If you need to run on more than one cloud or move off Google Cloud, Snowflake runs on AWS, Azure and Google Cloud with time-based compute billing instead of per-scan charges. If you are moving to AWS, Amazon Redshift is the warehouse and Amazon Athena is the closest serverless, pay-per-scan equivalent. For a lakehouse with Spark and ML on the same tables, Databricks; for a Microsoft estate, Microsoft Fabric. For dashboards that need consistently fast queries, ClickHouse and Firebolt. If your data is in the gigabytes, DuckDB or MotherDuck may cost far less than any cloud warehouse.
BigQuery is Google Cloud's serverless data warehouse. There are no clusters or warehouses to size. Compute is bought in one of two ways: on-demand, billed per TiB of data scanned by each query (with the first 1 TiB a month free), or capacity, where you reserve slots through the Standard, Enterprise or Enterprise Plus edition, with autoscaling and optional commitments. Storage is billed separately on logical or physical bytes.
Teams that look for alternatives usually have one of these reasons. The first three follow from Google's own pricing and editions documentation; the last is our editorial reading rather than survey data.
- Scan-based bills. On-demand cost depends on how many bytes each query reads, so a dashboard on an unpartitioned table, or an analyst running SELECT * on a large table, can cost far more than expected. Capacity editions make spend more predictable but move the work to slot planning.
- Tied to Google Cloud. BigQuery runs in Google Cloud regions. BigQuery Omni can query data in Amazon S3 and Azure Blob Storage, but Google documents it in six AWS regions and one Azure region. Organisations standardising on AWS or Azure often want a warehouse that runs natively there.
- Features depend on the edition. Google documents that the Standard edition does not include BigQuery ML, continuous queries or column- and row-level security, that managed disaster recovery is in Enterprise Plus, and that Omni works only with Enterprise edition reservations or on-demand pricing.
- Latency and data size. Customer-facing analytics with many concurrent sub-second queries, or data sets of a few gigabytes, are served by different tools (editorial).
For head-to-head pages, see Snowflake vs BigQuery, BigQuery vs Redshift, Databricks vs BigQuery, ClickHouse vs BigQuery and DuckDB vs BigQuery.
Quick picks
Runs on AWS, Azure and Google Cloud and bills compute by warehouse running time rather than bytes scanned.
Serverless SQL over Amazon S3, billed per data scanned or by provisioned capacity, with a Trino-derived engine.
Provisioned RG and RA3 clusters or Redshift Serverless, with queries over Iceberg tables in S3.
Apache 2.0 open source engine, managed on AWS, Google Cloud and Azure.
Free in-process engine, with a managed service that has a free plan.
How we chose
We considered Snowflake, Amazon Redshift, Amazon Athena, Databricks, Microsoft Fabric, ClickHouse Cloud, Firebolt, DuckDB with MotherDuck, Starburst and Dremio. To be included, a product had to be sold or actively developed in October 2026, run analytical SQL over large data sets, and publish its pricing model and supported clouds on an official page. Starburst and Dremio are covered in our Snowflake alternatives guide; they matter less here because BigQuery users rarely start from a self-managed lake.
For each product we recorded the billing unit (especially whether it charges per data scanned or per compute time), the clouds it runs on, open table format support, its free tier or trial, and its main limitation. Exact prices are not quoted because they vary by edition, cloud and region. None of this comes from our own use of the platforms.
The order is not a ranking. Entries start with general-purpose warehouses on other clouds, then lakehouse platforms, then low-latency engines, then single-machine options.
At a glance
| Platform | Pricing model | Clouds | Open formats | Best for | Main trade-off |
|---|---|---|---|---|---|
| Snowflake | Credits for warehouse running time, plus storage | AWS, Azure, Google Cloud | Iceberg tables (Snowflake or external catalog) | Multi-cloud SQL warehousing | Warehouses to size and suspend |
| Amazon Redshift | Node-hours (RG, RA3) or Serverless RPU-hours | AWS only | Reads Iceberg via AWS Glue Data Catalog | Moving analytics to AWS | AWS only; more tuning choices |
| Amazon Athena | Per data scanned, or provisioned capacity (DPU-hours) | AWS only | Iceberg on S3 via AWS Glue Data Catalog | Serverless SQL over S3 | Same scan-cost discipline as BigQuery |
| Databricks | DBUs per second, plus cloud VM costs on classic compute | AWS, Azure, Google Cloud | Delta Lake; managed Iceberg tables | Lakehouse with Spark and ML | More to configure; two bills on classic compute |
| Microsoft Fabric | Capacity units (F SKUs), OneLake storage separate | Microsoft-hosted SaaS on Azure | Delta Parquet and Iceberg in OneLake | Power BI and Microsoft 365 shops | Capacity sizing; Power BI licences below F64 |
| ClickHouse Cloud | Compute units per minute plus storage | AWS, Google Cloud, Azure; BYOC | Reads and writes Iceberg (with limits) | Real-time and user-facing analytics | Different SQL dialect and modelling |
| Firebolt | Per-second engine billing, storage pass-through | AWS and Google Cloud (GA), Azure (preview) | Reads Parquet and Iceberg | Low-latency, high-concurrency queries | Smaller ecosystem |
| DuckDB and MotherDuck | DuckDB free; MotherDuck free plan then usage-based | Any machine; MotherDuck on AWS | Parquet; Iceberg (read, and write via REST catalog) | Data that fits on one machine | Not a multi-user enterprise warehouse |
Snowflake
Snowflake is the closest general-purpose alternative that is not tied to one cloud: each Snowflake account runs on AWS, Azure or Google Cloud, so a team can stay on Google Cloud during a migration and move later. Instead of paying per byte scanned, you pay credits while a virtual warehouse runs, billed per second after a 60-second minimum each time it starts, plus storage on average compressed volume. Credit prices depend on edition, cloud and region.
That changes the cost question from "how much does each query read" to "how long do warehouses run". Snowflake supports Apache Iceberg tables with its own or external catalogs, and its trial lasts 30 days or until the free usage balance is used. See Snowflake vs BigQuery.
- You size, suspend and resume virtual warehouses, which BigQuery users have not had to manage.
- Idle-but-running warehouses still use credits, so auto-suspend settings matter.
- SQL dialect differences (for example in arrays, structs and date functions) mean queries need rewriting.
Amazon Redshift
Amazon Redshift is AWS's data warehouse, offered as provisioned clusters or Redshift Serverless. AWS recommends RG (Graviton-based) or RA3 nodes, both with managed storage billed separately from compute, and provisioned clusters can be paused. Redshift Serverless bills in RPU-hours per second with a 60-second minimum, which is the nearest Redshift equivalent to BigQuery's no-cluster model, and AWS offers a free trial credit for new Serverless users.
Redshift can query Apache Iceberg tables in the AWS Glue Data Catalog and join them with local tables. See BigQuery vs Redshift.
- AWS only.
- Provisioned clusters need node type and size decisions that BigQuery users do not make.
- AWS documents that Python UDFs are not supported after 30 June 2026, and Iceberg time travel queries are not supported.
Amazon Athena
Amazon Athena is the AWS service closest to BigQuery's on-demand model: serverless SQL billed by the amount of data each query scans, rounded up per megabyte with a 10 MB minimum per query, or alternatively by provisioned capacity in DPU-hours. Data stays in Amazon S3, typically catalogued in the AWS Glue Data Catalog, and Athena can query and manage Iceberg tables.
AWS documents that Athena engine version 3 tracks the open source Trino and Presto projects, so its SQL follows Trino functions. A Spark option is billed separately. See Trino vs Presto for the engine background.
- Per-scan billing means the same cost discipline as BigQuery on-demand: partition, compress and avoid SELECT *.
- S3 storage, requests, Glue Data Catalog and federated query Lambda calls are billed separately.
- Trino and Presto connectors are not supported; other sources go through Athena Federated Query.
Databricks
Databricks stores data as Delta Lake or Iceberg tables in your own object storage, governed by Unity Catalog, and runs Spark, pipelines, ML and Databricks SQL warehouses over them on AWS, Azure or Google Cloud. Billing is in DBUs per second; on classic compute your cloud provider also bills for the VMs, while serverless compute runs without provisioning resources in your account.
It suits BigQuery users whose work is shifting from SQL reporting to data engineering and machine learning. Databricks documents an Iceberg REST Catalog API in Unity Catalog that external engines can use to read and write managed Iceberg tables. Free Edition is for personal, non-commercial use only. See Databricks vs BigQuery.
- More to configure (compute, jobs, catalogs) than BigQuery.
- Classic compute produces both a Databricks bill and a cloud provider bill.
- Free Edition does not allow commercial use.
Microsoft Fabric
Microsoft Fabric bundles a T-SQL warehouse, Spark, Data Factory pipelines, real-time analytics and Power BI under one capacity. F SKUs run from F2 to F8192 capacity units, billed per second with a one-minute minimum on pay-as-you-go or discounted through reservations, and capacities can be paused; OneLake storage is billed separately. A 60-day trial capacity is available.
For BigQuery users the appeal is a fixed capacity rather than per-scan billing, and tight integration with Power BI and Microsoft 365. OneLake stores tables as Delta Parquet or Iceberg, and shortcuts can reference data in other clouds, including Amazon S3, without copying.
- Capacity is shared by all workloads, so heavy jobs can throttle others.
- On F SKUs smaller than F64, Power BI viewers need Pro or Premium Per User licences.
- Runs only as a Microsoft service.
ClickHouse Cloud
ClickHouse is an Apache 2.0 licensed column-oriented database built for fast aggregations over large append-heavy tables such as events, logs and metrics. ClickHouse Cloud runs on AWS, Google Cloud and Azure in Basic, Scale and Enterprise tiers, metered per minute in compute units with storage billed separately; there is also a bring-your-own-cloud option and a 30-day trial with credits that needs no card.
Because it is available on Google Cloud, teams can move a latency-sensitive workload off BigQuery without leaving Google Cloud, and billing follows compute time rather than bytes scanned. See ClickHouse vs BigQuery.
- Table design (table engines, sort keys) needs more up-front modelling than BigQuery.
- Frequent row-level updates and deletes are not its strength.
- Its SQL dialect differs from GoogleSQL, so queries need rewriting.
Firebolt
Firebolt is an analytical database aimed at applications that run many fast concurrent queries. Its documentation lists the managed service as generally available on AWS and Google Cloud and in preview on Azure. Engines are billed per second with auto-stop and resume, and object storage is charged through at cost, so spend follows engine running time rather than data scanned; new users receive trial credits.
It reads Parquet and Iceberg, and also offers bring-your-own-cloud and a self-hosted open source edition (Firebolt OSS), which was in preview when checked.
- Smaller ecosystem of integrations and practitioners than BigQuery.
- The open source edition and Azure support are in preview.
- Engines must be sized and managed, unlike BigQuery on-demand.
DuckDB and MotherDuck
DuckDB is an MIT-licensed analytical database that runs inside your application or notebook with no server. It reads Parquet and CSV directly, including from cloud storage, and its Iceberg extension reads Iceberg tables and writes to tables in an Iceberg REST catalog. The current stable release is 1.5.6; DuckDB 2.0.0 is scheduled for 21 October 2026, so check the release notes.
MotherDuck is a managed service built on DuckDB, on AWS in six regions. Its free Lite plan includes 10 GB of storage and 10 hours of Pulse compute a month for up to 3 users, as listed on 7 October 2026, with Business and Enterprise plans above it. In our view, teams using BigQuery for a few gigabytes of data should test this route first. See DuckDB vs BigQuery.
- DuckDB is single-node and in-process, not a shared warehouse by itself.
- MotherDuck runs only on AWS, so data in Google Cloud Storage crosses clouds.
- A major DuckDB release (2.0) is imminent.
When to stay with BigQuery
Stay with BigQuery if your data and applications are on Google Cloud and the problem is cost on on-demand pricing. Partitioning and clustering tables, setting maximum bytes billed on queries, and moving steady workloads to a capacity edition with autoscaling can change the bill without a migration. BigQuery's lack of clusters to manage is something most alternatives here do not offer in the same way.
Moving makes sense when the cloud strategy changes (Snowflake, Redshift, Athena, Fabric), when engineering and ML outgrow SQL (Databricks), when dashboards need consistently low latency (ClickHouse, Firebolt), or when the data is small (DuckDB). BigQuery's Iceberg managed tables in your own Cloud Storage bucket can make a gradual move easier, because other engines can read the same tables. If you only need an operational database, see BigQuery vs PostgreSQL and data warehouse vs database.
Frequently asked questions
What is the AWS equivalent of BigQuery?
Amazon Athena is closest in billing model (serverless, charged per data scanned), and Amazon Redshift, especially Redshift Serverless, is the AWS data warehouse. See BigQuery vs Redshift.
Is Snowflake cheaper than BigQuery?
It depends on the workload. BigQuery on-demand charges for bytes scanned; Snowflake charges for warehouse running time. Spiky ad hoc queries on well-partitioned data can favour BigQuery, while long scans on a small warehouse can favour Snowflake. Model both with the vendors' calculators. See Snowflake vs BigQuery.
Is there an open source alternative to BigQuery?
ClickHouse (Apache 2.0), DuckDB (MIT) and Trino (Apache 2.0) are open source analytical engines. None is a drop-in serverless replacement; you either run them yourself or use their managed services.
Does BigQuery have a free tier?
Yes. Google's pricing page, checked on 7 October 2026, lists 1 TiB of query processing and 10 GiB of storage free each month.
Can I keep my data on Google Cloud and use another warehouse?
Yes. Snowflake, Databricks, ClickHouse Cloud and Firebolt all run on Google Cloud. BigQuery Iceberg managed tables also store data in your own Cloud Storage bucket where other Iceberg engines can read it.
Sources
- BigQuery pricing
- BigQuery editions
- BigQuery Omni
- BigQuery Iceberg managed tables
- Snowflake pricing options
- Snowflake: understanding compute cost
- Snowflake: supported cloud platforms
- Amazon Redshift pricing
- Amazon Redshift provisioned clusters and node types
- Amazon Redshift: Apache Iceberg tables
- Amazon Athena pricing
- Athena engine version 3
- Databricks pricing
- Databricks: Iceberg in Unity Catalog
- Microsoft Fabric pricing
- Microsoft Fabric licences and capacity
- OneLake overview
- ClickHouse Cloud pricing
- Firebolt pricing
- Firebolt documentation
- DuckDB Iceberg extension
- MotherDuck pricing
Checked October 2026.
How we research these guides: our editorial method.