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Databases · head to head

DuckDB vs Longhorn

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
Longhorn logo

Longhorn

Cloud

Open source distributed block storage for Kubernetes, incubating at the CNCF

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.; Longhorn there is no vendor and no SLA, so a production incident at three in the morning is your own problem unless you buy SUSE Rancher Prime support separately.
  • They diverge on capability: DuckDB covers In-process execution, Longhorn covers Per-volume controllers.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which DuckDB and Longhorn actually diverge.

Attributes where DuckDB and Longhorn differ
AttributeDuckDBLonghorn
Pricing modelopen-sourceOpen source, no licence fee
PlatformsLinux, macOS, Windows, WebAssemblyLinux, Kubernetes
CategoryDatabasesCloud
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 Longhorn

  • Per-volume controllers
  • Synchronous replication
  • Snapshots and backups
  • Volume expansion
  • Disaster recovery volumes
  • Web interface

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

Longhorn

  • An on-premises Kubernetes cluster with local disks and no SAN that needs replicated persistent volumesnot DuckDB
  • Edge sites where shipping a storage array is impractical and three nodes is the whole clusternot DuckDB
  • A K3s deployment where the storage layer must be light enough to run alongside the workloadsnot DuckDB
  • A team that wants snapshots and S3 backups of persistent volumes without paying per-node storage licencesnot 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.

Longhorn

  • There is no vendor and no SLA, so a production incident at three in the morning is your own problem unless you buy SUSE Rancher Prime support separately.
  • Synchronous replication across nodes means write latency depends on the slowest replica and the network between nodes, which makes it a poor fit for latency-sensitive databases.
  • Every replica is a full copy, so three-way replication consumes three times the raw capacity, unlike erasure-coded systems that are far more space efficient.
  • It is designed for block storage on modest clusters and does not scale to the node counts or throughput that Ceph or a commercial array handles, so growth eventually forces a migration.
  • Recovery from certain degraded states, such as a volume stuck detaching or replicas failing to rebuild, requires manual intervention and knowledge of Longhorn internals that is not widely held.

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Longhorn

Free
  • LonghornFree
    • Apache 2.0 licensed, no licence fee
    • Community support via GitHub and Slack only
    • No vendor SLA or escalation path

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 Longhorn if

  • You need per-volume controllers.
  • You want to start without paying.
  • You work on Linux, Kubernetes.
  • You also want synchronous replication.
Longhorn review

Questions people ask

Is DuckDB or Longhorn better?
Neither clearly leads. DuckDB starts at Free and Longhorn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Longhorn?
DuckDB starts at Free and Longhorn at Free.
Does DuckDB or Longhorn run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Longhorn runs on Linux, Kubernetes.
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 Longhorn is typically brought in for.
What can DuckDB do that Longhorn cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Longhorn covers Per-volume controllers, Synchronous replication, Snapshots and backups, Volume expansion.

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.

Longhorn: Who do we call when it breaks?

Nobody, unless you buy SUSE Rancher Prime, which includes commercial support for Longhorn. This is the decisive question for production use.

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.

Longhorn: How much capacity does replication cost?

Full copies, so three replicas means three times the raw capacity. Budget accordingly rather than assuming erasure coding efficiency.

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.

Longhorn: Is it suitable for production databases?

For modest workloads yes, but synchronous replication adds write latency and high-transaction databases usually want something faster.

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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