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Databases · head to head

Dragonfly vs DuckDB

Dragonfly logo

Dragonfly

Databases

High-performance Redis-compatible in-memory datastore with 25x better throughput

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Dragonfly flex tier starting at $36/month may be underpriced, requiring careful usage monitoring; 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.
  • They diverge on capability: Dragonfly covers Redis API compatibility, DuckDB covers In-process execution.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dragonfly and DuckDB actually diverge.

Attributes where Dragonfly and DuckDB differ
AttributeDragonflyDuckDB
Pricing modelUsage-based cloud pricing with flexible tiersopen-source
PlatformsCloud, AWS, GCP, AzureLinux, macOS, Windows, WebAssembly
FoundedUnknown2019

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 Dragonfly

  • Redis API compatibility
  • Thread-per-core architecture
  • High-performance caching
  • Memory efficiency
  • Real-time leaderboards
  • Message queue support
  • ML feature serving
  • Cloud deployment

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

What people use each for

The jobs each tool is most often brought in to do.

Dragonfly

  • High-throughput caching for web applicationsnot DuckDB
  • Real-time leaderboards and rankingsnot DuckDB
  • Message queue and event processingnot DuckDB
  • ML model feature serving at millisecond latenciesnot DuckDB
  • Gaming session state and player data storagenot DuckDB

DuckDB

  • Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot Dragonfly
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot Dragonfly
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot Dragonfly
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot Dragonfly

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Dragonfly

  • Flex tier starting at $36/month may be underpriced, requiring careful usage monitoring
  • Business tier $2,000/month represents significant jump in cost
  • Limited to in-memory storage, not suitable for cold data or archival
  • Bring-your-own-cloud requirement on Business tier adds operational complexity
  • Cloud availability dependent on AWS/GCP/Azure uptime

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.

Pricing, plan by plan

Dragonfly

Free
  • Free TierFree
    • 100 cloud credits for new signups
    • Equivalent to free trial
  • Business$2000/month
    • Starting price for enterprise offering
    • Bring-your-own-cloud deployment
    • Auto-scaling with custom SLAs
  • Enterprise$undefined/custom
    • Custom pricing
    • Any-cloud deployment
    • Custom instances and sizing

DuckDB

Free

No published plan breakdown. See the DuckDB review.

Which should you pick?

Choose Dragonfly if

  • You need redis api compatibility.
  • You want to start without paying.
  • You work on Cloud, AWS, GCP, Azure.
  • You also want thread-per-core architecture.
Dragonfly review

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

Questions people ask

Is Dragonfly or DuckDB better?
Neither clearly leads. Dragonfly starts at Free and DuckDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dragonfly or DuckDB?
Dragonfly starts at Free and DuckDB at Free.
Does Dragonfly or DuckDB run on more platforms?
Dragonfly runs on Cloud, AWS, GCP, Azure. DuckDB runs on Linux, macOS, Windows, WebAssembly.
Can I use Dragonfly for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dragonfly best used for?
Dragonfly is most often used for high-throughput caching for web applications, real-time leaderboards and rankings, message queue and event processing, ml model feature serving at millisecond latencies. Of those, high-throughput caching for web applications and real-time leaderboards and rankings are not what DuckDB is typically brought in for.
What can Dragonfly do that DuckDB cannot?
Dragonfly covers Redis API compatibility, Thread-per-core architecture, High-performance caching, Memory efficiency. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.

Answered from the vendors’ own pages

Dragonfly: How much faster is Dragonfly than Redis?

Dragonfly achieves 3.97M queries per second compared to Redis's 718K QPS, representing a 25x improvement. Memory efficiency is also 30% better.

Source
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.

Dragonfly: Can I migrate from Redis to Dragonfly without code changes?

Yes. Dragonfly maintains full API compatibility with Redis and Memcached, allowing drop-in replacement with minimal to no code modifications.

Source
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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