LanceDB pricing, free plan and limits
Open-source embedded retrieval library with a distributed Enterprise lakehouse on the same Lance format; its GitHub repository shows 11.9k stars.
- Best for
- ML or AI engineer building retrieval inside an application
- Not for
- Team that wants a managed vector database with a public price list
Should you use LanceDB?
LanceDB suits engineers who want free in-process vector, full-text and hybrid search, and data teams that outgrow one machine and move to Enterprise on the same format. It has no public price list, so budget a sales call before relying on Enterprise. Its $30 million Series A closed in June 2025.
Keeps vectors, metadata and raw multimodal data in one versioned Lance table that both the free embedded library and the distributed Enterprise service read.
A shared open format makes moving between the two editions cheap but ties the data to Lance, while rivals such as Qdrant and Chroma compete on retrieval alone.
Also for
- Backend engineer who wants vector search without a separate service
- Data engineer curating multimodal training sets
Show 4 more
- ML engineers building retrieval pipelines who want search inside the application process
- Backend developers embedding vector search without running a separate database service
- Data and training teams curating multimodal datasets before fine-tuning or pretraining
- Platform teams that need SOC 2 or HIPAA-covered retrieval under an Enterprise contract
Also not for
- Non-technical team needing a no-code search interface
Main trade-off
Enterprise has no public price list, and Embedded is limited to a single host.
LanceDB pricing
| Plan | Monthly price | What it includes |
|---|---|---|
| Embedded | Free | Free, Apache 2.0 open source, Runs inside your application on local disk or object storage, Python, TypeScript and Rust SDKs, Vector, full-text and hybrid search with filtering and reranking, Versioned tables, Manual indexing and compaction |
| Enterprise | Custom | Distributed search, indexing and maintenance, Private cloud or bring-your-own-cloud deployment, Distributed NVMe data cache, SQL over Arrow Flight SQL, joins and near-duplicate detection, Computed columns and materialized views, Dedicated support |
LanceDB Embedded costs nothing: the code is open source under Apache 2.0 and the docs list no account, seat or usage fee. LanceDB Enterprise has no public price. The pricing page redirects to a contact form, and the docs send teams to the sales team for a cluster. Cost is therefore known only after a conversation with sales.
Before you commit
- Embedded leaves indexing, compaction and scaling to your application and your team, per the docs
- Enterprise has no public price, so cost is known only after a sales conversation
What LanceDB does
LanceDB pairs an embedded retrieval library with a distributed multimodal lakehouse on the Lance table format. Embedded runs inside an application in Python, TypeScript or Rust; Enterprise is a cluster you deploy privately or in your own cloud account. Pick Embedded for free in-process search, and ask sales before planning around Enterprise.
Embedded retrieval library
Runs in-process with Python, TypeScript and Rust SDKs, reading a local disk or a bucket, and installs with one pip command.
One versioned multimodal table
Keeps vectors, metadata and raw data such as images in a single Lance table, and table versions let a training run be traced to the exact data it used.
Hybrid search in the free edition
Hybrid search, full-text search and filtering sit in the free Embedded edition as well as Enterprise, per the feature comparison table.
Distributed Enterprise cluster
Spreads query execution, indexing and maintenance across a cluster with an NVMe data cache, deployable by LanceDB or in your own cloud.
Show all 8 features
Curation over SQL
Enterprise adds SQL through Arrow Flight SQL, joins, statistical sampling and near-duplicate detection for building training sets, none of which Embedded offers.
Feature engineering jobs
Computed columns and materialized views refresh as background jobs on the Enterprise cluster; Embedded has no computed columns.
Training data loading
A StreamingDataset feeds multimodal data into PyTorch with globally shuffled, deterministic batches, and the PyTorch loader is in both editions.
Documented integrations
The docs cover LangChain, LlamaIndex, DuckDB, Pandas and PyArrow, Polars and the Hugging Face Hub.
Works with
- LangChain
- LlamaIndex
- DuckDB
- Pandas and PyArrow
- Polars
- Hugging Face Hub
Capabilities
- Vector database
- Multimodal lakehouse
- Embedded database
- Open source
- Retrieval
- Training data
Strengths and limits
Strengths
- Embedded is free and open source, needs no account or server, and is one pip command away, per LanceDB's docs.
- Embedded and Enterprise share the Lance format, so moving to a cluster later needs no data conversion step, according to the Embedded versus Enterprise page.
- Full-text and hybrid search with filtering are in the free edition rather than reserved for the paid one.
- Compliance is documented: SOC 2 Type 2, HIPAA and GDPR appear on the trust center, and Enterprise keeps customer data in the customer's own account, per the docs.
- G2 shows 4.2 out of 5 from 22 reviews, with 59% of the star ratings at four stars and 40% at five.
Limits
- There is no public price list: lancedb.com/pricing redirects to the contact page, so Enterprise cost cannot be estimated without talking to sales.
- Embedded runs on a single host and leaves indexing and compaction to your application, and joins, near-duplicate detection and computed columns are Enterprise-only, per the docs.
- LanceDB's homepage and docs now present a multimodal lakehouse for curation and training, a broader pitch than a team wanting only a small vector store needs.
- G2 lists 22 reviews, a thin base for judging reliability or support quality.
LanceDB alternatives
The nearest AI tools on Hokai for the same job.
| Tool | Price from | Free plan | Runs on | Rating |
|---|---|---|---|---|
| LanceDB This page | -- | Yes | Web | 3.6 |
 ExaSearch API that ranks the web by meaning for AI agents and retrieval pipelines, with Lite, Auto and Deep search modes, an asynchronous research Agent and an official MCP server. A free Starter plan with monthly credits needs no payment method. | $49/mo | Yes | Web | 4.0 |
| AlgoliaAI search and retrieval platform powering 1.75 trillion searches annually for 18,000+ businesses with semantic search, vector embeddings, and AI agents. | -- | Yes | Web · iOS · Android · Mac · Windows | 4.0 |
 ReductoAgentic document platform that parses, extracts and splits PDFs, scans and spreadsheets for AI teams; its About page cites more than 5 billion pages processed. | -- | Yes | Web | 3.9 |
LanceDB facts
- Training-data policy
- The docs say Enterprise customer data stays within the customer's account and LanceDB receives only telemetry for system health; they state no model-training policy (LanceDB Enterprise compliance docs, read 12 November 2026).
- Compliance
- SOC 2 Type II · HIPAA · GDPR (LanceDB trust center and docs)
- Works offline
- Yes
- Minimum skill level
- Advanced
- Price band
- Mid-range
- Total funding
- $30M Series A (June 2025) led by Theory Ventures, with CRV, Y Combinator, Databricks Ventures, RunwayML, Zero Prime and Swift; the company does not state a total raised
Data and privacy
Apache 2.0 (Embedded); proprietary LanceDB Enterprise
Questions about LanceDB
How much does LanceDB cost in 2026?
LanceDB Embedded is free and open source, and a single pip command installs it. LanceDB Enterprise has no public price. On 12 November 2026 lancedb.com/pricing redirected to the contact page, and the docs tell teams to reach out to set up a cluster.
Is LanceDB free to use?
Yes, the Embedded edition is. It needs no account or server, runs inside your application and keeps its code on GitHub. Enterprise is the paid, sales-led edition that adds a distributed cluster, SQL and feature engineering jobs.
What is the difference between LanceDB Embedded and Enterprise?
Embedded runs on one host inside your application and leaves indexing and compaction to you. Enterprise spreads queries, indexing and refresh jobs across a cluster, adds an NVMe cache, SQL, joins and near-duplicate detection, and can be deployed privately or in a cloud account you own. Both read the same Lance format, so data needs no conversion between them.
Is LanceDB SOC 2 and HIPAA compliant?
LanceDB's trust center lists SOC 2 Type 2, HIPAA and GDPR, and its docs say Enterprise maintains all three. The docs add that customer data remains inside the customer's own account and that LanceDB receives only telemetry for monitoring system health. Reports sit behind a trust center access request.
What are the main alternatives to LanceDB?
The usual comparisons are Chroma, Qdrant and Pinecone. Chroma's homepage calls it open-source search infrastructure on object storage, Qdrant offers hosted, hybrid and beta edge products, and Pinecone sells four named plans, from Starter to Enterprise. LanceDB differs by storing multimodal data in a Lance table that its free library and Enterprise both read.
About LanceDB
LanceDB is an open-source retrieval library and a commercial multimodal lakehouse built on one open table format, Lance. Its docs list two editions: Embedded, which runs inside your application process, and Enterprise, a distributed service deployed privately or inside a cloud account you own. Co-founder Chang She announced a $30 million Series A led by Theory Ventures on 24 June 2025, with CRV, Y Combinator, Databricks Ventures, RunwayML, Zero Prime and Swift taking part, and wrote that he and his co-founder Lei began four years earlier by asking why embeddings, images and video were so much harder to work with than tabular data. The homepage now leads with "Train on the right data faster", so the company sells to teams that curate and train on multimodal data as well as to developers adding vector search.
Read the full background
Embedded is free and Apache 2.0 licensed, and per its docs one pip install lancedb command sets it up with no account or server; the docs show Embedded 0.41.0 and Enterprise 1.12.0 as the current releases on 12 November 2026. It reads and writes Lance tables on local disk or object storage with Python, TypeScript and Rust SDKs, keeps vectors, metadata and raw files such as images in one versioned table, and supports hybrid, full-text and vector queries, filters and reranking. Operations stay with you: Embedded runs on one host, and indexing and compaction are manual. Enterprise adds platform-managed indexing and maintenance, an NVMe cache for hot data, Arrow Flight SQL access, joins, near-duplicate detection, computed columns and materialized views, per LanceDB's Embedded versus Enterprise page. On 12 November 2026 we installed version 0.41.0 with pip in a fresh virtual environment, with no account, built a four-row table in a temporary folder and ran two searches: a vector query returned the two closest rows, and the same query filtered to one kind returned the single matching row, with all data in one demo.lance folder.
It fits machine learning and backend engineers who want retrieval inside an application, and data teams curating multimodal sets for training. The Series A post says Enterprise runs at Runway, at Midjourney and at Character.AI, searching tens of billions of vectors. The customers page links a case study from CodeRabbit, another from Netflix and one from Dosu, and an engineering post from Exa on its petabyte-scale use of Lance. The homepage lists 130k queries per second and petabyte-to-exabyte scale without naming a workload, so treat it as a ceiling claim. For other retrieval tooling see AI search and retrieval infrastructure and our retrieval infrastructure guide.
Who should look elsewhere: a team that wants a managed vector database with a public price list. LanceDB publishes none: lancedb.com/pricing now redirects to its contact page, its docs describe only Embedded and Enterprise, and the old Cloud documentation address returned a 404 on 12 November 2026. The market's closest rivals are Chroma, which its homepage calls open-source search infrastructure under Apache 2.0, built on object storage; Qdrant, which sells a managed cloud, a hybrid cloud and a beta edge product; and Pinecone, whose pricing page lists Starter, Builder, Standard and Enterprise plans. None of the three has a Hokai record yet.
Sources and guides
Official sources
- LanceDB official site Vendor
- LanceDB documentation Docs
- lancedb/lancedb on GitHub Docs
- LanceDB trust center Docs
- GitHub repository GitHub
- Discord Community
Product facts on this page (pricing, plans, integrations, compliance) are read from LanceDB's own pages and documentation. The verdict is Hokai's reading of them, not the vendor's claim.
Hokai guides that mention LanceDB
Lists that include LanceDB
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