Snowflake vs Google BigQuery vs Databricks
Snowflake, BigQuery and Databricks compared on pricing models, Iceberg support, SQL vs Python workflows, ML tooling and lock-in, with figures current to October 2026.
Quick Answer
Pick BigQuery if you live on Google Cloud and want zero infrastructure to manage; pick Snowflake if SQL analysts are your main users and you want the smoothest warehouse experience on any cloud. Pick Databricks if data engineering and ML in Python/Spark dominate your workload and you want open Delta/Iceberg storage at the core.
Reviewed by TechLogHub Engineering Team. Last updated October 15, 2026. Reflects BigQuery's current $6.25/TiB on-demand and edition slot prices, Databricks Free Edition replacing Community Edition, and GA managed Iceberg support on all three platforms as of 2026.
| Feature | Snowflake | Google BigQuery | Databricks |
|---|---|---|---|
| Pricing Model | Credits per warehouse-second + storage per TB | $6.25/TiB on-demand or slot-hour editions | DBUs (+ cloud VM costs on classic compute) |
| Free Option | 30-day trial with credits | 1 TiB queries + 10 GiB storage per month | Free Edition (perpetual, limited) + trial |
| Compute Model | Virtual warehouses (XS to 6XL, multi-cluster) | Serverless slots (on-demand or reserved) | Spark clusters, serverless jobs and SQL warehouses |
| Native Storage Format | Proprietary micro-partitions or Iceberg | Managed columnar (Capacitor) or Iceberg | Delta Lake (open) or Iceberg |
| Apache Iceberg Support | Managed and external Iceberg tables, Iceberg v3 | Managed Iceberg tables via BigLake/Lakehouse | Managed Iceberg tables in Unity Catalog |
| SQL Experience | Excellent, ANSI-friendly | Very good, GoogleSQL dialect | Good, Spark SQL dialect |
| Python & Notebooks | Snowpark + Snowflake Notebooks | BigQuery Studio notebooks, BigFrames | Native and first-class |
| ML / AI Tooling | Cortex AI functions, Snowpark ML | BigQuery ML, Vertex AI, Gemini | MLflow, Mosaic AI, model serving |
| Cloud Availability | AWS, Azure, Google Cloud | Google Cloud (Omni for limited AWS/Azure) | AWS, Azure, Google Cloud |
| Governance Catalog | Horizon Catalog (Apache Polaris) | Dataplex | Unity Catalog (open-sourced core) |
Snowflake
A fully managed cloud data warehouse that separates storage from compute and bills compute in credits consumed by virtual warehouses.
Pros
- Very low operational overhead: warehouses auto-suspend and resume, no cluster tuning
- Strong ANSI SQL support makes it the easiest of the three for analyst-heavy teams
- Runs on AWS, Azure and Google Cloud with cross-cloud data sharing
- Managed Iceberg tables and the Polaris-based Horizon catalog reduce format lock-in
- Per-second billing (60-second minimum) and clean workload isolation per warehouse
- Mature data sharing and marketplace for distributing datasets to partners
Cons
- Credit-based billing is easy to overspend with oversized or idle-prone warehouses
- Python/ML workflows via Snowpark are improving but less natural than Spark-native tools
- Serverless features (Snowpipe, search optimization, tasks) each carry their own credit rates
- Native tables use a proprietary format unless you opt into Iceberg
- No permanent free tier, only a time-limited trial
Best For
SQL-first analytics teams that want a low-maintenance warehouse on whichever cloud they already use.
Google BigQuery
Google Cloud's serverless data warehouse, billed either per TiB scanned (on-demand) or per slot-hour through Standard, Enterprise and Enterprise Plus editions.
Pros
- Truly serverless: no warehouses or clusters to size for on-demand queries
- Permanent free tier of 1 TiB of queries and 10 GiB of storage per month
- On-demand pricing ($6.25 per TiB scanned in US regions) is ideal for spiky, low-volume workloads
- Long-term storage pricing drops automatically for tables untouched for 90 days
- Tight integration with Vertex AI, Gemini, Looker, Pub/Sub and Dataflow
- BigQuery ML lets analysts train and run models in plain SQL
Cons
- Effectively tied to Google Cloud; Omni for other clouds is limited
- On-demand bytes-scanned billing punishes unpartitioned tables and SELECT * habits
- Slot-based editions require capacity planning to get predictable costs
- GoogleSQL dialect quirks make porting SQL from other warehouses non-trivial
- Fewer knobs for workload isolation than Snowflake's per-team warehouses
Best For
Teams already on Google Cloud that want analytics with zero infrastructure and a genuinely free entry tier.
Databricks
A lakehouse platform built on Apache Spark and Delta Lake that combines data engineering, SQL warehousing and ML on open table formats, billed in DBUs.
Pros
- Best-in-class for Spark, Python notebooks, streaming and large-scale ETL
- Data stays in open Delta Lake or Iceberg formats in your own object storage
- End-to-end ML stack: MLflow, feature store, model serving, Mosaic AI
- Unity Catalog governs tables, files, models and federated catalogs in one place
- Free Edition gives perpetual no-cost access for learning and prototypes
- Runs on AWS, Azure and Google Cloud
Cons
- Steepest learning curve: clusters, policies, DBU tiers and workspaces to understand
- Classic compute bills DBUs plus your own cloud VM costs, making spend harder to forecast
- Serverless options are simpler but carry higher DBU rates
- Pure SQL/BI users may find Databricks SQL less polished than Snowflake
- Platform sprawl means many overlapping features to evaluate
Best For
Engineering-heavy data teams running Spark pipelines, streaming and ML who want open formats in their own storage.
Pricing at scale: credits vs bytes scanned vs DBUs
The three bill compute in fundamentally different units. Snowflake charges credits for every second a virtual warehouse runs (60-second minimum per resume), with the per-credit rate rising by edition and region; an X-Small burns one credit per hour and each size doubles that. BigQuery on-demand charges $6.25 per TiB scanned in US regions after a free 1 TiB per month, while its Standard, Enterprise and Enterprise Plus editions charge per slot-hour ($0.04, $0.06 and $0.10 pay-as-you-go in us-central1). Databricks charges DBUs per workload type, and on classic compute you also pay your cloud provider for the VMs. In practice, BigQuery on-demand is cheapest for sporadic, well-partitioned queries and most dangerous for analysts who scan whole tables. Snowflake costs are driven by warehouse size and idle time, so auto-suspend settings and right-sizing matter more than query count. Databricks rewards teams that tune clusters and use job compute rather than all-purpose clusters for scheduled work. At sustained high volume, all three push you toward commitments: Snowflake capacity contracts, BigQuery slot commitments, and Databricks committed-use discounts. Model your actual query patterns before signing, because the cheapest platform for one workload shape can be the most expensive for another.
Open formats and lock-in: Iceberg changes the calculus
Historically Snowflake and BigQuery stored data in proprietary formats, while Databricks kept it in open Delta Lake files in your own bucket. That distinction has narrowed sharply. Snowflake now offers managed Iceberg tables and a Horizon catalog built on Apache Polaris that external engines can read and write through the Iceberg REST API. BigQuery offers managed Iceberg tables through its lakehouse layer with a REST-compatible runtime catalog. Databricks, after acquiring Tabular in 2024, supports managed Iceberg tables in Unity Catalog alongside Delta. The practical upshot: if lock-in is a concern, put your large, long-lived tables in Iceberg and treat the engine as replaceable. You will still be tied to each vendor's governance layer, SQL dialect, stored procedures and orchestration features, which are where most migration pain really lives. Databricks still has the most natural open-format story because Delta was its native format from the start, and it never forced data into a vendor-owned store. Snowflake and BigQuery native tables remain faster and simpler to operate inside their own engines, so many teams run a hybrid: native tables for hot BI marts, Iceberg for shared and archival data.
SQL analysts vs Python and ML engineers
Snowflake is the most comfortable home for analysts. Its SQL is forgiving, warehouses isolate teams cleanly, and BI tools connect without fuss. Snowpark and Snowflake Notebooks bring Python into the platform, and Cortex exposes LLM functions from SQL, but Python-first engineers often still feel like guests. Databricks is the reverse. Notebooks, Spark, PySpark, structured streaming and MLflow are first-class, and Unity Catalog extends governance to models and files. Databricks SQL warehouses are capable and serverless, yet BI-only users typically find Snowflake or BigQuery more approachable. BigQuery sits between them. Analysts get serverless SQL and BigQuery ML for training models without leaving SQL. Data scientists get BigQuery Studio notebooks, the BigFrames pandas-style API, and a short hop to Vertex AI and Gemini. If your organisation is already standardised on Google Cloud, that integration often outweighs feature-by-feature comparisons. A useful heuristic: count who writes the majority of your queries. If it is analysts and dbt models, start with Snowflake or BigQuery. If it is data engineers writing Spark jobs and ML pipelines, Databricks will save more time than it costs in complexity.
Getting started and operational overhead
BigQuery has the lowest barrier: a Google Cloud project, a dataset and you are querying, with 1 TiB of queries and 10 GiB of storage free every month. There is nothing to size for on-demand use, which suits startups and side projects. Snowflake requires choosing an edition, cloud and region, then creating warehouses, but day-to-day operations are minimal. New accounts start on a time-limited trial rather than a permanent free tier, so prototypes eventually cost money. Databricks retired its old Community Edition at the end of 2025 in favour of a perpetually free Free Edition for learning, and offers a trial of the full platform. Production use means deciding between classic compute in your cloud account, with cluster policies, instance types and networking to manage, or serverless compute that hides those details at a higher unit price. Expect more platform engineering effort than with the other two. For a small team without a dedicated data platform engineer, BigQuery or Snowflake will usually reach value faster. Databricks pays back that extra setup when pipelines, streaming and ML workloads are large enough to benefit from Spark and open storage.
Verdict
There is no universal winner; the right choice follows your team's skills and cloud. Choose BigQuery if you are on Google Cloud and value serverless simplicity plus a real free tier. Choose Snowflake if most consumers write SQL, you need multi-cloud, and you want the least operational work. Choose Databricks if your workload is dominated by Spark pipelines, streaming and ML, and you want your data in open formats you control. With all three now reading and writing Iceberg, picking one is less of a one-way door than it was in 2023.