Databricks vs Snowflake Pricing: A Real Cost Comparison for US Data Teams

Snowflake and Databricks both publish a clean per-unit price, and neither price can be compared with the other. Snowflake charges $2.00 to $4.00 per credit. Databricks charges $0.15 to $0.75 per DBU. The units measure different amounts of work, and more importantly, only one of them includes the machines the work runs on. Every "Databricks is 4x cheaper" comparison you will read starts by getting that wrong.

This is the comparison done properly, with both rate cards read from the vendors on 20 August 2026: Snowflake from its own pricing page for AWS US East (Northern Virginia), Databricks from its JavaScript-rendered pricing pages plus Microsoft's public retail price API for Azure Databricks. Nothing below is estimated.

The one rule that makes the numbers comparable

Snowflake runs the compute. When you buy a credit, the virtual warehouse, the servers underneath it and the management of both are inside that credit price. There is no second invoice.

Databricks splits it. Under its serverless SKUs the page reads "Includes underlying compute costs". Under every classic SKU it reads "Plus underlying compute costs billed by cloud provider". So a Databricks classic workload produces two bills: DBUs from Databricks, and EC2 or Azure VM hours from your cloud account, on a different invoice with a different billing cycle.

That gives you the rule. Compare Snowflake credits against Databricks serverless DBUs, never against classic DBUs. If you must use a classic rate, add your instance bill first.

Subtracting the Databricks rows gives you something the vendors do not print: the instance allowance built into each serverless price. SQL Serverless costs $0.48 per DBU hour more than SQL Classic, so serverless is the cheaper choice the moment your warehouse's EC2 bill passes 48 cents per DBU hour. For Jobs and all-purpose the allowance is only $0.20, which is a much harder bar for classic to fail. The full derivation, with every SKU, sits on our Databricks pricing page.

Tier upgrades: one is a multiplier, one is a puzzle

This is where the two pricing models genuinely diverge, and it decides more real budgets than the per-unit rate does.

Snowflake's ladder is linear and uniform. Standard to Enterprise is $2.00 to $3.00, exactly 1.5x. Enterprise to Business Critical is $3.00 to $4.00, exactly 1.333x. Standard to Business Critical is exactly 2x. Crucially, the multiplier applies to every credit you burn across the whole account, so you can forecast a tier change by multiplying last month's compute line and stopping there.

Databricks has no single multiplier. Read the Azure rate card and the Standard to Premium uplift is different on every SKU: Jobs Light goes $0.07 to $0.22 (3.14x), Jobs Compute $0.15 to $0.30 (exactly 2x), all-purpose compute $0.40 to $0.55 (1.375x), and SQL Analytics stays at $0.22 on both tiers, a 1.00x uplift. Anyone who budgets "Premium is about double" is wrong on three of those four lines.

Then there is a naming trap that catches cross-cloud spreadsheets. Databricks states it plainly: the Premium tier on Azure Databricks corresponds to the Enterprise tier on AWS and GCP. A table with an AWS Premium column next to an Azure Premium column is comparing two different products.

If you are regulated, the comparison is not close

Most US buyers in healthcare, insurance or financial services need customer-managed keys, private connectivity and a compliance posture their auditor will accept. Both vendors sell that. They charge for it in completely different shapes, and the gap is large.

Doubling every credit is a blunt instrument. A team that needs Tri-Secret Secure for one regulated schema pays twice as much for the ad hoc marketing queries in a completely different database, because the edition is an account-level setting rather than a workload-level one.

Databricks is cheaper here, but read its definition of the charge before you celebrate. Product Spend is spend "at list incurred in the specific workspaces where the add-on is enabled (turned on), before the application of any discounts, usage credits, add-on uplifts, or support fees". The add-on is billed on list, not on what you actually pay. A customer with a 30% negotiated discount spending $350,000 against a $500,000 list still owes 15% of $500,000, which is $75,000, or 21.4% of real spend. The better your commercial terms, the worse that ratio gets.

The Azure flat rate has its own quirk worth pricing out. Ten cents per DBU hour on top of Premium all-purpose at $0.55 is an 18.2% surcharge. The same ten cents on Premium SQL Analytics at $0.22 is 45.5%. A flat adder is regressive: the cheaper your workload, the harder it lands.

Storage is a bigger line than most comparisons admit

Snowflake stores your data and charges $23.00 per TB per month on demand, measured on the monthly average after compression. That last part is a genuine kindness, since Snowflake's micro-partitions compress hard and you are billed on the compressed figure rather than the raw one.

Databricks generally does not charge you for storage at all, because your data sits in an S3 or ADLS bucket you own. Your cloud provider bills that directly, at whatever rate and storage class you have negotiated, and you can move cold partitions to cheaper tiers without asking Databricks. Azure does expose a managed storage meter (the Databricks Storage Unit, $0.026 per DSU) for the products that need it, but the default lakehouse pattern leaves the bytes with you.

For a team with 5 TB of hot analytics data the difference is around a hundred dollars a month and nobody cares. For a team retaining 500 TB of event history it is the difference between a storage line you notice and one you do not. Weigh it against the fact that owning the bucket also means owning the lifecycle policies, the encryption configuration and the cleanup nobody remembers to do.

The AI layer is a separate purchase on both platforms

Databricks now sells Genie, its natural-language layer: Genie One, Genie Agents and Genie Code all list at $0.070 per DBU. Genie One and Genie Agents are promotionally free until 31 January 2027, and from 1 February 2027 every user gets 150 free DBUs a month, which Databricks values at $10.50 in US East.

Read the footnote before you budget from that, because it is the most consequential sentence on the page: "Free usage applies to Genie LLM usage only; Genie compute (e.g., SQL Serverless) is billed separately." The meter being given away is $0.070 per DBU. The meter that actually executes the query Genie writes is SQL Serverless at $0.70, ten times the price. Genie also requires SQL Pro or Serverless rather than Classic, so adopting it moves your warehouse off the $0.22 rate as well. One feature, three line items.

The general lesson applies to both vendors: price the AI SKU separately, on its own meter, and assume the compute underneath it is billed at full rate.

So which one should you buy?

Neither vendor is cheaper in the abstract, and any article that tells you otherwise has not read the footnotes. The decision splits cleanly along three lines.

• Choose Snowflake when predictability matters more than unit cost. Credits are all-in, tier changes are a clean multiplier, and finance can model the bill without knowing what a Photon emission rate is. That is worth real money in a company where the data team has to defend a forecast.
• Choose Databricks when you are regulated, storage-heavy, or already doing ML. A 15% compliance add-on beats a 100% edition uplift, keeping petabytes in your own bucket beats $23.00 per TB, and if your team is training models the lakehouse was built for that and Snowflake was not.
• Watch out for the shape of your usage, not the size of it. Databricks rewards disciplined, well-packed, auto-terminating compute and punishes idle clusters brutally, since the idle time lands on your cloud invoice rather than your Databricks one. Snowflake auto-suspends by default and forgives the same mistake more cheaply.

One practical warning before you model either. You cannot forecast DBU hours or credit burn from headcount, query count or data volume with any confidence, because consumption depends on instance type, tier, warehouse size and whether Photon is on. Expect the first two months on either platform to be measurement rather than budgeting. The useful first step is finding out which tables the expensive queries actually touch, which is a data lineage question before it is a pricing one, and it usually reveals that a handful of jobs are producing most of the bill.

The part neither platform prices

Both of these are places to put data and run compute against it. Neither changes the fact that when someone in ops asks a question on a Tuesday afternoon, the question turns into a ticket for whoever owns the warehouse, and the cost of that ticket appears on no rate card at all.

That is the gap Agentsql fills. It connects read-only to Postgres, MySQL, Snowflake or BigQuery, turns a plain-English question into SQL, runs it, and shows the SQL every time so an analyst can check the logic before anyone acts on the number. It is a flat monthly price with no credit meter and no DBU meter attached. To be clear about the boundary, we do not connect to Databricks, so if your data lives in the lakehouse, Genie is the right thing to price and our Databricks pricing breakdown is where to price it.

If you are still narrowing the platform list, the seat-priced side of the market is covered in Tableau pricing, Looker pricing and Power BI pricing, the usage-metered side in Snowflake pricing and BigQuery pricing, and all of them together in our BI tools comparison.