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Databases · head to head

DuckDB vs Jitsu

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
Jitsu logo

Jitsu

Developer Tools

Open source event pipeline that streams behavioural data to your own warehouse

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.; Jitsu jitsu is a pipeline, not a customer data platform, so identity resolution, audience building and reverse ETL are absent and a marketing team expecting Segment parity will be disappointed.
  • They diverge on capability: DuckDB covers In-process execution, Jitsu covers Event collection.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which DuckDB and Jitsu actually diverge.

Attributes where DuckDB and Jitsu differ
AttributeDuckDBJitsu
Pricing modelopen-sourcePer month by event volume
PlatformsLinux, macOS, Windows, WebAssemblyWeb, Linux, Docker, Kubernetes, iOS, Android
CategoryDatabasesDeveloper Tools
Founded2019Unknown

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

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 Jitsu

  • Event collection
  • Warehouse destinations
  • Connector syncs
  • Transformations
  • Bundled ClickHouse
  • Event debugger
  • Self-hosting
  • Custom domains

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

Jitsu

  • A team paying five figures a year to a customer data platform when all it actually does is send events to Snowflakenot DuckDB
  • An engineering group that needs event collection running inside its own VPC for data residency or security review reasonsnot DuckDB
  • A product analytics setup that wants raw events in the warehouse as the source of truth rather than trapped in a vendor toolnot DuckDB
  • A startup that needs first-party event collection on its own domain to reduce loss from tracker blocking without paying CDP pricesnot 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.

Jitsu

  • Jitsu is a pipeline, not a customer data platform, so identity resolution, audience building and reverse ETL are absent and a marketing team expecting Segment parity will be disappointed.
  • Connector sync frequency is deliberately tiered, with the free plan limited to manual runs and one daily sync, so anything approaching operational freshness requires the paid plan or self-hosting.
  • Self-hosting means you own the reliability of a system that drops data silently when misconfigured, and event loss is uniquely hard to notice because nothing errors, the numbers are just quietly lower.
  • The connector catalogue is far smaller than Fivetran or Airbyte, so if your requirement is pulling from many SaaS sources rather than pushing events, Jitsu is the wrong half of the problem.
  • It is a small company with a small commercial team, so enterprise procurement processes around security review, contractual SLAs and support escalation take longer than with an incumbent vendor.

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Jitsu

Free
  • Open SourceFree
    • MIT licence
    • Self-host on any cloud
    • No usage limits
  • Cloud FreeFree
    • Unlimited captured events
    • 200,000 active events per month
    • Manual connector runs only
  • Business$99/month
    • 2,000,000 active events per month
    • $40 per additional million events
    • Hourly connector sync frequency
  • Enterprise$undefined/year
    • Custom event volume
    • One minute sync frequency
    • Unlimited active syncs

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 Jitsu if

  • You need event collection.
  • You want to start without paying.
  • You work on Web, Linux, Docker, Kubernetes, iOS, Android.
  • You also want warehouse destinations.
Jitsu review

Questions people ask

Is DuckDB or Jitsu better?
Neither clearly leads. DuckDB starts at Free and Jitsu at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Jitsu?
DuckDB starts at Free and Jitsu at Free.
Does DuckDB or Jitsu run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Jitsu runs on Web, Linux, Docker, Kubernetes, iOS, Android.
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 Jitsu is typically brought in for.
What can DuckDB do that Jitsu cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Jitsu covers Event collection, Warehouse destinations, Connector syncs, Transformations.

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.

Jitsu: Is Jitsu the same as Jitsi?

No. Jitsu is an open source event data pipeline. Jitsi is an unrelated video conferencing project.

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.

Jitsu: Can I self-host for free?

Yes. The project is MIT licensed with no usage limits when self-hosted.

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.

Jitsu: How does the cost compare with Segment?

The Business plan is 99 US dollars a month for two million active events, where a per-tracked-user CDP typically costs orders of magnitude more at comparable volume.

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.

Jitsu: Does Jitsu do identity resolution?

No. It transports and transforms events; identity stitching and audiences are not part of the product.

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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