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Databases · head to head

DuckDB vs Sigma Computing

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
Sigma Computing logo

Sigma Computing

Spreadsheets

Spreadsheet-style analytics that queries a cloud data warehouse directly with no extract layer

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only DuckDB has a free tier, so it costs nothing to try first.
  • 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.; Sigma Computing every user interaction is a live warehouse query, so compute costs rise with adoption and land on the warehouse invoice rather than the BI line item, which routinely surprises the team that approved the purchase.
  • They diverge on capability: DuckDB covers In-process execution, Sigma Computing covers Spreadsheet interface over SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which DuckDB and Sigma Computing actually diverge.

Attributes where DuckDB and Sigma Computing differ
AttributeDuckDBSigma Computing
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsLinux, macOS, Windows, WebAssemblyWeb
CategoryDatabasesSpreadsheets
Founded2019Unknown

Identical on both: 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 Sigma Computing

  • Spreadsheet interface over SQL
  • No extract layer
  • Input tables and write-back
  • Workbooks
  • Embedded analytics
  • Version control and dbt awareness

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

Sigma Computing

  • A finance or operations team that lives in spreadsheets and needs warehouse-scale data without exporting CSVsnot DuckDB
  • An organisation that has consolidated on Snowflake or Databricks and wants one governed access layer rather than several desktop toolsnot DuckDB
  • Planning and scenario work where users need to type assumptions back into governed tables rather than into a local filenot DuckDB
  • Embedding customer-facing analytics into a product where each tenant must only see their own rowsnot 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.

Sigma Computing

  • Every user interaction is a live warehouse query, so compute costs rise with adoption and land on the warehouse invoice rather than the BI line item, which routinely surprises the team that approved the purchase.
  • It cannot work without a supported cloud data warehouse, so an organisation running analytics on an on-premises database or a plain application database has nothing to connect to.
  • Pricing is not published and seats are differentiated by capability, so budgeting requires a sales cycle and a headcount forecast before you can compare it with a tool you could simply buy.
  • Performance is only as good as the warehouse behind it, which means slow or badly modelled tables surface as a slow interface and the fix is a data engineering project, not a Sigma setting.
  • Statistical and scientific visualisation is limited compared with dedicated tools, so anything beyond standard business charting has to be done elsewhere and brought back in.

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Sigma Computing

On request
  • Sigma Computing$undefined/year
    • Quoted per organisation
    • Seat types are differentiated by whether a user views, explores or authors
    • Warehouse compute is billed separately by your cloud data warehouse vendor

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 Sigma Computing if

  • You need spreadsheet interface over sql.
  • You also want no extract layer.
Sigma Computing review

Questions people ask

Is DuckDB or Sigma Computing better?
Neither clearly leads. DuckDB starts at Free and Sigma Computing at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Sigma Computing?
DuckDB has a free tier; the other does not. Paid plans start at Free for DuckDB and On request for Sigma Computing.
Does DuckDB or Sigma Computing run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Sigma Computing runs on Web.
Can I use DuckDB for free?
Yes. DuckDB has a free tier, so you can try it without paying. Sigma Computing starts at On request.
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 Sigma Computing is typically brought in for.
What can DuckDB do that Sigma Computing cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Workbooks.

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.

Sigma Computing: Does Sigma store a copy of my data?

No. Queries are executed on your warehouse and results are returned for display, which is why there is no extract refresh to manage.

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.

Sigma Computing: What does it actually cost?

The vendor does not publish prices. Expect an annual contract priced by seat type, plus a warehouse compute increase you should model separately.

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.

Sigma Computing: Can business users break the warehouse?

They can run expensive queries. Warehouse sizing, query limits and materialised tables are the controls, and they need configuring before a wide rollout.

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.

Sigma Computing: Is it a replacement for a modelling layer like dbt?

No. Sigma works best on top of modelled tables and is usually deployed alongside dbt rather than instead of it.

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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