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Databases · head to head

Apache Pulsar vs DuckDB

Apache Pulsar logo

Apache Pulsar

Databases

Cloud-native messaging and streaming with separated storage

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 Pulsar more components than Kafka: brokers, BookKeeper and ZooKeeper each need operating; 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 Pulsar covers Separated storage, 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 Pulsar and DuckDB actually diverge.

Attributes where Apache Pulsar and DuckDB differ
AttributeApache PulsarDuckDB
Pricing modelOpen source, no licence feeopen-source
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, macOS, Windows, WebAssembly
FoundedUnknown2019

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 Apache Pulsar

  • Separated storage
  • Queuing and streaming
  • Built-in multi-tenancy
  • Geo-replication

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 Pulsar

  • Platforms needing both work queues and replayable streams without running two systemsnot DuckDB
  • Multi-tenant messaging where isolation between teams is a requirementnot DuckDB
  • Deployments where storage and traffic grow at genuinely different ratesnot DuckDB

DuckDB

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

Where each one falls short

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

Apache Pulsar

  • More components than Kafka: brokers, BookKeeper and ZooKeeper each need operating
  • Correspondingly harder to run well, and the expertise is rarer than Kafka expertise
  • A much smaller ecosystem of connectors, tooling and hiring pool than Kafka
  • The architectural advantages only pay off at a scale most deployments never reach

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 Pulsar

Free
  • Apache PulsarFree
    • Full functionality
    • No usage limits
    • Community support

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Which should you pick?

Choose Apache Pulsar if

  • You need separated storage.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want queuing and streaming.
Apache Pulsar 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 Pulsar or DuckDB better?
Neither clearly leads. Apache Pulsar 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 Pulsar or DuckDB?
Apache Pulsar starts at Free and DuckDB at Free.
Does Apache Pulsar or DuckDB run on more platforms?
Apache Pulsar runs on Linux, Docker, Kubernetes, Self-hosted. DuckDB runs on Linux, macOS, Windows, WebAssembly.
Can I use Apache Pulsar for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Pulsar best used for?
Apache Pulsar is most often used for platforms needing both work queues and replayable streams without running two systems, multi-tenant messaging where isolation between teams is a requirement, deployments where storage and traffic grow at genuinely different rates. Of those, platforms needing both work queues and replayable streams without running two systems and multi-tenant messaging where isolation between teams is a requirement are not what DuckDB is typically brought in for.
What can Apache Pulsar do that DuckDB cannot?
Apache Pulsar covers Separated storage, Queuing and streaming, Built-in multi-tenancy, Geo-replication. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.

Answered from the vendors’ own pages

Apache Pulsar: Is Apache Pulsar free?

Yes, open source under the Apache Software Foundation.

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 Pulsar: Pulsar or Kafka?

Pulsar separates storage from compute and covers queuing and streaming in one system. Kafka has a far larger ecosystem and hiring pool. Most teams should have a specific reason before choosing Pulsar.

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 Pulsar: Why does separated storage matter?

Brokers hold no data, so adding or replacing one requires no rebalancing, and storage can grow without adding serving capacity.

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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