Databricks vs Redshift: Cost, Performance, and How Databricks SQL Compares to Snowflake

Databricks vs Redshift cost: which one is actually cheaper?

For plain SQL analytics, Redshift is cheaper per active hour, and it is not close. Redshift Serverless bills $0.375 per RPU-hour with a 4-RPU floor, roughly $1.50 an hour while it is processing. A Small Databricks SQL Serverless warehouse draws 12 DBUs an hour at $0.70 a DBU, roughly $8.40 an hour while it is awake. Those are not identical amounts of compute, so treat the gap as the shape of the bill rather than a benchmark, but the direction holds across real deployments.

Run the monthly math and it gets concrete. Eight active hours a day across 22 working days is 176 hours. That is about $264 a month of Redshift Serverless compute, plus $24.58 for a terabyte of managed storage at $0.024 per GB, so roughly $289. The same 176 hours on a Small Databricks SQL Serverless warehouse is about $1,478 of compute, with S3 storage billed separately by AWS at standard rates. Both platforms auto-suspend when idle, so the real variable is how many hours you leave them awake, and that is where most overspending happens.

The trap on the Databricks side is the second bill. On classic compute, and that includes SQL Classic at the attractive-looking $0.22 per DBU, Databricks charges you for DBUs and AWS charges you separately for the EC2 instances underneath. One 2026 pricing guide puts infrastructure at 50 to 70 percent of total Databricks spend; another warns that teams underestimate the total by 50 to 200 percent when they budget DBUs alone. Serverless bundles the infrastructure into the DBU rate, which costs more per hour but is the number you can actually forecast.

The trap on the Redshift side is provisioned clusters that never sleep. An ra3.4xlarge at $3.26 an hour is about $2,380 a month if you leave it running, whether or not anyone queries it. Reservations cut that by up to 24 percent for a year or 45 percent for three, but only if your usage is genuinely steady. For spiky ad-hoc analytics, Serverless is usually the cheaper answer.

Databricks vs Redshift performance: which is faster?

Both vendors publish benchmarks showing themselves winning, and neither will tell you much about your workload. What is stable enough to plan around is the shape of their strengths. Redshift is tuned for repeated, well-modeled SQL over data it has ingested and organized, and when the sort and distribution keys match the query pattern it is very fast at exactly that. Databricks with the Photon engine is strong on large scans of open files, on messy or semi-structured data, and on anything that mixes SQL with Python in the same job.

Concurrency behaves differently too, which matters more than raw scan speed for a BI workload. Redshift Serverless scales capacity automatically, and provisioned clusters get one free hour of concurrency scaling a day per cluster before overage applies. Databricks SQL warehouses scale by adding clusters, and each added cluster multiplies the DBU burn, so concurrency on Databricks is a cost decision as much as a configuration one.

The honest advice is to stop reading benchmark posts and run your own. Both platforms have free trials or pay-as-you-go entry, so load a representative slice of your real data, run the ten queries your dashboards actually issue, and compare wall-clock time against the dollars each one burned. That afternoon of work will tell you more than every vendor benchmark combined.

Databricks vs Redshift vs Snowflake: where does the third one land?

Most buyers comparing these two are really running a three-way evaluation, because Snowflake sits between them. Snowflake is a fully managed warehouse like Redshift, but multi-cloud like Databricks, and it separates storage from compute so different teams can query the same data on their own virtual warehouses without fighting for capacity. It bills by credit, roughly $2 on Standard, $3 on Enterprise and $4 on Business Critical, with storage around $23 per terabyte per month on AWS US East.

A rough sorting rule: Redshift wins on price and integration if you are an AWS shop doing SQL. Databricks wins if one team owns engineering, ML and analytics together and wants open storage formats. Snowflake wins on ease of operation and workload isolation, and it is often the fastest to get running because there is very little to tune.

The comparison none of the three will frame for you is that the platform is rarely the reason answers are slow. Data teams switch warehouses expecting self-service to improve, and it usually does not, because the queue was in front of the analyst rather than inside the engine.

Is Redshift being replaced by Databricks?

No, and the framing is misleading. Redshift remains a heavily used AWS service with active development, including Serverless and the zero-ETL integrations that shipped precisely to keep SQL workloads on AWS. What has genuinely happened is that the lakehouse pattern took share of new greenfield platform builds, especially where machine learning was part of the plan from day one.

What we see in practice is coexistence rather than replacement. Companies keep Redshift for the modeled, governed SQL layer their dashboards depend on, and add Databricks where engineering and ML workloads live. That is two bills, which nobody enjoys, but ripping out a working warehouse is expensive for reasons unrelated to the software: rewritten SQL, re-tested pipelines, re-certified numbers, and the month-end report somebody quietly depends on.

If you are choosing for a new build rather than migrating, the decision is cleaner. Ask what your team will be doing in eighteen months. If the answer is SQL and dashboards on AWS, Redshift is the cheaper and simpler bet. If it involves pipelines, streaming and models built by the same people, Databricks earns its higher hourly rate by removing the integration work between four separate services.

Where a third option fits

We build Agentsql, so read this section as interested rather than neutral. It is here because the Databricks vs Redshift decision often gets asked to solve a problem that neither answer touches.

Both platforms are engines. They make queries run, and they both do it well enough that engine choice is rarely what stands between a sales manager and a number they need. What stands there is SQL. Someone has to write it, and that someone has a backlog. Making the query 400 milliseconds faster does nothing about the two days it waited in a queue.

Agentsql connects read-only to Postgres, MySQL, Snowflake or BigQuery, turns a plain-English question into SQL, runs it, and returns a chart, a table and a one-line answer. The generated SQL is shown every time so an analyst can check the work before anyone acts on it. It starts at $49 a month.

To be clear about the fit: we do not connect to Databricks or Redshift today, so if you land on either of those platforms we are not your querying layer. We support Snowflake and BigQuery, and if your evaluation is trending toward Snowflake it is worth knowing that the plain-English layer exists and does not require building a semantic model first.