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Databases · head to head

DuckDB vs TimescaleDB

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
TimescaleDB logo

TimescaleDB

Databases

Time-series database built on PostgreSQL for real-time analytics

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.; TimescaleDB inherits PostgreSQL write path limitations, creating a ceiling on ingestion throughput
  • They diverge on capability: DuckDB covers In-process execution, TimescaleDB covers Time-series Optimization.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which DuckDB and TimescaleDB actually diverge.

Attributes where DuckDB and TimescaleDB differ
AttributeDuckDBTimescaleDB
Pricing modelopen-sourceUnknown
PlatformsLinux, macOS, Windows, WebAssemblyLinux, macOS, Windows, Docker, Kubernetes, Cloud (AWS, GCP, Azure)
Founded20192012

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 TimescaleDB

  • Time-series Optimization
  • PostgreSQL Extension
  • Automatic Partitioning
  • Continuous Aggregates
  • Native Compression
  • Full SQL Support
  • Real-time Analytics
  • 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 TimescaleDB
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot TimescaleDB
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot TimescaleDB
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot TimescaleDB

TimescaleDB

  • Monitoringnot DuckDB
  • IoT datanot DuckDB
  • Financial datanot DuckDB
  • Log analyticsnot DuckDB
  • Observabilitynot 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.

TimescaleDB

  • Inherits PostgreSQL write path limitations, creating a ceiling on ingestion throughput
  • Operational complexity increases significantly at scale, requiring expertise in chunk tuning and autovacuum management
  • Bloom filter indexes on compressed columns can return incorrect query results before upgrade
  • PostgreSQL 15 support ending June 2026, forcing mandatory upgrades to PostgreSQL 16 or later

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

TimescaleDB

Free
  • Open SourceFree
    • Self-hosted TimescaleDB
    • MIT-licensed core
    • Full PostgreSQL compatibility
  • Scale Plan (Cloud)$36/month
    • Compute and storage charges
    • Multi-node HA
    • Unlimited VPCs

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 TimescaleDB if

  • You need time-series optimization.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes, Cloud (AWS, GCP, Azure).
  • You also want postgresql extension.
TimescaleDB review

Questions people ask

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

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.

TimescaleDB: Is TimescaleDB free?

Yes. TimescaleDB is free and open source under the Timescale License. The managed cloud service offers a free trial with $1,000 in credits expiring in 30 days.

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.

TimescaleDB: What database does TimescaleDB run on top of?

TimescaleDB is a PostgreSQL extension that runs on top of PostgreSQL. You retain full PostgreSQL compatibility including SQL queries, transactions, and ecosystem tools.

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.

TimescaleDB: How much can TimescaleDB compress data?

TimescaleDB offers transparent columnar compression that can reduce storage by up to 95%. Newer data remains in row-oriented format for fast writes, while older data is automatically compressed to the column store.

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.

TimescaleDB: Does TimescaleDB require manual partitioning?

No. TimescaleDB handles automatic time-based partitioning through hypertables. Data is automatically chunked based on time intervals, requiring no manual partition management.

Source
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.

TimescaleDB: What PostgreSQL versions does TimescaleDB support?

As of October 2025, TimescaleDB requires PostgreSQL 16 or greater. PostgreSQL 15 support will end with the June 2026 release, after which all instances must upgrade to PostgreSQL 16.

Source
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