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Databases · head to head

DuckDB vs Google Cloud SQL

DuckDB logo

DuckDB

Databases

MIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.

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: DuckDB a database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.; Google Cloud SQL locked into Google Cloud ecosystem with limited cross-cloud portability
  • They diverge on capability: DuckDB covers In-process execution, 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 DuckDB and Google Cloud SQL actually diverge.

Attributes where DuckDB and Google Cloud SQL differ
AttributeDuckDBGoogle Cloud SQL
Pricing modelopen-sourceusage-based
PlatformsLinux, macOS, Windows, WebAssemblyGoogle Cloud Platform
Founded20192008

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

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 DuckDB

  • In-process execution
  • Vectorised columnar engine
  • Direct file querying
  • Zero dependencies
  • Larger-than-memory queries
  • MIT licence
  • Postgres-flavoured SQL
  • Extension ecosystem

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.

DuckDB

  • Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot Google Cloud SQL
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot Google Cloud SQL
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot Google Cloud SQL
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot Google Cloud SQL

Google Cloud SQL

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

Where each one falls short

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

DuckDB

  • A database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
  • There is no network protocol, authentication or user management, so exposing it to remote clients means writing and securing your own service around it.
  • It is built for scans and aggregations, not for many small transactions, so a workload of high-frequency single-row inserts and updates performs badly compared with SQLite or Postgres.
  • Storage files are backwards compatible but not forwards compatible, so a file written by a newer version cannot be read by an older one and every consumer of a shared file must be upgraded together.
  • Query memory settings matter: some operations still need to hold significant state, so an under-configured memory limit turns a large join or a high-cardinality aggregation into a spill-heavy query or an out-of-memory failure rather than a slow success.

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

DuckDB

Free

No published plan breakdown. See the DuckDB review.

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 DuckDB if

  • You need in-process execution.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, WebAssembly.
  • You also want vectorised columnar engine.
DuckDB 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 DuckDB or Google Cloud SQL better?
Neither clearly leads. DuckDB 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, DuckDB or Google Cloud SQL?
DuckDB starts at Free and Google Cloud SQL at Free.
Does DuckDB or Google Cloud SQL run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Google Cloud SQL runs on Google Cloud Platform.
Can I use DuckDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is DuckDB best used for?
DuckDB is most often used for transformation steps in a data pipeline that would otherwise need spark, replaced by sql over parquet in a single process, analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptable, local exploration of files that are too large for a pandas dataframe but far too small to justify a warehouse, continuous integration and testing of analytical sql, where a real engine can run in the test process without provisioning anything. Of those, transformation steps in a data pipeline that would otherwise need spark, replaced by sql over parquet in a single process and analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptable are not what Google Cloud SQL is typically brought in for.
What can DuckDB do that Google Cloud SQL cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Google Cloud SQL covers High Availability, Automated Backups, Point-in-time Recovery, Encryption.

Answered from the vendors’ own pages

DuckDB: Can multiple applications share one DuckDB database?

Not for writing. One process holds the database read-write; others may attach read-only and will not see later writes. Shared multi-writer access needs a different database or a table format with a catalogue.

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
DuckDB: Is it a replacement for a data warehouse?

For single-node analytical workloads up to a few hundred gigabytes it very often is. It is not a replacement when many concurrent users need a shared, governed, always-on service.

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
DuckDB: Do I have to load data into it?

No. It queries Parquet, CSV, JSON and Arrow in place, including on object storage. Its own storage format is optional and mainly useful when you want indexes, constraints and faster repeated access.

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
DuckDB: What is MotherDuck's relationship to it?

MotherDuck is a separate company offering a managed and hybrid service built on the DuckDB engine. DuckDB itself remains MIT-licensed and independent of it, with the IP held by the DuckDB Foundation.

DuckDB: Is it suitable for OLTP?

No. It is designed for analytical scans. For transactional workloads with frequent small writes, SQLite or Postgres is the right tool.

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