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Databases · head to head

BigQuery vs Google Cloud SQL

BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
Rated
-
Full reviewWebsite
Google Cloud SQL logo

Google Cloud SQL

Databases

Fully managed relational database service for MySQL, PostgreSQL, and SQL Server

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Google Cloud SQL locked into Google Cloud ecosystem with limited cross-cloud portability
  • They diverge on capability: BigQuery covers Serverless compute, Google Cloud SQL covers High Availability.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Google Cloud SQL actually diverge.

Attributes where BigQuery and Google Cloud SQL differ
AttributeBigQueryGoogle Cloud SQL
PlatformsWeb, Cloud APIGoogle Cloud Platform

Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), user rating (Not yet rated), category (Databases), founded (2008).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

Only in Google Cloud SQL

  • High Availability
  • Automated Backups
  • Point-in-time Recovery
  • Encryption
  • Regional/Zonal Instances
  • Read Replicas
  • Private IP
  • BigQuery

What people use each for

The jobs each tool is most often brought in to do.

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Google Cloud SQL
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Google Cloud SQL
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Google Cloud SQL
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Google Cloud SQL

Google Cloud SQL

  • Transaction processingnot BigQuery
  • Data storagenot BigQuery
  • Application backendnot BigQuery
  • Reportingnot BigQuery
  • Data analyticsnot BigQuery

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

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.

Google Cloud SQL

  • Locked into Google Cloud ecosystem with limited cross-cloud portability
  • Pay-as-you-go pricing can become expensive with unpredictable workloads
  • Limited customization options compared to self-managed databases

Pricing, plan by plan

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/TB
    • Pay per query
    • Pay per storage
    • All features

Google Cloud SQL

Free
  • Free TierFree
    • db-f1-micro instance
    • 30GB storage
    • Limited usage
  • Standard$25/month
    • High availability
    • Automated backups
    • Point-in-time recovery

Which should you pick?

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, Cloud API.
  • You also want separation of storage and compute.
BigQuery review

Choose Google Cloud SQL if

  • You need high availability.
  • You want to start without paying.
  • You work on Google Cloud Platform.
  • You also want automated backups.
Google Cloud SQL review

Questions people ask

Is BigQuery or Google Cloud SQL better?
Neither clearly leads. BigQuery starts at Free and Google Cloud SQL at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Google Cloud SQL?
BigQuery starts at Free and Google Cloud SQL at Free.
Does BigQuery or Google Cloud SQL run on more platforms?
BigQuery runs on Web, Cloud API. Google Cloud SQL runs on Google Cloud Platform.
Can I use BigQuery for free?
Both have a free tier, so you can try either at no cost before committing.
What is BigQuery best used for?
BigQuery is most often used 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. Of those, a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place and bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running are not what Google Cloud SQL is typically brought in for.
What can BigQuery do that Google Cloud SQL cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Google Cloud SQL covers High Availability, Automated Backups, Point-in-time Recovery, Encryption.

Answered from the vendors’ own pages

BigQuery: 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.

Google Cloud SQL: What database engines does Google Cloud SQL support?

Google Cloud SQL supports MySQL, PostgreSQL, and SQL Server. Users can choose their preferred engine when provisioning an instance and Google handles automated backups, replication, patching, and scaling.

Source
BigQuery: 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.

Google Cloud SQL: Does Google Cloud SQL have a free tier?

Google Cloud SQL does not have a free tier, though new users receive free trial credits from Google Cloud Platform. Pricing is based on compute resources (CPU and memory) and storage used, with options for committed use discounts.

Source
BigQuery: 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.

Google Cloud SQL: Can Google Cloud SQL scale automatically?

Yes. Cloud SQL automatically scales database storage and compute resources to handle increased workloads without manual intervention, and includes automated backups and high availability configurations.

Source
BigQuery: 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.

BigQuery: 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.

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