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Databases · head to head

DuckDB vs Memcached

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
M

Memcached

Databases

Distributed memory object caching system

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.; Memcached no persistence at all: restart a node and its cache is gone, which every design must assume
  • They diverge on capability: DuckDB covers In-process execution, Memcached covers In-memory key-value cache.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which DuckDB and Memcached actually diverge.

Attributes where DuckDB and Memcached differ
AttributeDuckDBMemcached
Pricing modelopen-sourceOpen source, no licence fee; managed cloud billed separately
PlatformsLinux, macOS, Windows, WebAssemblyLinux, macOS, Windows, Docker, Self-hosted
Founded2019Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

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 Memcached

  • In-memory key-value cache
  • Multithreaded
  • Client-side sharding
  • Predictable memory use

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

Memcached

  • Caching expensive database query results to cut loadnot DuckDB
  • Session storage where losing sessions on restart is acceptablenot DuckDB
  • Fronting an API whose responses are costly and change slowlynot 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.

Memcached

  • No persistence at all: restart a node and its cache is gone, which every design must assume
  • No replication or failover, so losing a node loses that share of the cache
  • Only simple key-value, with none of the lists, sorted sets or streams Redis offers
  • Values are capped at 1MB by default, which surprises teams caching large documents

Pricing, plan by plan

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Memcached

Free
  • MemcachedFree
    • Full functionality
    • Self-hosted
    • No usage limits

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 Memcached if

  • You need in-memory key-value cache.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Self-hosted.
  • You also want multithreaded.
Memcached review

Questions people ask

Is DuckDB or Memcached better?
Neither clearly leads. DuckDB starts at Free and Memcached at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DuckDB or Memcached?
DuckDB starts at Free and Memcached at Free.
Does DuckDB or Memcached run on more platforms?
DuckDB runs on Linux, macOS, Windows, WebAssembly. Memcached runs on Linux, macOS, Windows, Docker, Self-hosted.
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 Memcached is typically brought in for.
What can DuckDB do that Memcached cannot?
DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. Memcached covers In-memory key-value cache, Multithreaded, Client-side sharding, Predictable memory use.

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.

Memcached: Is Memcached free?

Yes, open source with no licence fee.

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.

Memcached: Memcached or Redis?

Memcached is a pure cache: simpler, multithreaded and very predictable. Redis adds persistence, replication and rich data structures, which is why it is the default choice unless you specifically want a cache and nothing more.

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.

Memcached: Does Memcached persist data?

No. Everything is in memory and lost on restart, by design.

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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