Snowflake vs Databricks Comparison on Cost, BI and AI
A warehouse built for SQL against a lakehouse built for engineers, compared on the prices both vendors actually publish, on who each one suits, and on what it costs to let business users ask either one a question in plain English. Try the console while you read: it is running against a demo database.
PostgreSQL · MySQL · Snowflake · BigQuery · read-only
Ask the demo shop a question, or type your own:
Reading the schema, writing SQL… Writing SQL… Running (read‑only)… SQL AgentSQL wrote
▋
A real MySQL shop: customers, products, orders and signups over the last year. AgentSQL writes the SQL, runs it read-only, and answers.
Direct answer
Snowflake is a managed SQL data warehouse and Databricks is a Spark lakehouse over open files in your own cloud storage. Pick Snowflake when your work is mostly BI, dashboards and ad-hoc SQL and nobody on the team wants to manage compute. Pick Databricks when you run large pipelines, streaming or machine learning in Python. On price, a Snowflake credit is $2.00 on Standard, $3.00 on Enterprise and $4.00 on Business Critical with infrastructure included, and Databricks Serverless SQL is $0.70 per DBU hour, also all-in, read from both vendors on 27 September 2026. The newer difference is the AI question layer: Snowflake bills Cortex Analyst at 67 credits per 1,000 messages, while Databricks gives Genie's LLM usage away until 31 January 2027 and bills only the SQL compute behind it.
›_ list prices, read from the vendors on 27 September 2026
What Snowflake and Databricks actually charge, and what each unit covers.
US East, on demand, before any negotiated discount. The third column matters more than the second, because only some of these rates include the machines.
| Meter | List price | What it covers | Source |
|---|---|---|---|
| Snowflake Platform Credit | $2.00 Standard, $3.00 Enterprise, $4.00 Business Critical | Warehouse compute with the cloud infrastructure included. An X-Small warehouse burns 1 credit an hour, billed per second after a 60-second minimum. | Consumption Table PDF, effective 25 Sep 2026, AWS US East, on demand |
| Snowflake storage | $23.00 per TB per month | Compressed data, averaged over the month. The first capacity discount starts at $1.2M of annual contract value. | Consumption Table PDF, Table 3(a) |
| Snowflake AI Credit | $2.00 Global, $2.20 Regional | Cortex AI functions, Cortex Agents and CoWork token usage. A separate currency, and your negotiated Platform Credit discount does not apply to it. | Consumption Table PDF, Table 2(b) |
| Databricks Serverless SQL | $0.70 per DBU hour | SQL warehouse compute with the cloud instances included. The one Databricks SKU that compares cleanly with a Snowflake credit. | Azure retail price API, East US, Premium |
| Databricks SQL Pro / Classic | $0.55 / $0.22 per DBU hour | SQL warehouses running on virtual machines you pay your cloud provider for separately. The DBU is only part of the bill. | Azure retail price API, East US, Premium |
| Databricks Jobs / All-purpose | $0.30 / $0.55 per DBU hour | Pipelines and notebooks on classic compute, again plus the VM bill. Standard tier Jobs Compute is $0.15. | Azure retail price API, East US, Premium |
Where these numbers come from, so you can check them. Snowflake's marketing pricing page draws its table in JavaScript, so the authoritative list is its Credit Consumption Table, a legal PDF on snowflake.com. The version we read carries the line "Effective: September 25, 2026", two days before this page was updated. Databricks also injects its rates with JavaScript. For Azure Databricks the canonical price comes from Microsoft, which publishes it through a public retail price API; Databricks' own pricing page says Azure pricing "is set by Microsoft". The Genie figures were read off Databricks' Genie pricing page rendered in a browser.
The table deliberately stops short of a per-query cost. A credit and a DBU measure different amounts of work, and the only honest way to compare them is to run the same queries on both and read the bills. Our Databricks vs Snowflake pricing breakdown walks through that method, and the full rate cards live on Snowflake pricing and Databricks pricing.
›_ read from the vendors' own documents
Four things that change the Snowflake vs Databricks decision in 2026.
Each one is a line on a vendor rate card, and none of them shows up in the comparisons ranking above this page.
finding one
Snowflake now bills AI in a second currency
The consumption table effective 25 September 2026 prices AI Credits separately at $2.00, and states the Platform Credit discount in your contract does not apply to them. A committed-spend discount on warehouses says nothing about what AI features will cost.
finding two
Cortex Analyst is priced per message, but only through the API
67 Platform Credits per 1,000 messages is $0.134 a question on Standard and $0.201 on Enterprise. A footnote limits that rate to the Cortex Analyst API; the same questions asked through Cortex Agents or CoWork are billed per token instead.
finding three
Genie is free for the thinking, billed for the doing
Genie One and Genie Agents are free until 31 January 2027, then each user gets 150 DBUs a month ($10.50 in US East) before $0.070 per DBU. Databricks' own footnote: the free usage covers the LLM only, and the Serverless SQL that runs the query bills at $0.70.
finding four
Only one Databricks rate compares with a credit
A Snowflake credit includes the machines. A Databricks classic DBU does not, because the VMs are billed by AWS, Azure or Google on a separate invoice. Serverless SQL is the Databricks SKU that includes them, so it is the only fair line to set beside a credit.
Findings one and three pull in opposite directions, and that is the real story of this comparison in 2026. Snowflake has made its AI layer a separately metered line, outside whatever discount you negotiated on compute. Databricks has done the opposite for now, making Genie's language model free so that every question turns into Serverless SQL time, which is where it earns. Neither approach is wrong. They just mean the platform that looks cheaper for dashboards is not automatically the one that is cheaper once 200 people start typing questions into it.
›_ nine dimensions, both directions
Snowflake vs Databricks on what actually differs.
Including where each one is the weaker choice.
| Dimension | Snowflake | Databricks |
|---|---|---|
| What it is | A managed cloud data warehouse. Storage and compute are separate, and the interface is SQL. | A lakehouse on Apache Spark and open Delta Lake files in your own cloud storage. Notebooks first, SQL warehouses on top. |
| Who is productive on day one | SQL analysts and analytics engineers. Almost nothing to tune. | Data engineers and data scientists who work in Python, Scala and notebooks. |
| Where your data lives | In Snowflake's managed storage, billed by Snowflake at $23 per TB month. Iceberg tables are supported for open formats. | In your own S3, ADLS or GCS bucket as Delta or Iceberg, billed by your cloud provider. |
| BI and dashboards | The natural home. Every BI tool connects, and warehouse caching makes dashboard queries cheap. | Databricks SQL and AI/BI dashboards are solid now, but most BI traffic still means a SQL warehouse running. |
| Data engineering and ML | Snowpark runs Python, Java and Scala; Cortex covers LLM functions. Workable, not the center of gravity. | The home turf: Spark pipelines, streaming, MLflow, feature engineering and model serving. |
| Natural-language questions | Cortex Analyst, API only, $0.134 to $0.268 per message by edition, plus Snowflake Intelligence and CoWork on AI Credits. | Genie, a UI inside the workspace. LLM free until 31 Jan 2027, then 150 DBUs per user a month, then $0.070 per DBU, plus SQL compute. |
| Governance | Horizon catalog, role-based access, masking and row access policies. | Unity Catalog across tables, files, models and notebooks. |
| How you pay | Credits with infrastructure included. One invoice. | DBUs, plus a separate cloud invoice on classic compute. Serverless folds the machines in. |
| Price transparency | Full list prices in a public legal PDF. Compute discounts are negotiated and never printed. | Rates render in JavaScript on its site; Azure prices are public through Microsoft's retail API. |
The row buyers underrate is where the data lives. On Databricks your tables sit as Delta or Iceberg files in a bucket you own, so leaving Databricks means pointing a different engine at the same files. On Snowflake the default is managed storage, which is simpler and bills at $23 per TB a month on demand, and moving out means an export. Snowflake's Iceberg table support narrows that gap, but it is something you choose table by table rather than the default. If lock-in is a board-level worry at your company, that single row can outweigh everything else here.
The row buyers overrate is raw performance. Both vendors have published benchmarks that show themselves winning, and both have disputed the other's. For typical BI queries on a few terabytes, either platform returns in seconds when sized sensibly, and warehouse sizing and query design move the bill far more than the engine does. If Amazon Redshift is also on your shortlist, Snowflake vs Redshift covers that pairing, and BigQuery vs Snowflake covers Google's per-query model.
›_ the part of the bill nobody else prices
What it costs to let 10 business users ask 1,000 questions a month.
List prices only, US dollars, per month. The gaps in this table are the vendors' gaps, not ours.
In 2026 most platform decisions come with a second question attached: once the data is in, can the sales, finance and operations people get answers without filing a ticket? Both vendors now sell a natural-language layer for exactly that, and they price it in completely different ways. Here is the same modest team, ten managers asking about a hundred questions each a month, priced on each option.
| Option | Per month | How that number is built |
|---|---|---|
| Snowflake Cortex Analyst (API) | $134 to $201 | 1,000 messages x 67 credits per 1,000 = 67 credits, at $2.00 Standard or $3.00 Enterprise. Plus the warehouse credits to run each generated query, plus the app you build, because Analyst is an API with a semantic model you write in YAML. |
| Databricks Genie (until 31 Jan 2027) | $0 for the LLM | Promotional free period for Genie One and Genie Agents. The Serverless SQL that executes each answer still bills at $0.70 per DBU hour while the warehouse runs. |
| Databricks Genie (from 1 Feb 2027) | Not calculable from the rate card | 10 users x 150 free DBUs = 1,500 DBUs of LLM usage a month, worth $105 at $0.070. Databricks does not publish how many DBUs one question consumes, so whether 1,000 questions fit inside that allowance is unknowable in advance. Plus Serverless SQL compute. |
| AgentSQL Team on Snowflake | $59 | Flat, billed yearly, up to 10 seats and 5 connections, no per-question charge. Queries still run on your Snowflake warehouse, so its credits apply as they would for any BI tool. Does not connect to Databricks. |
Three things stand out. Cortex Analyst is the only one of the vendor options you can price exactly before you start, at $0.134 to $0.201 a question, but it is a building block: someone has to write the semantic model and the app around it. Genie is the easiest to turn on and costs nothing for the language model until the end of January 2027, but after that its per-user allowance is measured in DBUs and Databricks does not publish how many DBUs one question uses, so a budget is a guess until you have a month of usage. And on both platforms the query the AI writes still runs on warehouse compute you pay for, which no promotion covers.
If you are on Snowflake and want Genie's switch-it-on convenience without building on the Analyst API, a separate read-only tool is the third route. How the Snowflake-native options compare in practice is on Snowflake natural language query, and the Genie meters are broken down line by line on Databricks Genie pricing.
›_ pick by who does the work, not by the benchmark
Three situations, three different right answers.
Choose Snowflake if your team lives in SQL
Your analysts write SQL, your output is dashboards and board reports, and nobody's job title includes "platform". Snowflake is the lower-operations choice: an X-Small warehouse at 1 credit an hour suspends itself when idle, every BI tool connects, and the whole bill arrives on one invoice. At a 50 to 500 person US company without a data engineer, that simplicity is usually worth more than any feature on the other side.
Choose Databricks if pipelines and models are the job
You have data engineers running Spark jobs over large or messy data, you stream events, or you train and serve models. Databricks is built for that end of the work, keeps your data in open files you own, and at $0.30 per DBU hour for Premium Jobs Compute it can be very cheap per unit of engineering work when tuned. Budget for the separate cloud invoice and for someone to own Unity Catalog.
Choose both only with a reason
Plenty of larger companies run Databricks for engineering and ML and Snowflake for analytics, sharing Iceberg tables between them. It works, and it doubles your contracts, your governance and your cost reviews. If the only reason for the second platform is that business users cannot get answers out of the first, fix that problem directly instead.
›_ for teams already on snowflake
AgentSQL puts plain-English questions on Snowflake without a build project.
We build AgentSQL, so read this as interested rather than neutral. It connects to your Snowflake account with a read-only role, reads the live schema, turns a typed question into Snowflake SQL, runs it on the warehouse you choose and returns the number, a chart and the exact query so an analyst can check it. There is no semantic model to write before the first answer and no per-question charge: Team is $59 a month billed yearly for up to 10 seats and 5 connections, and Scale is $179 with role-based access and an audit log of every question. It never trains on your data. It also connects to BigQuery, PostgreSQL and MySQL.
What it does not do, stated plainly: AgentSQL does not connect to Databricks, so if your data lives there, Genie is the tool to evaluate. It is not a warehouse and does not replace Snowflake. It does not build pipelines or train models. What it removes is the queue of one-off questions that lands on the data team every week. How the SQL generation works is on text to SQL, and the permission model is on read-only security. Setup specifics for Snowflake are on the Snowflake connection page.
›_ frequently asked
Snowflake vs Databricks questions, answered.
What is the difference between Snowflake and Databricks?
Snowflake is a managed cloud data warehouse you work with in SQL, built so analysts are productive with almost no tuning. Databricks is a lakehouse built on Apache Spark, where data sits in open files in your own cloud storage and most work happens in notebooks. Snowflake leans toward BI and analytics, Databricks toward data engineering and machine learning.
Is Snowflake or Databricks better?
Neither is better in general. Snowflake is the better fit when most of your work is SQL analytics, dashboards and ad-hoc questions and you have no platform engineer. Databricks is the better fit when you run Spark-scale pipelines, streaming or model training in Python. Choose by what your team does all day, not by feature lists, which now overlap heavily.
Is Databricks cheaper than Snowflake?
Not reliably. A Snowflake credit costs $2.00 to $4.00 with the infrastructure included, and Databricks Serverless SQL costs $0.70 per DBU hour, also all-in, but a credit and a DBU are not the same amount of work. Databricks classic compute looks cheaper per DBU because the virtual machines are billed separately. Compare a real workload, not the unit price.
Why do companies use both Snowflake and Databricks?
Because the two teams inside one company want different things. Data engineering and ML teams run pipelines and models on Databricks, and analytics teams serve BI from Snowflake. Both now read Apache Iceberg tables, so sharing one copy of the data between them is more practical than it was, though it still means two contracts and two sets of governance.
Can Databricks replace Snowflake?
For many workloads, yes. Databricks SQL warehouses serve BI tools and Genie answers business questions, so a Databricks-first company can run analytics without Snowflake. The cost is operational: someone has to own workspaces, compute policies and Unity Catalog. Teams without a data engineer usually find Snowflake simpler to run for the same SQL work.
Is Snowflake Cortex Analyst better than Databricks Genie?
They are built for different buyers. Cortex Analyst is an API you build an app around, with a YAML semantic model, priced at 67 credits per 1,000 messages. Genie is a finished interface inside the Databricks workspace, with its LLM usage free until 31 January 2027. Genie is quicker to switch on; Analyst is easier to embed in your own product.
Does Snowflake charge for AI features separately?
Yes. Snowflake's consumption table effective 25 September 2026 prices AI Credits separately at $2.00 on demand, $2.20 for regional processing, and says the Platform Credit discount in your contract does not apply to them. Cortex Analyst called through its API is the exception: it is billed in Platform Credits, 67 per 1,000 messages.
Can business users query Snowflake or Databricks without SQL?
Yes, through a natural-language layer. On Databricks that is Genie. On Snowflake it is Cortex Analyst, Snowflake Intelligence, or a separate tool that connects read-only to the warehouse. AgentSQL is one: it connects to Snowflake, writes the SQL for a typed question, runs it and shows the SQL. It does not connect to Databricks.
›_ pricing, read from the vendor
›_ the other warehouse side-by-sides
›_ connect your database
Ask Snowflake a question in plain English.
Read-only on Snowflake, BigQuery, Postgres or MySQL. Every answer comes back with the SQL beside it.