloud object storage; managed tables governed by Unity Catalog
Open table formats	Apache Iceberg tables with Snowflake or an external catalog; can read Delta files via a catalog integration	Delta Lake natively; managed and foreign Iceberg tables and an Iceberg REST Catalog API in Unity Catalog
Languages	SQL; Python, Java and Scala through Snowpark	SQL, Python, Scala and R in notebooks and jobs; Apache Spark APIs
Governance	Role-based access; masking and row access policies from Enterprise edition	Unity Catalog: privileges, attribute-based policies, row and column filters, lineage, auditing
AI and ML (as documented)	Cortex AI Functions, Cortex Analyst, Cortex Search, Cortex Agents; Snowflake ML with feature store and model registry	MLflow, Feature Store, Model Serving, AI Gateway, serverless GPU compute, Foundation Model APIs
Free option	30-day trial	14-day trial; Free Edition for non-commercial use
Main trade-off	Least to manage, but compute is Snowflake's and native tables use its storage format	Open formats and broad engine choice, but more concepts, compute types and cost lines to understand

Key differences

Architecture: managed warehouse versus lakehouse

In Snowflake, you create virtual warehouses: named clusters of compute in sizes from X-Small upwards. Snowflake documents that each size up doubles the credits per hour (X-Small 1, Small 2, Medium 4, and so on for standard warehouses), that warehouses are billed per second with a 60-second minimum each time they start, and that a suspended warehouse uses no credits. Multi-cluster warehouses (Enterprise edition and above) add clusters automatically when queries queue, which is Snowflake's main answer to high concurrency. Data in native tables is stored and organised by Snowflake; you do not manage files.

-- Snowflake: a small warehouse that suspends after 60 seconds idle
CREATE WAREHOUSE reporting_wh
  WAREHOUSE_SIZE = 'XSMALL'
  AUTO_SUSPEND = 60
  AUTO_RESUME = TRUE;

In Databricks, tables are files in cloud object storage, described by Delta Lake or Iceberg metadata and registered in Unity Catalog. Many kinds of compute can work on them: all-purpose clusters for notebooks, job compute, Lakeflow pipelines and SQL warehouses for SQL and BI. Databricks documents three SQL warehouse types: serverless (runs in Databricks' serverless compute plane; Databricks recommends it for most ETL, BI and exploratory work), pro (runs in your cloud account; for when serverless is not available in your region or you need custom networking) and classic (runs in your cloud account, entry-level). That flexibility is useful, but it also means more choices about which compute type each workload should use.

Billing: credits versus DBUs plus cloud costs

Snowflake charges for three things: compute (credits consumed by warehouses, serverless features and, above a threshold, the cloud services layer), storage (a flat rate per TB per month) and data transfer out of a region or cloud. Snowflake documents that cloud services usage is charged only when it exceeds 10% of daily warehouse usage. The price of a credit depends on your edition, cloud and region, and newer warehouse types have their own rates: the Service Consumption Table lists Gen2 standard warehouses at 1.35 credits per hour for X-Small on AWS and Google Cloud and 1.25 on Azure, against 1 for the first generation. For Iceberg tables in your own storage, your cloud provider bills the storage, not Snowflake.

Databricks charges DBUs, which it defines as a normalised unit of processing power, billed per second. The DBU rate differs by product and compute type (for example jobs, all-purpose, SQL classic, SQL pro, SQL serverless), by tier and by cloud and region. For classic compute, the virtual machines run in your cloud account and your cloud provider bills them separately, alongside storage and networking. For serverless compute, Databricks allocates and manages the compute, so you are not provisioning instances in your own account. In our view, this split is the most common source of confusion when people compare the two: a Databricks DBU price is not the full cost of a classic workload, and a Snowflake credit price does not include storage.

Both vendors publish system views for cost monitoring (Snowflake's account usage views, Databricks' system.billing.usage table) and both sell committed-use discounts. See the pricing section below for a dated Snowflake example.

Open table formats: Iceberg and Delta on both sides

Snowflake Iceberg tables come in two kinds. With Snowflake as the catalog, Snowflake has full read and write support and manages the table lifecycle, and the data can sit in Snowflake-managed storage or in your own S3, Google Cloud Storage or Azure storage through an external volume. With an external catalog (AWS Glue, Snowflake Open Catalog, or Delta files in object storage via a catalog integration), Snowflake has more limited support and does not manage the table lifecycle. Catalog-linked databases can discover and sync tables from a remote Iceberg REST catalog. Snowflake documents that third-party clients cannot append, delete or upsert data in Iceberg tables that use Snowflake as the catalog.

-- Snowflake: Iceberg table managed by Snowflake, data in your storage
CREATE ICEBERG TABLE orders_ice (
  order_id INT,
  customer STRING,
  amount   NUMBER(10,2)
)
  CATALOG = 'SNOWFLAKE'
  EXTERNAL_VOLUME = 'my_ext_volume'
  BASE_LOCATION = 'orders_ice/';

Databricks uses Delta Lake as its native format and now documents, as generally available, managed Iceberg tables in Unity Catalog (Databricks Runtime 16.4 LTS or later with serverless enabled), read-only foreign Iceberg tables from AWS Glue, Hive metastore or Snowflake Horizon Catalog via Lakehouse Federation, an Iceberg REST Catalog API so engines such as Spark, Flink and Trino can access Unity Catalog tables, and Iceberg specification versions 1 to 3. The docs list limitations for managed Iceberg tables, for example no UUID or TIME types and no branching or tagging.

-- Databricks SQL: managed Iceberg table in Unity Catalog
CREATE TABLE main.sales.orders_ice (
  order_id INT,
  customer STRING,
  amount   DECIMAL(10,2)
) USING ICEBERG;

The practical result: if open formats matter to you, both can now keep data in Iceberg in your own storage, and each can read the other's catalog in some form. Check the current limitations on both sides for the exact read and write paths you need, because they differ by catalog.

Governance: Snowflake access policies versus Unity Catalog

Snowflake uses role-based access control on all editions. Column-level security (masking policies) and row access policies require Enterprise edition or higher, as do extended Time Travel (up to 90 days), materialized views and search optimisation. Business Critical adds Tri-Secret Secure (customer-managed keys), private connectivity and account failover and failback.

Databricks centralises governance in Unity Catalog, which it describes as operating beneath every data and AI interaction: access control on tables, views, volumes, functions and models in a catalog.schema.object namespace, attribute-based policies, row and column filters, lineage tracking and audit logging. Unity Catalog is enabled automatically for workspaces created after 8 November 2023, and an open source implementation is also available. On Azure, Unity Catalog requires the Premium tier; Microsoft Learn states that Standard tier workspaces on Azure Databricks reach end of life on 1 October 2026 and are upgraded to Premium automatically.

Programming model and AI features

Snowflake is SQL first. Snowpark adds DataFrame APIs and user-defined functions in Python, Java and Scala; Snowflake documents that the computation runs inside Snowflake, with no separate cluster. Snowpark-optimized warehouses offer larger memory configurations for heavier Python and ML work. Snowflake Cortex groups the AI features: Cortex AI Functions (LLM functions callable from SQL), Cortex Analyst, Cortex Search and Cortex Agents, while Snowflake ML provides a feature store, model registry and framework connectors. Snowflake states that the models run inside its security and governance perimeter. AI usage is billed in AI credits, priced separately from platform credits, and trial accounts must add a credit card before AI features are enabled.

Databricks is notebook and Spark first, with SQL as a peer. Its ML documentation lists MLflow for experiment tracking and model lifecycle, Feature Store, Model Serving (custom models and LLMs as REST endpoints with GPU support), AI Gateway for governance and usage tracking, AI Runtime for serverless GPU training and inference, Ray on Databricks and Foundation Model APIs. In our view, teams that already train and deploy their own models usually find more of their workflow inside Databricks, while teams whose main AI need is calling LLMs over governed warehouse data can do much of that in Snowflake. We have not compared the quality or cost of either vendor's AI features.

Pricing and licensing

Snowflake. Compute is billed in credits and storage per TB per month; data transfer between regions or clouds is charged per TB. You can buy On Demand (billed monthly in arrears) or Capacity (prepaid, with a negotiated discount). As one example, Snowflake's Service Consumption Table (effective 2 October 2026, USD) lists the On Demand price per credit in AWS US East (Northern Virginia) as USD 2.00 for Standard, USD 3.00 for Enterprise, USD 4.00 for Business Critical and USD 6.00 for VPS, and On Demand standard storage there at USD 23.00 per TB per month. So an Enterprise X-Small first-generation warehouse (1 credit per hour) running for one hour in that region would use USD 3.00 of compute. Prices are higher in many other regions. A 30-day free trial is available; the docs say it ends after 30 days or when the free usage balance runs out, and do not state the balance.

Databricks. Compute is billed in DBUs per second, with pay-as-you-go and committed-use contracts. DBU rates vary by product and compute type, tier, cloud and region, and the per-DBU rates on databricks.com load dynamically, so we could not read and quote one reliably; use Databricks' pricing calculator. For classic compute, add your cloud provider's charges for virtual machines, storage and networking; for serverless, the compute runs in Databricks' account. The pricing page lists a 14-day free trial (during which you are still billed by your cloud provider for resources in your account). Free Edition is serverless only, quota limited (for example, one 2X-Small SQL warehouse) and may not be used for commercial purposes. On Azure, Azure Databricks is billed through Microsoft, and new workspaces must be Premium tier.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Snowflake strengths

Very little to administer: no files, clusters or table maintenance to manage for native tables
One billing unit for compute, with per-second billing and automatic suspend and resume
Multi-cluster warehouses (Enterprise and above) for many concurrent BI users
Same product on AWS, Azure and Google Cloud, with a published per-region credit price list
Iceberg tables, Snowpark and Cortex extend it beyond SQL warehousing without leaving the platform

Databricks strengths

Data stays in open formats (Delta Lake or Iceberg) in cloud object storage
Spark, streaming, SQL and ML on the same tables, in notebooks or jobs
Unity Catalog governs tables, files, functions and models in one place, with lineage
Broad ML tooling: MLflow, Feature Store, Model Serving and serverless GPU compute
Choice of serverless or classic compute, including compute in your own network

Limitations

Snowflake limitations

Native tables are stored in Snowflake's own format; other engines need Iceberg tables or an export to use the data
Masking and row access policies, multi-cluster warehouses and longer Time Travel need Enterprise edition or above, at a higher credit price
Third-party engines cannot write to Iceberg tables that use Snowflake as the catalog
Credit consumption differs between warehouse generations and types, so cost estimates need the consumption table

Databricks limitations

More moving parts: several compute types, tiers and runtimes to choose and govern
For classic compute, cost is split between the Databricks bill and the cloud provider bill
Managed Iceberg tables have documented limitations (for example no UUID or TIME types, no branching or tagging)
Free Edition cannot be used commercially, and per-DBU prices are not shown as static values on the pricing pages

When to choose each

Choose Snowflake if

Your main workload is SQL analytics and BI dashboards for many users
You want a managed service with as little infrastructure and tuning as possible
Your team is mostly analysts and SQL developers rather than data engineers
You want one predictable billing unit and a published credit price per region
You need Business Critical features such as customer-managed keys and cross-region failover in one product

Choose Databricks if

Data engineering, streaming or large-scale Spark processing is a core workload
You train, track and serve your own machine learning models
You want data to live as open Delta or Iceberg tables in your own cloud storage
Your team works in Python notebooks as much as in SQL
You need compute inside your own cloud network for some workloads

When neither is right

You are all-in on Google Cloud and want a serverless warehouse billed per TB scanned or by slots: compare Snowflake vs BigQuery and Databricks vs BigQuery.
You are a Microsoft and Power BI shop and want one SaaS capacity for lakehouse, warehouse and BI: see Databricks vs Microsoft Fabric and Snowflake vs Azure Synapse.
Your data fits on one server and your queries are mostly reports: a well-indexed PostgreSQL or SQL Server database may be enough; see Data warehouse vs database and Snowflake vs PostgreSQL.
You run on AWS and want the native option: see Snowflake vs Redshift.

Final recommendation

Bottom line

Start from the work your team does most. If it is SQL analytics and BI, and you value a managed service with a single billing unit, Snowflake is the more direct fit, and its Iceberg tables now reduce the lock-in argument against it. If it is data engineering, streaming and machine learning on large volumes, or open formats in your own storage are a requirement, Databricks is the more direct fit, and its serverless SQL warehouses now make it a credible BI back end too. Because both platforms overlap heavily, the deciding factors are often skills, existing cloud contracts and cost on your own workload. Run the same representative pipeline and dashboard queries on trials of both, and compare the full bill, including cloud provider charges for Databricks classic compute, before you decide.

Frequently asked questions

Is Databricks a data warehouse?

Databricks describes itself as a lakehouse platform, but it includes Databricks SQL with serverless, pro and classic SQL warehouses for SQL and BI workloads. Data is stored as Delta Lake or Iceberg tables in cloud object storage and governed by Unity Catalog.

Can Snowflake use open table formats?

Yes. Snowflake supports Apache Iceberg tables, either with Snowflake as the catalog (full read and write, data in Snowflake-managed storage or your own) or with an external catalog such as AWS Glue or Snowflake Open Catalog. It can also read Delta files in object storage through a catalog integration. Third-party engines cannot write to Iceberg tables that use Snowflake as the catalog.

What is the difference between a Snowflake credit and a Databricks DBU?

Both are compute billing units. A Snowflake credit is consumed by virtual warehouses (for example 1 credit per hour for a first-generation X-Small) and serverless features, and its price depends on edition, cloud and region. A Databricks DBU is a normalised unit of processing power whose rate depends on the product and compute type, tier, cloud and region. For Databricks classic compute, the underlying virtual machines are billed separately by your cloud provider.

Which is cheaper, Snowflake or Databricks?

It depends on the workload, and neither vendor's list prices settle it. Snowflake publishes per-credit prices by region; Databricks per-DBU rates vary by compute type and must be combined with cloud costs for classic compute. The reliable way to compare is to run the same representative workload on both trials and compare total cost, including storage and data transfer.

Can Snowflake and Databricks share the same data?

To a degree, through Apache Iceberg. Databricks documents read-only foreign Iceberg tables from Snowflake Horizon Catalog via Lakehouse Federation and an Iceberg REST Catalog API for Unity Catalog tables, and Snowflake can use Iceberg tables from external catalogs. Exact read and write support depends on which catalog owns each table, so check both vendors' current limitations.

Does Databricks have a free tier?

Databricks offers Free Edition, a serverless-only, quota-limited environment for learning and experimentation that may not be used for commercial purposes, plus a 14-day free trial of the full platform. Snowflake offers a 30-day trial.

Sources

Checked October 2026.

How we research comparisons: our editorial method.