DuckDB vs PostgreSQL: Embedded OLAP or Server? (2026)

Quick verdict

Short answer

Choose DuckDB when you want to analyse data inside a Python, R or Java program, a notebook or a data pipeline, especially data in Parquet, CSV or JSON files, with no server to run. Choose PostgreSQL when many users or services must read and write shared data concurrently, with roles and permissions, replication and a managed cloud option. They are often used together: DuckDB's postgres extension can query a PostgreSQL database directly, and the pg_duckdb extension, hosted in the DuckDB GitHub organisation, embeds DuckDB's engine inside PostgreSQL.

How we know: This comparison is research-based: architecture, concurrency, SQL dialect, integrations, versions and licences were checked against duckdb.org, the pg_duckdb repository and postgresql.org in October 2026. We have not run performance tests, so no speed claims are made beyond what each project documents about its own design.

DuckDB is an in-process analytical (OLAP) database. It runs embedded in a host process with no server software, stores data in columns, and executes queries on batches of values with a vectorised engine. It reads Parquet, CSV and JSON files directly and is released under the MIT License, with intellectual property held by the DuckDB Foundation. duckdb.org lists 1.5.6 (28 September 2026) as the latest stable release and 1.4.5 as the current LTS release, with 2.0.0 planned for 21 October 2026.

PostgreSQL is an open source object-relational database server. A server process manages the data, and clients connect over the network with roles and permissions. It uses multiversion concurrency control (MVCC), supports streaming and logical replication, and is extended through extensions. It is released under the PostgreSQL License, a permissive licence. postgresql.org lists 18.6 as the current release; PostgreSQL 19 is in beta, and PostgreSQL 14 reaches end of life on 12 November 2026.

Side by side

Aspect	DuckDB	PostgreSQL
Architecture	Embedded library inside your process; no server	Client-server; clients connect to a `postgres` server over the network
Designed for	Analytical queries: large scans, aggregations and joins	General purpose, with a strong focus on transactional (OLTP) workloads
Storage and execution	Columnar storage in compressed row groups; vectorised execution	Row-oriented heap tables with B-tree and other index types; parallel query for large scans
Concurrent writers	One process reads and writes a file (multiple threads, optimistic concurrency); other processes read-only	Many concurrent sessions reading and writing, using MVCC
Users and permissions	None in the engine; access follows the host program and file permissions	Roles, GRANT/REVOKE and row-level security
Replication and HA	Not built in	Streaming (physical) and logical replication; many HA tools and managed services
Reading files	Queries Parquet, CSV and JSON in place, locally or over HTTPS/S3	`COPY` loads CSV and text; no built-in Parquet reader
Licence	MIT License	PostgreSQL License
Main trade-off	No server, users or replication; not a shared multi-writer database	A server to run and secure; row storage is not designed primarily for large analytical scans

Key differences

Embedded analytics versus a shared database server

DuckDB is a library. You install it as a package for your language (the install page lists the CLI, Python, Go, Java, Node.js, C/C++, R, Rust, ODBC and WebAssembly clients), open a file or an in-memory database and run SQL. There is no network protocol, no service to monitor and no user accounts. That makes it convenient for notebooks, scripts, CI jobs and applications that need local analytics, but it also means the data is not shared with other users unless you share the file.

PostgreSQL is a server that many applications and users connect to at once. That brings work (installation, configuration, backups, upgrades, security) but also the things a shared system of record needs: authentication, roles, row-level security, replication and point-in-time recovery. Managed PostgreSQL is widely available from cloud providers.

Columnar OLAP versus row-store OLTP

DuckDB's documentation explains that its columnar-vectorised engine processes large batches of values together, which it contrasts with the row-at-a-time processing of systems such as PostgreSQL and SQLite. It targets analytical queries that read a large share of a table. Its indexing guide notes that indexes slow down changes to a table and advises defining explicit indexes only for highly selective queries, so it is not designed for high rates of small updates.

PostgreSQL stores rows together and offers B-tree, hash, GiST, SP-GiST, GIN and BRIN indexes, which suits inserting, updating and looking up individual records under concurrency. It can also run analytical queries, with parallel query for large scans, and many teams run reporting on a PostgreSQL replica. In our view, PostgreSQL is enough for analytics while data volumes and query complexity stay moderate; DuckDB becomes attractive when you need to scan large tables or files repeatedly and do not need a shared server for it.

Concurrency and transactions

DuckDB supports transactions, using MVCC combined with optimistic concurrency control inside one process. Only one process can open a database file for writing; other processes can open it read-only. Transactions that modify the same rows at the same time fail with a conflict error. For multi-process writes the docs point to the Quack remote protocol (beta in the 1.5 series) or to the DuckLake format with PostgreSQL as its catalog database.

PostgreSQL is built for many concurrent sessions. Its MVCC design means readers do not block writers and writers do not block readers, and it offers Read Committed, Repeatable Read and Serializable isolation levels.

Using them together

The two projects integrate in both directions, and both integrations are official DuckDB projects.

DuckDB reading PostgreSQL. DuckDB's postgres core extension attaches a running PostgreSQL database and can read and write its tables, so you can run DuckDB queries over live PostgreSQL data or copy it into DuckDB or Parquet. The extension is autoloaded on first use.

-- DuckDB: attach a PostgreSQL database and export a table to Parquet
ATTACH 'dbname=shop host=127.0.0.1 user=analyst' AS pg (TYPE postgres, READ_ONLY);
COPY (SELECT * FROM pg.public.orders) TO 'orders.parquet' (FORMAT parquet);

DuckDB inside PostgreSQL. pg_duckdb is a PostgreSQL extension hosted in the official DuckDB GitHub organisation, under the MIT License, built in collaboration with Hydra and MotherDuck. Its README says it integrates DuckDB's columnar-vectorised engine into PostgreSQL and lists support for PostgreSQL 14 to 18. Check the release notes for which DuckDB version a given pg_duckdb release bundles, and whether your managed PostgreSQL provider allows the extension.

SQL dialect differences

DuckDB states that its dialect closely follows PostgreSQL, and lists the exceptions. The ones most likely to surprise someone moving queries between them:

-- Integer division
SELECT 1 / 2 AS x;    -- DuckDB: 0.5   PostgreSQL: 0
SELECT 1 // 2 AS x;   -- DuckDB integer division: 0

-- Division by zero
SELECT 1.0 / 0.0 AS x;  -- DuckDB: Infinity (IEEE 754)   PostgreSQL: error

DuckDB also preserves the case of identifiers while matching them case-insensitively (PostgreSQL folds unquoted names to lower case), and its ~ regular-expression operator requires a full match, whereas PostgreSQL's matches anywhere in the string. DuckDB adds conveniences that PostgreSQL does not have, such as GROUP BY ALL, SELECT * EXCLUDE (...) and querying a file path as a table:

-- DuckDB
SELECT region, sum(total) FROM 'orders/*.parquet' GROUP BY ALL;

-- PostgreSQL: load the data first (server-side COPY, or \copy in psql)
COPY orders FROM '/data/orders.csv' WITH (FORMAT csv, HEADER true);
SELECT region, sum(total) FROM orders GROUP BY region;

Pricing and licensing

DuckDB is free and open source under the MIT License, with no paid edition of the engine. duckdb.org notes that extended support for LTS releases beyond the one-year community window is available from DuckDB Labs; prices are not published. Hosted services built on DuckDB, such as MotherDuck, are run by separate companies and priced by them.

PostgreSQL is free under the PostgreSQL License, which permits use, modification and distribution for any purpose, including commercial software. There is no paid edition from the PostgreSQL Global Development Group. Managed PostgreSQL services from cloud providers and commercial support from third-party companies are priced by those providers.

We describe licence terms only. If licence obligations matter for software you distribute, take your own legal advice.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

DuckDB strengths

No server: install a library and query data from Python, R, Java, Node.js, Go, Rust or the CLI
Columnar, vectorised engine designed for scans, aggregations and joins over large tables
Queries Parquet, CSV and JSON files in place and writes Parquet with COPY
Can attach a live PostgreSQL database and query or export it
PostgreSQL-like dialect plus conveniences such as GROUP BY ALL and SELECT * EXCLUDE

PostgreSQL strengths

Many concurrent readers and writers through MVCC, with three isolation levels
Roles, permissions and row-level security for shared, multi-user data
Streaming and logical replication, point-in-time recovery and wide managed-service availability
Large extension ecosystem, including official DuckDB integration through pg_duckdb
Permissive PostgreSQL License with no commercial edition to buy

Limitations

DuckDB limitations

Only one process can write a database file; other processes are read-only
Concurrent transactions that change the same rows fail with conflict errors
No users, permissions, replication or network access in the engine itself
Not designed for high rates of small updates; indexes slow down writes
Fast release cadence (2.0.0 planned for 21 October 2026); plan upgrades around LTS releases

PostgreSQL limitations

A server to install, configure, back up, upgrade and secure, or a managed service to pay for
Row-oriented storage is not designed primarily for large analytical scans
No built-in Parquet reader; files are loaded with COPY or through extensions
Major-version upgrades need planning; PostgreSQL 14 reaches end of life on 12 November 2026

When to choose each

Choose DuckDB if

You analyse data in notebooks, scripts or pipelines and do not want to run a server
Your data lives in Parquet, CSV or JSON files and you want to query it without loading it into a server
You need fast-to-set-up analytics inside an application or a CI job
You want to pull data out of PostgreSQL for heavy analysis without loading the production server

Choose PostgreSQL if

Many users or services must read and write the same data concurrently
You need authentication, roles, row-level security, replication or failover
The workload is transactional: orders, accounts, bookings and other individual records
You want a managed database service from a cloud provider
You want one database for transactions and moderate analytics, optionally adding pg_duckdb later

When neither is right

Analytical queries must serve many concurrent users from a shared server at large scale: look at a server-based columnar database; see ClickHouse vs PostgreSQL.
You need simple local transactional storage for one application: SQLite is lighter; see DuckDB vs SQLite and SQLite vs PostgreSQL.
You are choosing between relational servers rather than an analytics engine: see MySQL vs PostgreSQL.
Your data is document-shaped and schemas change often: see MongoDB vs PostgreSQL.

Final recommendation

Bottom line

DuckDB and PostgreSQL usually play different roles. PostgreSQL is the shared system of record: concurrent transactions, permissions, replication and managed hosting. DuckDB is the analysis engine you run next to your code: columnar, serverless and able to query Parquet and CSV files and live PostgreSQL tables directly. For most teams the practical answer is PostgreSQL for the application and DuckDB for analysis, either reading PostgreSQL through its postgres extension or running inside PostgreSQL through pg_duckdb. Choose DuckDB alone only when there is no need for a shared, multi-writer database.

Frequently asked questions

Can DuckDB replace PostgreSQL?

For a shared, multi-user application database, no. DuckDB allows only one process to write a database file and has no users, permissions or replication. It can replace PostgreSQL for local or pipeline analytics where no shared server is needed.

Can DuckDB query a PostgreSQL database directly?

Yes. DuckDB's postgres extension attaches a running PostgreSQL database with ATTACH '...' AS pg (TYPE postgres); and can read and write its tables. The extension is autoloaded on first use.

What is pg_duckdb?

pg_duckdb is a PostgreSQL extension, hosted in the DuckDB GitHub organisation under the MIT License and built in collaboration with Hydra and MotherDuck, that integrates DuckDB's analytical engine into PostgreSQL. Its README lists support for PostgreSQL 14 to 18. Managed PostgreSQL providers decide which extensions they allow, so check yours.

Is DuckDB SQL the same as PostgreSQL SQL?

Close, but not identical. DuckDB says its dialect closely follows PostgreSQL and documents the exceptions, for example 1 / 2 returns 0.5 in DuckDB and 0 in PostgreSQL, and DuckDB's ~ operator needs a full match.

Are DuckDB and PostgreSQL free?

Yes. DuckDB is under the MIT License and PostgreSQL under the PostgreSQL License; both allow commercial use. Hosted and managed services are priced by their providers.

Sources

Checked October 2026.

How we research comparisons: our editorial method.