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Databases · head to head

Apache Druid vs DuckDB

Apache Druid logo

Apache Druid

Databases

Real-time analytics database for sub-second OLAP queries

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Apache Druid open-source offering lacks high-availability, distributed architecture, and enterprise security features; 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.
  • They diverge on capability: Apache Druid covers Real-time Ingestion, DuckDB covers In-process execution.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Druid and DuckDB actually diverge.

Attributes where Apache Druid and DuckDB differ
AttributeApache DruidDuckDB
PlatformsDocker, Kubernetes, Native deployment (Java-based)Linux, macOS, Windows, WebAssembly
Founded19992019

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 Apache Druid

  • Real-time Ingestion
  • Sub-second Queries
  • Column-oriented Storage
  • Streaming Integration
  • Approximate Algorithms
  • Flexible Schemas
  • Time-based Partitioning
  • Kafka

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

What people use each for

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

Apache Druid

  • Real-time analytics platforms ingesting millions of events per second from streaming sourcesnot DuckDB
  • Applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows)not DuckDB
  • Time-series and event analysis at massive scale with columnar storage efficiencynot DuckDB

DuckDB

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

Where each one falls short

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

Apache Druid

  • Open-source offering lacks high-availability, distributed architecture, and enterprise security features
  • Requires native integration with Apache Kafka or Amazon Kinesis for real-time ingestion; custom integrations need development
  • High-concurrency query support (hundreds of thousands QPS) requires significant cluster infrastructure investment

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.

Pricing, plan by plan

Apache Druid

Free

No published plan breakdown. See the Apache Druid review.

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Which should you pick?

Choose Apache Druid if

  • You need real-time ingestion.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Native deployment (Java-based).
  • You also want sub-second queries.
Apache Druid review

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

Questions people ask

Is Apache Druid or DuckDB better?
Neither clearly leads. Apache Druid starts at Free and DuckDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Druid or DuckDB?
Apache Druid starts at Free and DuckDB at Free.
Does Apache Druid or DuckDB run on more platforms?
Apache Druid runs on Docker, Kubernetes, Native deployment (Java-based). DuckDB runs on Linux, macOS, Windows, WebAssembly.
Can I use Apache Druid for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Druid best used for?
Apache Druid is most often used for real-time analytics platforms ingesting millions of events per second from streaming sources, applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows), time-series and event analysis at massive scale with columnar storage efficiency. Of those, real-time analytics platforms ingesting millions of events per second from streaming sources and applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows) are not what DuckDB is typically brought in for.
What can Apache Druid do that DuckDB cannot?
Apache Druid covers Real-time Ingestion, Sub-second Queries, Column-oriented Storage, Streaming Integration. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.

Answered from the vendors’ own pages

Apache Druid: Is Apache Druid free to use?

Apache Druid is an open-source project with no licensing fees. It is licensed under CC BY-SA 4.0, and the Druid name and logo are trademarks of The Apache Software Foundation.

Source
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.

Apache Druid: Can I use Apache Druid for commercial purposes?

Yes, Apache Druid is open-source software available for commercial use at no cost. The CC BY-SA 4.0 license permits commercial deployment.

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.

Apache Druid: Where do I find pricing for commercial support or services?

No pricing or support tiers are published on the Apache Druid homepage. For commercial support options, contact the Apache Druid community or consult additional resources beyond the project website.

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.

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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