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Databases · head to head

DuckDB vs Teable

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
Teable logo

Teable

Spreadsheets

Spreadsheet interface over real PostgreSQL tables, so the data stays queryable by anything that speaks SQL

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.; Teable the automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
  • They diverge on capability: DuckDB covers In-process execution, Teable covers PostgreSQL native storage.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which DuckDB and Teable actually diverge.

Attributes where DuckDB and Teable differ
AttributeDuckDBTeable
Pricing modelopen-sourceOpen source, no licence fee
PlatformsLinux, macOS, Windows, WebAssemblyWeb, Linux
CategoryDatabasesSpreadsheets
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 Teable

  • PostgreSQL native storage
  • Multiple views
  • Linked records and rollups
  • Generated REST API
  • Real time collaboration
  • Self hosting via Docker
  • Field level permissions

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

Teable

  • A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot DuckDB
  • Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot DuckDB
  • A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot DuckDB
  • An internal tool where a grid interface and a REST API over the same table are both requirednot 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.

Teable

  • The automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
  • Self hosting moves PostgreSQL backups, version upgrades, connection pooling and capacity planning onto your team, and the licence saving disappears if that time is costed honestly.
  • The integration catalogue is small, so connecting to a common business system often means writing against the REST API rather than installing a connector.
  • The project is young relative to the products it replaces, and interface and API changes still arrive at a pace that requires reading release notes before upgrading.
  • Storing every table as a real PostgreSQL table means schema changes are real migrations, so a careless field type change on a large table can lock it far longer than a spreadsheet user would expect.

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Teable

Free
  • Self hosted open sourceFree
    • No licence fee
    • Unlimited rows subject to your PostgreSQL capacity
    • Docker deployment

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 Teable if

  • You need postgresql native storage.
  • You want to start without paying.
  • You work on Web, Linux.
  • You also want multiple views.
Teable review

Questions people ask

Is DuckDB or Teable better?
Neither clearly leads. DuckDB starts at Free and Teable at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Teable?
DuckDB starts at Free and Teable at Free.
Does DuckDB or Teable run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Teable runs on Web, Linux.
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 Teable is typically brought in for.
What can DuckDB do that Teable cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.

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.

Teable: How many rows can it actually hold?

As many as your PostgreSQL instance can serve. There is no product imposed record limit on the self hosted edition, which is the main reason to choose it.

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.

Teable: Can I query the data with SQL directly?

Yes. Tables are real PostgreSQL tables, so any SQL client or reporting tool can read them.

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.

Teable: Is self hosting genuinely free?

The licence is. The database server, backups and the engineer maintaining them are not.

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.

Teable: Does it replace Airtable feature for feature?

No. Views and field types are close; automations, apps and the integration catalogue are not.

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.

Teable: What happens to my data if the project stops?

It remains in a standard PostgreSQL database that you control, which is a materially better exit than a proprietary export format.

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