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Databases · head to head

Dremio vs LanceDB

Dremio logo

Dremio

Databases

SQL query engine and lakehouse layer over Iceberg tables in object storage

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 Dremio has a free tier, so it costs nothing to try first.
  • Each has a real cost: Dremio reflections consume compute and storage to build and refresh continuously, so a team that enables them widely discovers that background maintenance rather than user queries drives the DCU bill.; 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: Dremio covers Arrow-based execution, LanceDB covers Embedded operation.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Dremio and LanceDB actually diverge.

Attributes where Dremio and LanceDB differ
AttributeDremioLanceDB
Starting priceFreeOn request
Pricing modelPer Dremio Compute Unit consumedquote
Free tierYesNo
PlatformsLinux, Kubernetes, Cloud, DockerWeb

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 Dremio

  • Arrow-based execution
  • Reflections
  • Semantic layer
  • Iceberg catalogue
  • Federated queries
  • Autonomous management
  • Fine-grained access control
  • BI connectors

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.

Dremio

  • A company with petabytes of Parquet in S3 that wants BI dashboards without duplicating it into a warehousenot LanceDB
  • A data platform team standardising on Apache Iceberg and needing a SQL engine plus catalogue that does not lock the tables innot LanceDB
  • An analytics group accelerating slow lake queries with Reflections instead of hand-built aggregate tablesnot LanceDB
  • A regulated enterprise that must keep data on premises but wants a modern lakehouse SQL layernot 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 Dremio
  • A training and retrieval pipeline that must read the same rows for both purposes without maintaining two copies and a sync jobnot Dremio
  • Prototyping search locally with the same code path that later runs against S3, with no local server to installnot Dremio
  • Keeping a large, mostly cold vector corpus on object storage rather than paying to hold it in memory in a conventional vector databasenot Dremio

Where each one falls short

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

Dremio

  • Reflections consume compute and storage to build and refresh continuously, so a team that enables them widely discovers that background maintenance rather than user queries drives the DCU bill.
  • Self-managing Dremio on Kubernetes requires real platform engineering capacity for tuning executors, memory and coordinator sizing, and it is not comparable in effort to running a managed warehouse.
  • The Community Edition lacks the security and governance features most enterprises require, so the free tier is a trial path rather than a viable production option for regulated buyers.
  • Dremio Cloud is AWS-first, which leaves Azure and Google Cloud customers on the self-managed path with the operational burden that entails.
  • Query performance without Reflections on raw, poorly laid out files is often unremarkable, so the promise of querying the lake as is depends on file layout work you still have to do.

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

Dremio

Free
  • Community EditionFree
    • Self-managed on your own hardware
    • SQL engine and semantic layer
    • No vendor support
  • Dremio Cloud$0.2/hour
    • Billed at $0.20 per Dremio Compute Unit
    • Includes query execution, Reflections and background processing
    • 400 dollar trial credit for 30 days
  • Enterprise$undefined/year
    • Self-managed on Kubernetes, on premises or any cloud
    • Enterprise security, SSO and governance
    • Vendor support with SLA

LanceDB

On request

No published plan breakdown. See the LanceDB review.

Which should you pick?

Choose Dremio if

  • You need arrow-based execution.
  • You want to start without paying.
  • You work on Linux, Kubernetes, Cloud, Docker.
  • You also want reflections.
Dremio review

Choose LanceDB if

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

Questions people ask

Is Dremio or LanceDB better?
Neither clearly leads. Dremio 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, Dremio or LanceDB?
Dremio has a free tier; the other does not. Paid plans start at Free for Dremio and On request for LanceDB.
Does Dremio or LanceDB run on more platforms?
Dremio runs on Linux, Kubernetes, Cloud, Docker. LanceDB runs on Web.
Can I use Dremio for free?
Yes. Dremio has a free tier, so you can try it without paying. LanceDB starts at On request.
What is Dremio best used for?
Dremio is most often used for a company with petabytes of parquet in s3 that wants bi dashboards without duplicating it into a warehouse, a data platform team standardising on apache iceberg and needing a sql engine plus catalogue that does not lock the tables in, an analytics group accelerating slow lake queries with reflections instead of hand-built aggregate tables, a regulated enterprise that must keep data on premises but wants a modern lakehouse sql layer. Of those, a company with petabytes of parquet in s3 that wants bi dashboards without duplicating it into a warehouse and a data platform team standardising on apache iceberg and needing a sql engine plus catalogue that does not lock the tables in are not what LanceDB is typically brought in for.
What can Dremio do that LanceDB cannot?
Dremio covers Arrow-based execution, Reflections, Semantic layer, Iceberg catalogue. LanceDB covers Embedded operation, Lance columnar format, Object storage native, Multimodal storage.

Answered from the vendors’ own pages

Dremio: How is Dremio Cloud billed?

At 0.20 US dollars per Dremio Compute Unit, which counts query execution, Reflection building and platform overhead, not just user queries.

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.

Dremio: Is there a free version?

Yes, a Community Edition you self-manage, but it omits the enterprise security and governance features and comes with no support.

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.

Dremio: Does it lock in my data?

No, tables stay in Apache Iceberg or Parquet in your own object storage and can be read by Spark, Trino or other engines.

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.

Dremio: Do I still need a warehouse?

Often not for analytics, but Dremio is not a transactional store and high-concurrency operational serving is not its strength.

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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