Top Google BigQuery Alternatives in 2026
- If you want data engineering, SQL analytics, and ML training running on one governed copy of data, choose Databricks. Delta Lake and Unity Catalog were built specifically to unify engineering, BI, and AI workloads instead of stitching separate systems together.
- If you need a warehouse that runs the same way on AWS, Azure, and GCP, choose Snowflake. Its editions and compute-and-storage pricing model work identically across all three clouds, unlike BigQuery, which only runs on Google Cloud.
- If you're already deep in AWS and want serverless, pay-per-second billing, choose Amazon Redshift. Redshift Serverless bills per RPU-hour, plugs directly into S3, Glue, and QuickSight, and costs nothing while idle.
- If your data is spread across several existing warehouses and lakes and you need one SQL layer over all of them, choose Starburst. Its managed Trino engine has 50+ connectors and is built to query data in place, including BigQuery itself, without copying it anywhere.
- If you're built around Google Analytics 4, Google Ads, or Firebase and want SQL-based ML without standing up a separate platform, choose stay on Google BigQuery. None of these five alternatives match BigQuery's native Google Cloud connectors or the simplicity of running ML with plain SQL through BigQuery ML.
Google BigQuery is a serverless warehouse that scales compute automatically and bills per query scanned. Teams start looking elsewhere for two main reasons: on-demand costs can surprise you on large or unfiltered scans, and BigQuery is built around Google Cloud, which is a limit for teams doing large-scale data engineering, running multi-cloud, or building custom ML pipelines beyond BigQuery ML.
The five alternatives below split into three groups. Databricks and Dremio are lakehouses built on open table formats, with engineering and AI layered on top. Snowflake and Amazon Redshift are SQL-first warehouses like BigQuery itself, one multi-cloud and one AWS-only. Starburst is different again: instead of replacing your warehouse, it queries several systems at once, including BigQuery, without moving the data.
Google BigQuery alternatives compared
| Tool | Best for | Starting price | Free option | Last update |
|---|---|---|---|---|
| DatabricksBest for unified analytics and AI | Data engineering and data science teams running large-scale Spark pipelines and ML training on the same data | Free tier + custom | Yes | July 2026 |
| SnowflakeBest for multi-cloud portability | Teams that need to run SQL analytics, data engineering, and AI workloads on one governed copy of data instead of stitching together separate systems | Custom / quote | Trial (30 days with $400 in free credits) | June 2026 |
| Amazon RedshiftBest for AWS-native teams | Teams already on AWS who want a data warehouse tightly integrated with S3, Glue, QuickSight, and other AWS services | $0.375/RPU-hour | Trial ($300 credit (90-day expiration) for first-time Redshift Serverless users; in regions without Serverless, a two-month free trial for provisioned clusters (up to 750 hours/month)) | April 2026 |
| DremioBest free option | Teams that already store data as Apache Iceberg tables and want to query it without moving it into a warehouse | $0.2/DCU | Yes | July 2026 |
| Starburst | Teams that need to query data across multiple warehouses, lakes, and databases without building new ETL pipelines | $0.5/credit | Yes | May 2026 |
Why teams switch from Google BigQuery
On-demand costs are hard to predict
BigQuery's default pricing charges $6.25 per TiB scanned. An unfiltered query on a large table can produce a surprising bill if a team hasn't set up partitioning and clustering.
It's built around Google Cloud, not multi-cloud
BigQuery runs only on Google Cloud. Teams pursuing a multi-cloud or cloud-agnostic strategy, or already committed to AWS or Azure, look at Snowflake, Databricks, or Redshift instead.
Editions add a second pricing model to learn
Once query volume outgrows on-demand pricing, Standard, Enterprise, and Enterprise Plus editions each carry their own slot-hour rate and commitment terms, on top of the on-demand model teams already had to learn.
Teams need data engineering and ML beyond SQL
BigQuery ML covers SQL-based model training, but teams running large Spark pipelines or building custom ML platforms outgrow it and move to Databricks, which unifies engineering and ML on one copy of data.
The best Google BigQuery alternatives, ranked
Databricks is the pick for teams that need more than SQL analytics. It stores data once in open Delta Lake tables and runs data engineering, BI, and ML training against that same copy, which BigQuery doesn't attempt since it's built as a SQL-first warehouse rather than a Spark-based engineering platform. Unity Catalog governs all of it centrally, and it runs on AWS, Azure, and GCP rather than Google Cloud alone. The tradeoff shows up in cost and complexity. Databricks bills per Databricks Unit consumed per second with no published rate card, so you need a calculator or a sales quote to estimate spend, and third-party benchmarking puts the median customer at roughly $300,000 a year. It also assumes real comfort with Spark and cluster configuration. Teams whose whole workload is SQL dashboards, without engineering or ML needs, will find this more platform than they need.
Pros
- + One platform for data engineering, SQL analytics, and ML/AI, so you avoid separate warehouse-plus-ML-platform sprawl
- + Open formats, Delta Lake and Unity Catalog, cut lock-in compared with proprietary warehouse storage formats
- + Runs natively on AWS, Azure, and GCP, which helps multi-cloud or cloud-migrating organizations
Cons
- – No public price list. Total cost depends on DBU rates that vary by cloud, region, and workload, and you need a calculator or a sales call to estimate it
- – Usage-based DBU billing, plus separate cloud infrastructure cost, makes budgeting and cost governance harder than flat per-seat pricing
Snowflake competes with BigQuery as a pure SQL data warehouse, and the biggest practical difference is reach: it runs natively on AWS, Azure, and GCP, while BigQuery is Google Cloud only. Like BigQuery, it separates compute and storage billing, but instead of paying per TiB scanned, you pay per second for Snowflake credits consumed by a warehouse, and idle warehouses can auto-suspend to stop the meter. Snowpark extends SQL into Python, Java, and Scala, and Cortex adds managed LLM functions, covering some of the same ground as BigQuery ML. There's no free tier, only a 30-day trial with $400 in credits, so evaluation has a clock on it. Snowflake also publishes no flat price. Higher-security editions, Business Critical and Virtual Private Snowflake, require a sales conversation rather than a self-serve upgrade.
Pros
- + Compute and storage scale and bill independently, so idle warehouses can auto-suspend to stop charges
- + Native support for Apache Iceberg tables lets teams query open-format data without duplicating it into proprietary storage
- + Snowpark and Cortex let SQL, Python, and AI/LLM workloads run against the same data and governance model instead of separate platforms
Cons
- – No published flat price. You have to use the consumption calculator or get a sales quote to estimate real monthly cost
- – Per-credit compute rates differ by edition, cloud provider, and region, which makes it harder to compare quotes
Amazon Redshift is the closest match to BigQuery's on-demand simplicity, but built for AWS instead of Google Cloud. Redshift Serverless bills per RPU-hour and drops to zero cost when idle, similar in spirit to BigQuery's pay-per-scan model, though a 4-RPU minimum means an active workgroup runs about $1.50/hour once it's working. Provisioned clusters remain available for steady workloads, with Reserved Instance discounts for 1- or 3-year commitments. Redshift Spectrum queries data sitting in S3 directly at $5 per TB scanned, without a separate load step, which mirrors BigQuery's approach to letting you query without heavy upfront ETL. The clear limit is AWS lock-in: Redshift doesn't run on GCP or Azure. There's also no standing free tier, only a 90-day/$300 Serverless credit or a two-month provisioned trial, after which standard billing starts automatically.
Pros
- + Deep native integration with the AWS ecosystem (S3, Glue, Lambda, QuickSight, SageMaker, IAM) means less glue code for teams already on AWS
- + Redshift Serverless removes cluster sizing and management. You pay per RPU-hour and pay nothing while idle
- + Redshift Spectrum lets you query data sitting in S3 directly, without loading it into the warehouse first, at $5/TB scanned
Cons
- – No standing free tier. The only no-cost options are a 90-day/$300 Serverless credit or a two-month provisioned trial. After that, standard on-demand billing kicks in automatically
- – Pricing is split across compute (provisioned node-hour or serverless RPU-hour), managed storage (per GB-month), Spectrum scans (per TB), and Concurrency Scaling, so total cost is harder to estimate up front than with single-metric competitors
Dremio takes a narrower approach than a full warehouse swap. It's built directly on Apache Iceberg and queries tables in place using its own engine and a caching layer called Reflections, instead of asking you to load data into a proprietary format the way BigQuery does. Community Edition is free forever and self-managed, a real option for teams that want to run a lakehouse query layer without a subscription, though it skips governance and cataloging features. Dremio Cloud, the fully managed option, bills $0.20 per Dremio Compute Unit, a usage measure tied to query execution and background processing rather than a simple per-query charge. The biggest open question in mid-2026 is ownership: SAP completed its acquisition of Dremio on July 6, 2026, so pricing, roadmap, and support terms could shift as it's absorbed into SAP's data and AI lineup. Teams considering it should watch that transition closely.
Pros
- + Community Edition is free with no time limit and no row or data caps for self-hosting
- + Queries Iceberg tables directly, so you skip a separate ETL or copy step into a warehouse
- + Reflections (materialized, auto-maintained accelerations) can cut repeat-query latency a lot without manual tuning
Cons
- – Enterprise pricing is quote-only, so it's hard to budget or compare against fixed-price competitors upfront
- – Community Edition leaves out governance and cataloging features, pushing serious deployments toward paid tiers
Starburst isn't a warehouse replacement so much as a query layer that sits on top of BigQuery and other systems at once. It's a managed distribution of Trino, and it can run federated SQL across S3, Snowflake, BigQuery, Redshift, Databricks, and more, without moving data into a new home first. That makes it the right fit for teams whose data is already scattered across several platforms and who need one SQL layer over all of it, rather than teams simply looking for a BigQuery replacement. Starburst Galaxy has a genuinely free tier for up to 3 clusters with no time limit, and paid tiers scale from $0.50 per credit (Pro) up to $1.00 per credit (Mission Critical), with fine-grained access control like ABAC and SCIM reserved for Enterprise and above. AIDA, its natural-language query assistant, only reached general availability in May 2026. Starburst Enterprise, the self-managed option, is quote-only with no public pricing.
Pros
- + Queries data in place across S3, Snowflake, BigQuery, Redshift, Databricks, and other sources, no data movement required
- + Free forever tier for up to 3 clusters, good for evaluation or small workloads
- + Current CTO Martin Traverso and other original Presto/Trino creators sit on Starburst's technical leadership team, so the company stays close to the open-source engine
Cons
- – Usage-based credit pricing makes cost forecasting harder than flat per-seat plans, and the effective rate depends on your cloud provider and region
- – Starburst Enterprise pricing is quote-only with no public numbers
Google BigQuery alternatives: FAQ
What's the best BigQuery alternative for teams that also need data engineering and ML?+
Databricks. It runs Spark-based data engineering, SQL analytics, and ML training against one copy of data in Delta Lake, governed through Unity Catalog, which goes further than BigQuery ML's SQL-only approach.
Is there a BigQuery alternative that runs on AWS and Azure, not just Google Cloud?+
Yes. Snowflake and Databricks both run natively on AWS, Azure, and GCP. Amazon Redshift is the AWS-native option but doesn't run on other clouds.
Is there a free alternative to BigQuery?+
Dremio's Community Edition is free forever and self-managed. Starburst Galaxy has a free tier limited to 3 clusters with no time limit. BigQuery itself already has a free monthly allowance and a no-credit-card Sandbox, so cost may not be the reason to switch.
What's the best BigQuery alternative for a team already on AWS?+
Amazon Redshift. It's AWS's own managed warehouse, with Redshift Serverless billing per RPU-hour and native integration with S3, Glue, and QuickSight that BigQuery can't match outside Google Cloud.
Google BigQuery alternatives: pricing compared
Entry price, billing model, and whether pricing is public. 4 of 6 publish pricing you can check without talking to sales.
| Tool | Starting price | Billing | Free option | Pricing disclosed |
|---|---|---|---|---|
| Google BigQuery | $6.25/TiB scanned | usage-based | Yes | Public |
| Databricks | Free tier + custom | usage-based | Yes | Not disclosed |
| Snowflake | Custom / quote | usage-based | Trial (30 days with $400 in free credits) | Not disclosed |
| Amazon Redshift | $0.375/RPU-hour | usage-based | Trial ($300 credit (90-day expiration) for first-time Redshift Serverless users; in regions without Serverless, a two-month free trial for provisioned clusters (up to 750 hours/month)) | Partly public |
| Dremio | $0.2/DCU | usage-based | Yes | Partly public |
| Starburst | $0.5/credit | usage-based | Yes | Public |
How we made these picks. We compare tools on public pricing, features, and hands-on assessment, then verify every price against the vendor's own page. We never accept payment for rankings. Read the full methodology.