Snowflake vs Databricks: how to choose in 2026

Snowflake vs Databricks comes down to where your work starts. If most of your value is SQL, dashboards and finance reporting run by analysts, Snowflake is usually the simpler choice. If most of it is data engineering, sensor data and machine learning built by engineers, Databricks usually fits better.

That rule held for years. In 2026 it still holds, but less cleanly. Snowflake now runs Python, Postgres and AI agents. Databricks now runs serverless SQL warehouses, Postgres and business-user AI. The feature gap has closed. The operating model gap has not.

I've worked with both platforms in manufacturing and defence programmes. This guide is for the CIO, CFO or head of data who has to pick one, or justify keeping both. It covers origins, architecture, open formats, governance, pricing, AI and the skills you'll need, then gives decision rules by situation.

Origins still shape the products, the pricing and the people who sell them.

Snowflake started as a cloud data warehouse. Storage and compute are separate, the service is fully managed and SQL is the first language. You don't tune clusters. You size a warehouse, run queries and pay for the seconds it runs.

Databricks started from Apache Spark, built by the people who created Spark. Its centre of gravity is data engineering, streaming and machine learning on files in cloud object storage, using the Delta Lake table format. It called this the lakehouse, and SQL warehousing came later.

Centre of gravity

Snowflake

• SQL first, fully managed service
• Analysts and BI teams feel at home quickly
• Python via Snowpark and containers added later
• Fewer knobs, fewer ways to get it wrong

Databricks

• Spark, Python and notebooks first
• Data engineers and data scientists feel at home
• SQL warehouses and BI added later
• More control, more to operate

Both have spent three years moving into each other's space. Here is the 2026 position, checked against official release notes:

1. Postgres. Snowflake Postgres, built on technology from its Crunchy Data acquisition, became generally available on 24 February 2026 in selected AWS and Azure regions. Databricks Lakebase, its serverless Postgres, went GA on AWS in January 2026 and on Azure in March 2026.
2. Ingestion. Snowflake Openflow, built on Apache NiFi, is GA. It runs inside Snowflake on AWS, Azure and GCP, or in your own AWS account. Databricks has Lakeflow Connect, with pipelines built on Spark Declarative Pipelines.
3. Business-user AI. Snowflake Intelligence went GA in November 2025, alongside Cortex Agents and Cortex AI Functions, and was renamed Snowflake CoWork in June 2026. Databricks launched Genie One and Genie Agents (formerly Genie Spaces) as GA in June 2026. Genie App Builder went to private preview.
4. Agent building. Databricks Agent Bricks components reached GA in stages through 2026: Knowledge Assistant in January, Supervisor Agent in February, Document Intelligence and Custom Agents in April. Databricks now steers new low-code agents to Genie Agents and lists Knowledge Assistant and Supervisor Agent as legacy. Snowflake's equivalent is Cortex Agents.

This is where the difference that matters lives now.

Snowflake keeps data in its own managed storage by default. You can also use Apache Iceberg tables, stored in your own cloud storage. External engines such as Spark can read and write Snowflake-managed Iceberg tables through Horizon Catalog; writes became GA in May 2026. Snowflake says billing for that external-engine access starts in the second half of 2026, so ask about it.

Databricks keeps data in your cloud storage in Delta Lake by default. Unity Catalog governs it. Databricks announced managed Iceberg tables in Unity Catalog as GA in May 2026, though some Databricks docs pages still carried a public preview label when I checked. Confirm the status for your cloud and region before you design around it.

So the format war is mostly over. Both read and write Iceberg. The real questions are which catalog is the master and which engine writes to which table. If two catalogs both think they own a table, you will spend a quarter untangling it.

Governance follows the same pattern. Snowflake Horizon Catalog covers access policies, masking, lineage and audit inside Snowflake, and now reaches out to Iceberg tables. Unity Catalog does the same for Databricks across tables, files, models and AI functions. Databricks open-sourced Unity Catalog in 2024. My Unity Catalog governance guide goes deeper.

In defence work, governance is usually the deciding factor, not query speed. Classified and unclassified separation, export-controlled data and audit trails that survive a long programme matter more than benchmark charts. Both platforms can do fine-grained access control. What differs is how many places a policy has to be defined. Fewer catalogs means fewer gaps. The same logic applies once AI agents start querying that data, which is why I'd write the AI governance framework before the first agent goes live.

This table summarises the comparison as of October 2026. Prices are list prices, checked October 2026, for AWS US East.

Area	Snowflake	Databricks
Starting point	SQL data warehouse, fully managed	Spark, data engineering and ML on a lakehouse
Default storage	Snowflake-managed storage, or Iceberg in your storage	Your cloud storage, Delta Lake by default
Open formats	Iceberg read and write, external engines via Horizon Catalog (GA)	Delta Lake native, managed Iceberg in Unity Catalog (announced GA May 2026)
Governance	Horizon Catalog	Unity Catalog (open-sourced in 2024)
Compute unit	Credits, per-second billing, 60-second minimum	DBUs, rate depends on product and tier
List price	$2 (Standard), $3 (Enterprise), $4 (Business Critical) per credit	$0.70 per DBU for SQL Serverless, cloud compute included
Who pays the cloud bill	Snowflake, inside the credit (except Iceberg storage you own)	Your cloud account on classic compute; bundled on serverless
Storage list price	$23 per TB per month on demand	Your cloud provider's storage rates
Operations effort	Low. Few settings to tune	Medium to high on classic compute, lower on serverless
Business-user AI	Snowflake CoWork, formerly Snowflake Intelligence (GA)	Genie One and Genie Agents (GA)
Agent building	Cortex Agents (GA)	Agent Bricks (components GA through 2026)
Postgres	Snowflake Postgres (GA, selected regions)	Lakebase (GA on AWS and Azure)
Skills you need	SQL, dbt-style modelling, some Python	Python, Spark, SQL, MLOps, cloud engineering

The headline rates are not comparable. A credit and a DBU measure different things, and they sit on top of different cost structures.

On Snowflake's pricing page, on-demand credits list at $2.00 for Standard, $3.00 for Enterprise and $4.00 for Business Critical on AWS US East. Storage is $23 per TB per month on demand. The credit covers compute, so you get one bill from Snowflake. Prices change by region and cloud, and AI features use separate AI credits.

On Databricks' SQL pricing page, SQL Serverless lists at $0.70 per DBU on the Premium plan, AWS US East, with the cloud instance cost included. SQL Classic is $0.22 and SQL Pro $0.55 per DBU, but those exclude the virtual machines, which you pay to AWS, Azure or Google separately.

That split matters to a CFO. With classic Databricks compute, half the cost sits on your cloud invoice and half on the Databricks invoice. Nobody owns the total unless you make someone own it. Snowflake's single bill is easier to read, but it is also easier to grow without noticing.

Enterprise commitments and discounts move both numbers a lot. Never compare list prices and call it a business case. Run the same three or four real workloads on both, for at least a few weeks, and compare the invoices. My Databricks pricing guide and Snowflake credits guide break down the cost drivers.

Decision rules work better than feature checklists. This is how I'd call it in the four situations I see most.

Situation	My usual lean	Why
BI-heavy finance team, ERP reporting, few engineers	Snowflake	SQL first, low operations, analysts self-serve quickly
Manufacturing with heavy sensor, historian and quality data, ML on the roadmap	Databricks	Streaming, Python and ML are native; data stays in your storage
Regulated defence with residency and export-control needs	Either, decided by region and accreditation	Both offer US government regions; check exact services available there
Already deep in Microsoft Azure and Fabric	Azure Databricks first, Snowflake if SQL dominates	Azure Databricks is a first-party Microsoft service with Microsoft billing and support

A few notes behind that table.

1. Finance and ERP reporting. Most of the value is in modelled SAP, Oracle or Microsoft Dynamics 365 data, joined and reconciled. That is SQL work. Snowflake's lower operating effort usually wins. My guide to getting ERP data into Snowflake covers the extraction side.
2. Manufacturing. In the manufacturing programmes I've worked on, the volume comes from machines, not ERP. The data that decided the choice was sensor and machine data, and it pushed towards Databricks. High-frequency sensor data, MES events and quality images need engineering skills more than reporting skills. Databricks handles that natively.
3. Defence. Neither product runs on premises. Both are SaaS on public or government cloud. Snowflake lists US Gov and DoD regions on its pricing page. Databricks runs on AWS GovCloud with FedRAMP High, plus a separate DoD IL5 environment. Outside the US, check which sovereign regions exist and which features have reached them. Feature lists in government regions lag the commercial ones.
4. Microsoft shops. Snowflake works well on Azure too, and Microsoft announced OneLake and Snowflake interoperability as GA. My Databricks vs Microsoft Fabric comparison covers that choice.

SAP customers have one more input. SAP Business Data Cloud now has zero-copy connectors to both platforms. Snowflake lists its SAP BDC connector as GA from May 2026, and Databricks connects through SAP BDC Connect. That removes one argument for either side.

Many large companies end up with both. Engineering picked Databricks, finance picked Snowflake, and nobody wanted to fight about it. That can work. It usually costs more than it should.

Where I've seen both platforms used together, it was mostly to test the outputs. One platform checked the other's results. It was not a planned long-term split. If that is your reason for running both, put an end date on the parallel run.

The waste comes from three places:

1. Two copies of the same data. Raw ERP and plant data landed twice, stored twice and refreshed twice.
2. Two pipelines for the same source. Two teams extracting from the same SAP, Oracle or Dynamics 365 system, often with different business rules.
3. Two governance models. Access policies defined in Horizon and again in Unity Catalog, drifting apart.

The fix is a written split. Pick one platform to own ingestion and the curated layer. Store shared tables once, in Iceberg, with one catalog as master. Let the other platform read those tables rather than copy them. Assign a single owner for the combined bill.

I'd also set a review date. If after a year one platform is serving fewer than a handful of use cases, consolidate.

Don't start with a vendor demo. Start with a list of your top five data use cases for the next two years, who will build them and what skills you have today. That list usually answers the question before any proof of concept.

Then run a short, paid proof of concept on real data: one finance reporting case and one engineering or AI case. Compare cost, effort and how your own people found it. If you want an independent view on the decision or the commercial terms, get in touch.

Is Snowflake or Databricks better?

Neither is better in general. Snowflake is usually the simpler fit for SQL analytics and BI with a small engineering team. Databricks is usually the better fit for data engineering, streaming and machine learning. In 2026 both cover most of the other's ground, so the decision rests on your skills, workloads and how you want to pay.

Is Databricks cheaper than Snowflake?

It depends on the workload and on how you count. Snowflake's credit includes compute. On classic Databricks compute you pay DBUs to Databricks plus virtual machine costs to your cloud provider. Serverless Databricks bundles both. The only reliable comparison is to run the same real workloads on both and compare invoices after discounts.

Can Snowflake and Databricks share the same data?

Yes. Both read and write Apache Iceberg tables, and external engines can access Snowflake-managed Iceberg tables through Horizon Catalog. Decide which catalog owns each table, store it once and let the other platform read it. That avoids paying twice for storage and pipelines.

Does Snowflake support machine learning and Python?

Yes. Snowpark runs Python, Java and Scala inside Snowflake, and Cortex AI Functions and Cortex Agents are generally available. Teams with heavy custom ML, streaming or Spark workloads still tend to find Databricks more natural.

Can Snowflake or Databricks run on premises for defence?

No. Both are cloud services. For regulated defence work, Snowflake offers US government regions, including a DoD region, and Databricks runs on AWS GovCloud with FedRAMP High and a separate DoD IL5 environment. Check which features are available in your exact region, because government regions lag commercial ones.

Should I use Snowflake or Databricks with Microsoft Fabric?

Both work with Microsoft. Azure Databricks is a first-party Azure service with Microsoft billing and support. Snowflake has GA interoperability with OneLake. If your teams are mostly SQL and Power BI, look hard at whether Fabric alone covers the need before adding either.