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Databases · head to head

DuckDB vs QuestDB

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
QuestDB logo

QuestDB

Databases

Fast open source time-series database for high throughput ingestion

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.; QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features
  • They diverge on capability: DuckDB covers In-process execution, QuestDB covers High Throughput Ingestion.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which DuckDB and QuestDB actually diverge.

Attributes where DuckDB and QuestDB differ
AttributeDuckDBQuestDB
PlatformsLinux, macOS, Windows, WebAssemblyDocker, Kubernetes, Cloud (AWS, Azure, GCP)
Founded20192014

Identical on both: starting price (Free), pricing model (open-source), 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 QuestDB

  • High Throughput Ingestion
  • SQL Support
  • Time-series Optimization
  • SIMD Vectorization
  • Column-oriented Storage
  • Built-in Web Console
  • InfluxDB Line Protocol
  • PostgreSQL

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 QuestDB
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot QuestDB
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot QuestDB
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot QuestDB

QuestDB

  • Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot DuckDB
  • Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot DuckDB
  • Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not 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.

QuestDB

  • Open-source edition lacks high-availability, distributed architecture, and enterprise security features
  • Enterprise edition pricing not published; requires contacting sales for custom quote
  • Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

QuestDB

Free

No published plan breakdown. See the QuestDB review.

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 QuestDB if

  • You need high throughput ingestion.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
  • You also want sql support.
QuestDB review

Questions people ask

Is DuckDB or QuestDB better?
Neither clearly leads. DuckDB starts at Free and QuestDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or QuestDB?
DuckDB starts at Free and QuestDB at Free.
Does DuckDB or QuestDB run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
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 QuestDB is typically brought in for.
What can DuckDB do that QuestDB cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization.

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.

QuestDB: How much does QuestDB Enterprise cost?

QuestDB does not publish specific pricing for the Enterprise tier. Customers must contact QuestDB via their enterprise contact form to receive a custom quote.

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.

QuestDB: Does QuestDB offer a free version?

Yes, QuestDB Open Source is completely free and recommended for evaluation, prototyping, and pilot projects. Enterprise features, high availability, security, and dedicated support require the paid Enterprise tier.

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.

QuestDB: What deployment options does QuestDB offer?

QuestDB offers open source deployment, Enterprise deployment, and Bring Your Own Cloud (BYOC) deployment. Pricing details for BYOC and Enterprise tiers are not published and require direct contact with sales.

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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