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Databases · head to head

Dragonfly vs LanceDB

Dragonfly logo

Dragonfly

Databases

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

From
Free
Rated
-
Full reviewWebsite
LanceDB logo

LanceDB

Databases

Embedded retrieval library over the Apache 2.0 Lance columnar format, with proprietary Cloud and Enterprise tiers for serving at scale.

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Dragonfly has a free tier, so it costs nothing to try first.
  • Each has a real cost: Dragonfly flex tier starting at $36/month may be underpriced, requiring careful usage monitoring; LanceDB the open source build is a library with no network endpoint, authentication or tenancy model, so exposing it to more than one application means writing your own service in front of it and handing every consumer credentials to the bucket.
  • They diverge on capability: Dragonfly covers Redis API compatibility, LanceDB covers Embedded operation.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dragonfly and LanceDB actually diverge.

Attributes where Dragonfly and LanceDB differ
AttributeDragonflyLanceDB
Starting priceFreeOn request
Pricing modelUsage-based cloud pricing with flexible tiersquote
Free tierYesNo
PlatformsCloud, AWS, GCP, AzureWeb

Identical on both: 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 LanceDB

  • Embedded operation
  • Lance columnar format
  • Object storage native
  • Multimodal storage
  • Vector indexes
  • Full-text and hybrid search
  • Scalar filtering
  • Dataset versioning

What people use each for

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

Dragonfly

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

LanceDB

  • Retrieval over a dataset that includes images, audio or video, where keeping the embeddings and the source media in one format avoids a second storage systemnot Dragonfly
  • A training and retrieval pipeline that must read the same rows for both purposes without maintaining two copies and a sync jobnot Dragonfly
  • Prototyping search locally with the same code path that later runs against S3, with no local server to installnot Dragonfly
  • Keeping a large, mostly cold vector corpus on object storage rather than paying to hold it in memory in a conventional vector databasenot 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

LanceDB

  • The open source build is a library with no network endpoint, authentication or tenancy model, so exposing it to more than one application means writing your own service in front of it and handing every consumer credentials to the bucket.
  • Queries that miss the cache pay object storage round trips, so interactive latency depends on local SSD caching or the Enterprise serving tier rather than on the library itself.
  • Concurrent writers to the same dataset coordinate through commits on the object store, so multi-writer setups can conflict and the safe pattern is a single writer per table, which is an architectural constraint on your ingest design.
  • Newly written rows are not in the index until the index is rebuilt or updated, and until then they are searched by brute force, so recall and latency drift between reindexing jobs that you have to schedule and pay for.
  • The capabilities that make it operable at scale, distributed index building, managed caching and hosted serving, live in the proprietary Cloud and Enterprise tiers, so the open licence protects the data but not the production deployment.

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

LanceDB

On request

No published plan breakdown. See the LanceDB 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 LanceDB if

  • You need embedded operation.
  • You also want lance columnar format.
LanceDB review

Questions people ask

Is Dragonfly or LanceDB better?
Neither clearly leads. Dragonfly starts at Free and LanceDB at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dragonfly or LanceDB?
Dragonfly has a free tier; the other does not. Paid plans start at Free for Dragonfly and On request for LanceDB.
Does Dragonfly or LanceDB run on more platforms?
Dragonfly runs on Cloud, AWS, GCP, Azure. LanceDB runs on Web.
Can I use Dragonfly for free?
Yes. Dragonfly has a free tier, so you can try it without paying. LanceDB starts at On request.
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 LanceDB is typically brought in for.
What can Dragonfly do that LanceDB cannot?
Dragonfly covers Redis API compatibility, Thread-per-core architecture, High-performance caching, Memory efficiency. LanceDB covers Embedded operation, Lance columnar format, Object storage native, Multimodal storage.

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
LanceDB: Is LanceDB open source?

The LanceDB library and the underlying Lance format are Apache 2.0. LanceDB Cloud and LanceDB Enterprise are proprietary managed products built on top of them.

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
LanceDB: Do I need the managed service?

Not for development or for embedded use in a single application. You typically need it when many clients must query concurrently with predictable latency, or when index builds outgrow one machine.

LanceDB: Can other tools read my data?

Yes. Lance datasets are readable from DuckDB, Polars, Pandas, PyArrow and PyTorch, which is the main practical difference from a vector database that owns its own storage.

LanceDB: How does it compare to pgvector?

pgvector keeps vectors next to relational data in a database you already run. LanceDB keeps them in object storage in a format built for random access and multimodal payloads, and scales storage independently of any server.

LanceDB: What happens to updates and deletes?

Writes append new fragments and mark old rows deleted, with compaction reclaiming space later, so a workload with heavy in-place updates accumulates overhead until compaction runs.

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