LanceDB
by LanceDB
Open-source multimodal lakehouse and vector database built on the Lance columnar format
LanceDB is an open-source, embedded retrieval library and multimodal lakehouse built on the Lance columnar format. It lets AI and data teams store vectors, raw images, video and metadata in one table on object storage, then run vector, full-text and SQL search without operating a separate database cluster.
LanceDB is the company and open-source project behind Lance, a columnar data format designed for AI workloads that need fast random access to individual rows as well as large scans, and LanceDB, an embedded retrieval library built on top of it. Because the library runs in-process and persists tables directly to local disk or to object storage such as Amazon S3, Google Cloud Storage or Azure Blob, teams can add vector search to an application without standing up a server, and every write creates a new table version with zero-copy semantics. It supports vector similarity, full-text and hybrid search with SQL filters, GPU-accelerated index builds, and multimodal columns holding text, images, video and point clouds, with SDKs for Python, TypeScript and Rust plus a REST API. The Apache-2.0 GitHub repository had about 11.5k stars and 1.1k forks when checked in September 2026. Founded in 2022 by Chang She, a co-creator of pandas, and Lei Xu, and part of Y Combinator's Winter 2022 batch, the San Francisco company raised a $30M Series A led by Theory Ventures in June 2025 and used it to reposition as a 'Multimodal Lakehouse': LanceDB Enterprise adds data curation, feature engineering with Python UDFs, search and training-data loading on the same tables. Commercially it sells LanceDB Cloud, a managed serverless service, and LanceDB Enterprise, deployed either as a private managed service or bring-your-own-cloud in the customer's AWS, GCP or Azure account. The vendor lists Netflix, Runway, Character.AI, ByteDance, CodeRabbit and CrewAI among production users. It competes with Pinecone, Qdrant, Milvus and Chroma, differentiating on storage-first economics and native multimodal data rather than an always-on cluster.
ML platform and data engineering leads who manage large multimodal datasets (images, video, embeddings) on object storage and want retrieval and training data to live in one format.
Vector, full-text and SQL retrieval directly over data in object storage, without running and paying for a separate always-on vector database cluster.
At a Glance
- Category
- Infrastructure & Cloud
- Pricing
- Free, Usage-based, Contact for pricing
- Target Market
- CTOs, Data Scientists, Enterprise Developers, ML Engineers
- Deployment
- Open-source, Self-hosted, API-based, Multi-cloud
- Founded
- 2022
- Headquarters
- San Francisco, USA
- Team Size
- 11-50
Key Features
- ✓Lance columnar format
An open columnar format built for AI that supports both fast random row access and scans, so one copy of data serves search and training.
- ✓Embedded, serverless library
Runs in-process from Python, TypeScript or Rust and persists to local disk or object storage, removing the need for a separate database server.
- ✓Hybrid search with SQL filters
Combines vector similarity, full-text search and SQL filtering in one query, improving retrieval relevance for RAG and search applications.
- ✓Automatic zero-copy versioning
Every write creates a new table version without copying data, enabling rollback, reproducible experiments and reads of earlier versions.
- ✓GPU-accelerated indexing
Builds vector indexes on GPUs to cut index-creation time on very large tables holding hundreds of millions or billions of vectors.
- ✓Multimodal lakehouse (Enterprise)
Adds curation, feature engineering with Python UDFs and training-data loading on the same tables, avoiding copies between separate data systems.
- ✓Data ecosystem integrations
Works with Apache Arrow, pandas, Polars and DuckDB, and plugs into LangChain and LlamaIndex so existing RAG pipelines can adopt it quickly.
Capabilities
Use Cases
- •RAG over enterprise documents
Store chunked documents, embeddings and metadata in one table on S3 and serve hybrid vector plus keyword retrieval to an LLM application.
- •Training-data curation
ML teams deduplicate, filter and sample large image or video datasets, then load the curated table directly into model training jobs.
- •Agent memory
Agent frameworks persist long-term memory as local LanceDB tables, giving semantic recall without deploying or maintaining a separate database service.
- •Cutting vector-database cost at scale
Move a cluster-based vector store onto object storage; one engineer reported monthly cost falling from $30,000 to $7,000 after migrating 700M vectors off Milvus.
Ideal For
Best For
- ✓Embedding vector search inside Python or TypeScript applications without running a database server
- ✓Retrieval-augmented generation over large document or code corpora stored on S3, GCS or Azure Blob
- ✓Curating and deduplicating multimodal training datasets such as images, video and point clouds
- ✓Local-first long-term memory for AI agents with file-based persistence
- ✓Cost-sensitive search at hundreds of millions of vectors where object-storage economics matter
Not Ideal For
- ✗Teams that want a hands-off managed vector service with published self-serve pricing: LanceDB Cloud has no public rate card, and Enterprise on AWS Marketplace starts from a $60,000 annual commit.
- ✗Update-heavy workloads run on the open-source library by teams without ops capacity: practitioners report having to schedule compaction, old-version cleanup and index maintenance themselves to keep disk use and query speed under control.
Integrations
Deployment
Market Analysis
Pros
- ✓Free Apache-2.0 core with an active open-source community (about 11.5k GitHub stars)
- ✓Object-storage architecture can sharply cut cost at scale; one practitioner reported cutting monthly spend from $30,000 to $7,000 after moving 700M vectors off Milvus
- ✓One table format for vectors, raw multimodal data and metadata, queryable with SQL through the DuckDB integration
- ✓Enterprise tier carries SOC 2 Type II, HIPAA and GDPR compliance, with BYOC deployment for data residency
Cons
- ✗Operational overhead in the open-source library: a production write-up reported table versioning growing disk use into terabytes until hourly optimize() calls and cron cleanup jobs were added
- ✗The same report found connections and tables piling up under a long-running API server until a context-manager or singleton pattern was adopted
- ✗Building one IVF-PQ index over 700M vectors exhausted RAM on a 128GB machine and had to be split into 50M-row batches
- ✗No public pricing for LanceDB Cloud; the Enterprise AWS Marketplace listing is a $60,000, 12-month, non-refundable commit
Pricing
Open Source
$0
- ✓Apache-2.0 LanceDB library and Lance format
- ✓Self-managed on local disk or object storage
- ✓Community support via GitHub
LanceDB Cloud
Contact for pricing
- ✓Managed serverless vector search and retrieval
- ✓No public rate card; the pricing page is a contact form
LanceDB Enterprise (AWS Marketplace annual commit)
From $60,000/yr
- ✓12-month commitment, private offers available
- ✓Usage metered in LanceDB units at $0.01 each
- ✓SOC 2 Type II, HIPAA and GDPR
- ✓Managed private deployment or BYOC on AWS, GCP or Azure
The open-source library is free under Apache-2.0; you pay only for your own storage and compute. LanceDB Cloud has no public rate card, since its pricing page is a sales contact form. LanceDB Enterprise is listed on AWS Marketplace as a 12-month annual commit of $60,000 with usage metered in LanceDB units at $0.01 each, non-refundable, with private offers for custom terms.
Security & Compliance
Connect
Sources
This page was written from 8 sources, 6 on domains other than lancedb.com.
- 1.lancedb.com — lancedb.comvendor
- 2.github.com — lancedb
- 3.docs.lancedb.com — deployment
- 4.lancedb.com — series a fundingvendor
- 5.aws.amazon.com — prodview 7iy473li6ul34
- 6.technews180.com — lancedb raises 30m to build the future of ai data systems
- 7.ycombinator.com — lancedb
- 8.sprytnyk.dev — running lancedb in production
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