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Databases · head to head

DuckDB vs Lytics

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
L

Lytics

Automation Integration

The customer data platform for personalization

From
$400/month
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.; Lytics billed in credits where one credit is an update to a user profile, so the bill tracks how often profiles change rather than how many exist
  • They diverge on capability: DuckDB covers In-process execution, Lytics covers Data collection.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which DuckDB and Lytics actually diverge.

Attributes where DuckDB and Lytics differ
AttributeDuckDBLytics
Starting priceFree$400/month
Pricing modelopen-sourceusage-based
Free tierYesNo
PlatformsLinux, macOS, Windows, WebAssemblyWeb
CategoryDatabasesAutomation Integration
Founded20192013

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 Lytics

  • Data collection
  • Audience segmentation
  • Predictive analytics
  • Personalization
  • Real-time activation
  • Analytics
  • API access
  • 100+ integrations

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

Lytics

  • Building unified customer profiles from behavioural and marketing datanot DuckDB
  • Segmenting audiences and syncing them to marketing destinationsnot 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.

Lytics

  • Billed in credits where one credit is an update to a user profile, so the bill tracks how often profiles change rather than how many exist
  • Most inbound events consume a full credit each, and Cloud Connect sync events consume half a credit per updated row
  • The free Developer tier is capped at 2M monthly credits and 10 domains
  • The Growth plan is $500 a month for 5M credits, with additional credits at $500 per 10M
  • Enterprise begins above 10M credits and is quoted rather than published

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Lytics

$400/month
  • Professional$400/month
    • Core CDP features
  • Advanced$1200/month
    • Advanced personalization
    • Priority support
  • Enterprise$3000/month
    • Custom solutions
    • Dedicated support

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 Lytics if

  • You need data collection.
  • You also want audience segmentation.
Lytics review

Questions people ask

Is DuckDB or Lytics better?
Neither clearly leads. DuckDB starts at Free and Lytics at $400/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Lytics?
DuckDB has a free tier; the other does not. Paid plans start at Free for DuckDB and $400/month for Lytics.
Does DuckDB or Lytics run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Lytics runs on Web.
Can I use DuckDB for free?
Yes. DuckDB has a free tier, so you can try it without paying. Lytics starts at $400/month.
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 Lytics is typically brought in for.
What can DuckDB do that Lytics cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Lytics covers Data collection, Audience segmentation, Predictive analytics, Personalization.

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.

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.

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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