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Databases · head to head

DuckDB vs Nango

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
Nango logo

Nango

Automation Integration

Open source unified API and OAuth infrastructure for product integrations, licensed under Elastic License 2.0

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.; Nango the licence is Elastic License 2.0, which is source available rather than OSI open source, and it forbids offering Nango to third parties as a managed service, so anyone planning to resell or embed it in a platform for their own customers has a genuine legal problem.
  • They diverge on capability: DuckDB covers In-process execution, Nango covers Managed OAuth.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which DuckDB and Nango actually diverge.

Attributes where DuckDB and Nango differ
AttributeDuckDBNango
Pricing modelopen-sourcePer connection per month
PlatformsLinux, macOS, Windows, WebAssemblyWeb, Linux, Docker
CategoryDatabasesAutomation Integration
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 Nango

  • Managed OAuth
  • Pre-built integrations
  • Custom syncs and actions
  • Incremental sync
  • Rate limit and retry handling
  • Webhooks
  • Self-hosting
  • Unified models

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

Nango

  • A SaaS product that needs to ship twenty customer-facing integrations without hiring a team to maintain OAuth and token refresh for eachnot DuckDB
  • A team that needs a niche or internal API integrated, which closed unified API vendors will not build for themnot DuckDB
  • A company with data residency or security constraints that must self-host the integration layer rather than send customer tokens to a vendornot DuckDB
  • An engineering team replacing a homegrown integration service whose main cost is silent token expiry and rate limit failures in productionnot 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.

Nango

  • The licence is Elastic License 2.0, which is source available rather than OSI open source, and it forbids offering Nango to third parties as a managed service, so anyone planning to resell or embed it in a platform for their own customers has a genuine legal problem.
  • Pricing is per connection where a connection is one authorised end-user account, so cost scales linearly with your customer base and a product where each user links several services multiplies quickly beyond what a headline plan price suggests.
  • Pre-built integrations vary in depth, and a connection that exists is not the same as a connection that covers the endpoints and objects your feature needs, so each one must be verified before it is designed into a roadmap.
  • Custom syncs are written in TypeScript and run in Nango model, which means integration logic lives in a vendor runtime and migrating away later requires rewriting it rather than lifting it out.
  • Self-hosting removes the vendor from the data path but transfers operational responsibility for a component that holds customer OAuth tokens, and few teams appreciate the security burden that comes with running that themselves.

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Nango

Free
  • FreeFree
    • 10 connections
    • Pre-built integrations
    • Managed OAuth
  • Starter$50/month
    • 20 connections included
    • 1 USD per additional connection
    • Custom syncs and actions
  • Growth$500/month
    • 100 connections included
    • 1 USD per additional connection
    • Higher limits
  • Enterprise$undefined/month
    • Quoted
    • Custom connection volumes
    • Security review and SLA

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 Nango if

  • You need managed oauth.
  • You want to start without paying.
  • You work on Web, Linux, Docker.
  • You also want pre-built integrations.
Nango review

Questions people ask

Is DuckDB or Nango better?
Neither clearly leads. DuckDB starts at Free and Nango at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Nango?
DuckDB starts at Free and Nango at Free.
Does DuckDB or Nango run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Nango runs on Web, Linux, Docker.
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 Nango is typically brought in for.
What can DuckDB do that Nango cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Nango covers Managed OAuth, Pre-built integrations, Custom syncs and actions, Incremental sync.

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.

Nango: Is Nango open source?

It is source available under Elastic License 2.0. You can read, modify and self-host it, but you cannot offer it to third parties as a managed service. That is not the same as an OSI approved open source licence.

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.

Nango: How is it priced?

Per connection per month, where a connection is one authorised end-user account. Free to 10 connections, 50 dollars a month for Starter with 20, 500 for Growth with 100, and one dollar per additional connection.

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.

Nango: Can we integrate an API Nango does not support?

Yes. Custom syncs and actions in TypeScript cover any API including internal ones, which is the main advantage over closed unified API products.

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.

Nango: Can we self-host it?

Yes, under the Elastic License 2.0 terms, which permit self-hosting for your own use but not resale as a service.

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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