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Business Intelligence · head to head

Cube vs DuckDB

Cube logo

Cube

Business Intelligence

AI-native analytics platform with semantic layer and governed access

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: Cube per-developer licensing can be expensive for large analytics teams; 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: Cube covers Analytics Chat, DuckDB covers In-process execution.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Cube and DuckDB actually diverge.

Attributes where Cube and DuckDB differ
AttributeCubeDuckDB
Pricing modelPer-developer seats with volume pricingopen-source
PlatformsCloud, Self-hostedLinux, macOS, Windows, WebAssembly
CategoryBusiness IntelligenceDatabases
FoundedUnknown2019

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 Cube

  • Analytics Chat
  • Workbooks
  • Dashboards
  • Embedded Analytics
  • AI Integrations
  • Core Data APIs
  • Caching and pre-aggregations

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.

Cube

  • Building governed semantic data models for analyticsnot DuckDB
  • Embedding analytics into customer-facing productsnot DuckDB
  • Enabling natural language data queries for teamsnot DuckDB
  • Creating conversational dashboards with AI assistancenot DuckDB

DuckDB

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

Where each one falls short

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

Cube

  • Per-developer licensing can be expensive for large analytics teams
  • Additional cost for Explorer and Viewer roles beyond developers
  • Semantic layer approach requires upfront modeling investment
  • Smaller connector ecosystem than dedicated BI platforms
  • May be overengineered for simple reporting needs

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

Cube

Free
  • FreeFree
    • For hobbyists and personal projects
    • Basic data source connection
    • Semantic modeling
  • Starter$40/developer/month
    • Extended agent limits
    • Premium LLMs
    • Unlimited workbooks
  • Premium$80/developer/month
    • All Starter features
    • Embedded dashboards
    • Embedded analytics chat
  • Enterprise$null/custom
    • 99.990% uptime SLA
    • Dedicated single-tenant installation
    • Bring Your Own Cloud

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Which should you pick?

Choose Cube if

  • You need analytics chat.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want workbooks.
Cube 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 Cube or DuckDB better?
Neither clearly leads. Cube 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, Cube or DuckDB?
Cube starts at Free and DuckDB at Free.
Does Cube or DuckDB run on more platforms?
Cube runs on Cloud, Self-hosted. DuckDB runs on Linux, macOS, Windows, WebAssembly.
Can I use Cube for free?
Both have a free tier, so you can try either at no cost before committing.
What is Cube best used for?
Cube is most often used for building governed semantic data models for analytics, embedding analytics into customer-facing products, enabling natural language data queries for teams, creating conversational dashboards with ai assistance. Of those, building governed semantic data models for analytics and embedding analytics into customer-facing products are not what DuckDB is typically brought in for.
What can Cube do that DuckDB cannot?
Cube covers Analytics Chat, Workbooks, Dashboards, Embedded Analytics. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.

Answered from the vendors’ own pages

Cube: What is the cost per developer on Cube?

Starter plan costs $40/developer/month. Premium adds embedded analytics at $80/developer/month. Explorer and Viewer roles cost $40 and $20/month respectively.

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.

Cube: What uptime SLAs does Cube offer?

Premium plan offers 99.950% uptime SLA. Enterprise plan provides 99.990% uptime SLA with dedicated support.

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.

Cube: Can I use Cube for free?

Yes, the Free plan includes basic data source connection, semantic modeling, workbooks, and dashboards for hobbyists and personal projects.

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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