The honest answer to “Snowflake or Databricks” is that for a plain SQL analytics stack, either one works and you’ll be fine. The choice starts to matter when machine learning enters the pipeline, when your data climbs into the hundreds of terabytes, or when finance asks why the bill jumped 40 percent last quarter. That’s where the two stop looking alike and the architecture underneath decides what your days feel like.
Both started at opposite ends and have spent the last few years walking toward each other. Snowflake began as a SQL data warehouse and bolted on data science. Databricks began as a Spark engine for data science and bolted on a fast SQL layer. By 2026 they overlap enough that the marketing decks are nearly interchangeable, which is exactly why you should ignore the decks and look at how each one is built.
Two architectures wearing similar clothes
Snowflake is a managed warehouse. Your data sits in Snowflake’s own micro-partitioned columnar format, on storage you don’t touch directly, and you query it through virtual warehouses sized from XS up to 6XL. Each warehouse is an isolated compute cluster, so a brutal query from the marketing team can’t drag down the finance dashboard running next to it. You pick a size, it spins up in a second or two, and Snowflake handles the partitioning and clustering behind a wall you mostly can’t see. That opacity is the product. You give up knobs and you get a system that rarely needs a dedicated platform team to babysit it.
Databricks is a lakehouse. Your data stays in open Parquet files in your own cloud object storage, S3 or ADLS or GCS, with Delta Lake adding ACID transactions, schema evolution, and time travel on top. Compute runs on Spark clusters or serverless SQL warehouses, and the Photon engine does the vectorized query execution that makes its SQL competitive with a dedicated warehouse. Because the files are yours and the format is open, you can point other tools at the same data without copying it out. The price of that openness is more surface area to manage: cluster configs, runtime versions, and a billing model with more moving parts.
The line between them blurred badly in the last two years. Snowflake added native Apache Iceberg tables, Snowpark for Python, and Cortex for in-warehouse model inference. Databricks open-sourced Unity Catalog and handed it to the Linux Foundation in late 2025, added UniForm so Delta tables can be read as Iceberg, and shipped Lakebase, a serverless Postgres that came out of its Neon acquisition. Both can now read Iceberg, which quietly changes the most important part of the decision.
Lock-in is not what it was
The old argument against Snowflake was lock-in: your data lived in a proprietary format, getting it out was a project, and that gave the vendor pricing power. Iceberg softens that. When your tables are Iceberg sitting in your own bucket, the same data can be queried by Snowflake, by Databricks, and by an open engine like Trino without a migration. As of mid-2025 Snowflake can even read datasets registered in Databricks Unity Catalog through Iceberg, and the reverse works too.
This matters more than any benchmark. If you commit to Iceberg as your table format from day one, you keep the option to move compute later without moving data. You can run governed BI on Snowflake and heavy training jobs on Databricks against the same physical files. The lock-in question shifts from “which vendor owns my data” to “which engine do I want to pay for this particular job,” and that is a far healthier place to negotiate from.
Where the money actually goes
Both bill for compute by the second and storage by the terabyte, but the shape of the bill differs, and the shape is where teams get caught.
Snowflake sells credits. A credit runs somewhere in the low single digits of dollars depending on your edition and commitment, a warehouse burns credits at a rate that doubles with each size step, and there’s a 60-second minimum every time one wakes. Storage is a flat per-terabyte monthly charge in the low twenties. The math is simple, and the simplicity is the trap: because a warehouse is so easy to spin up, idle and oversized warehouses quietly pile up credits, and the convenience of letting Snowflake tune everything often shows up as a 20 to 40 percent premium over the same workload that someone actually right-sized.
Databricks bills in DBUs, but a DBU is only half the invoice. You also pay your cloud provider directly for the underlying instances, and that infrastructure line can add anywhere from half again to triple the DBU charge depending on what you pick. The upside is control over cost: Jobs Compute for scheduled pipelines is far cheaper than interactive clusters, spot instances cut the infra side hard, and for sustained ML training the mix of cheap object storage and Photon tends to beat Snowflake by a real margin. The downside is that forecasting the bill is genuinely harder, and a team with nobody watching cluster configs can light money on fire just as fast.
Treat any specific dollar figure you read, including the ones here, as a starting point that moves with edition, region, cloud, and how much you commit up front. Run a representative slice of your own workload on both for a week and read the actual invoices. Vendor calculators are optimistic by design.
The short version in a table
| Dimension | Snowflake | Databricks |
|---|---|---|
| Core model | Managed SQL warehouse | Lakehouse on open files |
| Storage | Proprietary, plus Iceberg | Your object storage, Delta and Iceberg |
| Compute | Virtual warehouses | Spark and Photon |
| Billing | Credits, one line | DBUs plus cloud infra |
| Strongest at | Governed BI, data sharing, fast onboarding | ML training, large ELT, data engineering |
| Operational load | Low, little tuning | Higher, more control |
| AI layer | Cortex, Snowpark | MLflow, model serving, notebooks |
Performance, with the asterisks that matter
Benchmarks published by each vendor show their own product winning, which is your cue to read them slowly rather than throw them out. The pattern that survives independent testing: Snowflake tends to edge ahead on warm-cache BI queries, the dashboard-refresh kind, often by a low double-digit percentage. Databricks with Photon tends to win on cold-start ELT and on large transformation jobs, sometimes by more than double, and the gap widens for sustained model training, where it can come out several times cheaper per unit of work.
None of that decides anything until you map it to what you run. A shop that’s ninety percent BI dashboards and ten percent light transforms should weight the warm-cache numbers heavily and ignore most of the training talk. A shop training models on terabytes every night should care about the training cost gap and barely feel the BI delta. A benchmark is a proxy. Your own job mix is the real test, and it’s the only one that bills you.
Picking by workload, not by hype
Reach for Snowflake when your center of gravity is SQL analytics that a lot of people touch. It onboards analysts fast and the governance story through its Horizon catalog is clean. Secure data sharing across teams and outside partners is genuinely one of the nicest things either platform does, and if your data team is small and you’d rather not staff a platform engineer to manage clusters, the lower operational load is worth real money.
Reach for Databricks when the heavy work is engineering and ML. If you’re running Spark pipelines, training or fine-tuning models, or pushing large-scale transformations, the open format and cheaper sustained compute fit the work better, and the path from notebook to production is shorter. The tradeoff is that you need someone who understands cluster sizing and the dual billing model, or the flexibility turns into waste.
The teams that struggle are usually the ones who chose on a benchmark or a sales relationship and then bent their workload to fit. The ones that do well pick the format first, commit to Iceberg so the call stays reversible, and put each workload on whichever engine is cheapest and fastest for that specific job. With both platforms able to read the same tables now, running both against one set of files is no longer the heresy it used to be, and for a lot of larger shops it’s quietly becoming the way things are done.
Useful next steps: