Databases · head to head
LanceDB vs QuestDB
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
- -
QuestDB
Databases
Fast open source time-series database for high throughput ingestion
- From
- Free
- Rated
- -
The short version
- Only QuestDB has a free tier, so it costs nothing to try first.
- Each has a real cost: 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.; QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features
- They diverge on capability: LanceDB covers Embedded operation, QuestDB covers High Throughput Ingestion.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which LanceDB and QuestDB actually diverge.
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 LanceDB
- Embedded operation
- Lance columnar format
- Object storage native
- Multimodal storage
- Vector indexes
- Full-text and hybrid search
- Scalar filtering
- Dataset versioning
Only in QuestDB
- High Throughput Ingestion
- SQL Support
- Time-series Optimization
- SIMD Vectorization
- Column-oriented Storage
- Built-in Web Console
- InfluxDB Line Protocol
- PostgreSQL
What people use each for
The jobs each tool is most often brought in to do.
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 QuestDB
- A training and retrieval pipeline that must read the same rows for both purposes without maintaining two copies and a sync jobnot QuestDB
- Prototyping search locally with the same code path that later runs against S3, with no local server to installnot QuestDB
- Keeping a large, mostly cold vector corpus on object storage rather than paying to hold it in memory in a conventional vector databasenot QuestDB
QuestDB
- Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot LanceDB
- Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot LanceDB
- Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not LanceDB
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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.
QuestDB
- Open-source edition lacks high-availability, distributed architecture, and enterprise security features
- Enterprise edition pricing not published; requires contacting sales for custom quote
- Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements
Pricing, plan by plan
LanceDB
On requestNo published plan breakdown. See the LanceDB review.
QuestDB
FreeNo published plan breakdown. See the QuestDB review.
Which should you pick?
Choose QuestDB if
- You need high throughput ingestion.
- You want to start without paying.
- You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- You also want sql support.
Questions people ask
- Is LanceDB or QuestDB better?
- Neither clearly leads. LanceDB starts at On request and QuestDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, LanceDB or QuestDB?
- QuestDB has a free tier; the other does not. Paid plans start at On request for LanceDB and Free for QuestDB.
- Does LanceDB or QuestDB run on more platforms?
- LanceDB runs on Web. QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- Can I use QuestDB for free?
- Yes. QuestDB has a free tier, so you can try it without paying. LanceDB starts at On request.
- What is LanceDB best used for?
- LanceDB is most often used for 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 system, a training and retrieval pipeline that must read the same rows for both purposes without maintaining two copies and a sync job, prototyping search locally with the same code path that later runs against s3, with no local server to install, keeping a large, mostly cold vector corpus on object storage rather than paying to hold it in memory in a conventional vector database. Of those, 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 system and a training and retrieval pipeline that must read the same rows for both purposes without maintaining two copies and a sync job are not what QuestDB is typically brought in for.
- What can LanceDB do that QuestDB cannot?
- LanceDB covers Embedded operation, Lance columnar format, Object storage native, Multimodal storage. QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization.
Answered from the vendors’ own pages
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.
QuestDB: How much does QuestDB Enterprise cost?
QuestDB does not publish specific pricing for the Enterprise tier. Customers must contact QuestDB via their enterprise contact form to receive a custom quote.
SourceLanceDB: 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.
QuestDB: Does QuestDB offer a free version?
Yes, QuestDB Open Source is completely free and recommended for evaluation, prototyping, and pilot projects. Enterprise features, high availability, security, and dedicated support require the paid Enterprise tier.
SourceLanceDB: 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.
QuestDB: What deployment options does QuestDB offer?
QuestDB offers open source deployment, Enterprise deployment, and Bring Your Own Cloud (BYOC) deployment. Pricing details for BYOC and Enterprise tiers are not published and require direct contact with sales.
SourceLanceDB: 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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