BigQueryvs
Amazon Redshift
Amazon Redshift: AWS's fully managed data warehouse with columnar storage, designed for fast query performance on large datasets.
BigQuery
Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.
As of 30 August 2026, BigQuery is free to use. BigQuery separates storage from compute completely, so there is no cluster to size and queries scale to thousands of workers automatically. Softwr lists it under Databases. BigQuery is made by Google Cloud, launched in 2008, available on Web, API.
Overview
What BigQuery does
BigQuery is Google Cloud's managed analytical data warehouse. It descends from Dremel and runs on Google's internal infrastructure: columnar storage in the Capacitor format on the Colossus file system, compute allocated dynamically as units called slots, and shuffling across the data centre network. There is no instance to provision, no vacuum to schedule and no index to maintain; you create a dataset, load or reference data and issue GoogleSQL. Around the core sit materialised views, partitioning and clustering, BigQuery ML for training models in SQL, BI Engine for in-memory acceleration of dashboards, the Storage Read and Write APIs for high-throughput ingestion and export, and BigQuery Omni for querying data that sits in other clouds. The defining choice is the pricing model, because it changes how engineers write SQL. On-demand billing charges for the bytes read from the columns a query references, so cost is proportional to the data touched rather than to time or hardware, and a `SELECT *` over a wide table can cost many times what the same filter over three columns costs. Partitioning and clustering therefore become cost controls as much as performance controls, and a badly written query is a bill rather than a slow response. The alternative is capacity pricing, where you reserve autoscaling slots and queries queue against a fixed pool, which converts unpredictable per-query costs into predictable spend plus contention. Choosing between the two, and knowing when a workload has crossed the threshold, is the single most consequential BigQuery decision a team makes. The buyers are organisations already on Google Cloud, and teams that want a warehouse with no operational surface at all. The trade-off is that BigQuery only exists inside Google. The compute cannot run anywhere else, cross-region joins are not possible so a multi-region data estate has to be designed around it, and while the data is exportable, the accumulated GoogleSQL, scheduled queries, authorised views, BigQuery ML models and IAM structure are not. It is the least operationally demanding warehouse and among the most locked-in.
What people use it for
- A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same place
- Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running
- Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load window
- Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portability
Related searches
The honest half
Where it falls short
Concrete and checkable, so you can decide whether any of them matter to you. This is the half of a review a vendor will not write about BigQuery.
- On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
- There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
- It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
- Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
- The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.
Cross-shopped
What people choose instead of BigQuery
Each pairing was judged by two reviewers asking whether a buyer would genuinely weigh the two against each other. The ones that failed were deleted rather than published.
- BigQueryvsSnowflake
Snowflake: Cloud-native data warehouse with separation of compute and storage, supporting multiple workloads.
- BigQueryvsFirebolt
Firebolt: GPU-accelerated data warehouse designed for ultra-fast analytics on massive datasets.
Pricing
What BigQuery costs
Taken from the vendor's own pricing page. Prices move, so check before you buy.
Free Tier
Free
- 1TB queries/month
- 10GB storage/month
- Standard support
On-demand
$6.25 /TB
- Pay per query
- Pay per storage
- All features
Capabilities
Features
Serverless compute
No clusters or nodes to size; a query allocates workers automatically and releases them when it finishes
Separation of storage and compute
Storage is billed independently of query capacity, so idle data is cheap to keep
Two pricing models
On-demand billing per byte scanned, or reserved autoscaling slot capacity for predictable spend
Partitioning and clustering
Time or integer partitions and sorted clusters that prune the data a query reads and therefore what it costs
Materialised views
Incrementally maintained precomputed results that queries are rewritten to use automatically
BigQuery ML
Model training and inference expressed as SQL, run where the data already is
Storage Write API
High-throughput streaming ingestion with exactly-once semantics for real-time pipelines
BI Engine
In-memory acceleration layer for dashboard queries against frequently read tables
BigQuery Omni
Queries data held in Amazon S3 or Azure storage, with the compute still run by Google
IAM integration
Access control through Google Cloud IAM at project, dataset, table, row and column level
Answered, with sources
Questions people ask
Each answer names the page it came from, so you can check it rather than take our word for it.
How is BigQuery actually billed?
Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.
How do I control query cost?
Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.
Can I use it without being on Google Cloud?
The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.
Is it suitable for serving application queries?
No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.
When should I move from on-demand to capacity pricing?
When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.
Behind it
Who makes BigQuery
- Company
- Google Cloud
- Based in
- Mountain View, California
Timeline
BigQuery over time
Keep looking
Where to go from BigQuery
- BigQuery alternativesEvery product people weigh against it
- BigQuery pricingPlans, floors and what the tiers exclude
- BigQuery badgesWhich rules it meets, and the figure behind each
- All Databases softwareThe full category
- Best Databases softwareThe shortlist, and how it was cut
Best Databases software for
Compare BigQuery with
- Amazon Redshift
- Firebolt
- MotherDuck
- FaunaDB
- DuckDB
- TiDB
- Apache Druid
- ClickHouse
- PlanetScale
- turbopuffer
- VerneMQ
- Vespa
- Xata
- YugabyteDB
- Zilliz
- Amazon RDS
- Apache Flink
- DynamoDB
Other Databases software
Document-relational database whose hosted service closed in 2025 and whose core is now unmaintained Apache 2.0 code.
Free plan14 researched notesMIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.
Free plan14 researched notesApache 2.0 distributed SQL database with MySQL wire compatibility and a separate columnar replica for analytical queries.
Free plan12 researched notesFast open-source column-oriented database for real-time analytics
Free plan12 researched notesClosed-source vector and full-text search service built directly on object storage, with cold queries measured in seconds rather than milliseconds.
From $16/mo14 researched notesErlang MQTT broker whose source is Apache 2.0 but whose official binaries need a paid subscription
Free plan12 researched notesDistributed AI search platform for retrieval, ranking, and inference
Free plan11 researched notesApache 2.0 platform for running many Postgres instances on Kubernetes, with copy-on-write branching and scale-to-zero.
Free plan14 researched notesOpen source distributed SQL database for cloud native apps
Free, then $0.1/hour13 researched notesAWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.
Free, then $0.625/million writes14 researched notes
Softwr does not host reviews and shows no star rating for BigQuery, because a rating we did not collect is not ours to publish. What is here is the pricing and platform detail from the vendor’s own pages, limitations we could state concretely, and alternatives a reviewer confirmed people weigh against it. Tell us if any of it is wrong.
More on BigQuery
BigQuery alternatives
What people switch to, and what they give up
BigQuery pricing
Every tier, and where the cost actually lands
Compare BigQuery
Put it head to head with anything we hold
BigQuery badge
Its rating, and an embed for your own site
Best Databases software alternatives
Industrial process historian with published per-tag pricing and no client licence fees
Per tag band per year
NetApp-owned managed service for Cassandra, Kafka, OpenSearch, PostgreSQL and Cadence with a bring-your-own-cloud model
quote
Erlang MQTT broker for large IoT fleets, relicensed to BSL with production free use limited to one node
Per month by connection and session volume
Attribute-based access control and masking applied inside Snowflake, Databricks and BigQuery
quote
MPP analytical database with a MySQL wire protocol and sub-second aggregation on wide tables
Open source, no licence fee
Streaming database that maintains incremental materialised views in SQL instead of Flink jobs
Per RisingWave Unit hour
Centralised data access governance from the creators of Apache Ranger, now rebranding as Trust3 AI
quote
The Meta-lineage distributed SQL query engine, distinct from the Trino fork
Open source, no licence fee