Designed for Multimodal. Built for Scale.

From agents to models, from search to training, one platform for all your AI data and workloads

Tomorrow's AI is being built on LanceDB today

The AI-Native
Multimodal Lakehouse

AI thrives on more than text. It needs multimodal data. Today’s complex workloads demand more than a database. They need a new foundation built for AI at scale.

Chunking

Vector storage

Model training

Hybrid search

Embedding pipelines

Multimodal data

Ad-hoc scripts

AI Needs Better
Data Infrastructure

Data lakes only handle tabular data, search engines just work with vectors, and neither work well with multimodal data. Researchers using today's infrastructure face more complexity, higher cost, and slower progress.

A Unified Solution

LanceDB provides one place for all your AI data and workloads so your team can move fast from idea to petabyte-scale production.

Storage

Feature Engineering

Analytics

Training

The new columnar standard for multimodal data

Fast scans and random access. Large blob storage. Zero-copy fine-grained data-evolution at petabyte scale.

table.add_columns({
  "title_frame": extract_key_frame("video", 0),
  "description": img2txt("title_frame"),
  "embedding": embed("description")
})

Advanced retrieval for AI

Blazing fast hybrid search, filter, and rerank over billions of vectors. Compute-storage separation for up to 100x savings.

(table.search("flying cars", query_type="hybrid")
  .where("date > '2025-01-01'")
  .reranker("cross_encoder_tuned")
  .select(["id"]).limit(10)
  .to_pandas())

Automated feature engineering

Declarative, distributed and versioned pre-processing for faster feature experimentation and iteration cycles. Native support for LLM-as-UDF.

ds = lance.dataset("s3://bucket/path.lance")
@lance.batch_udf()
def multiply_by_two(x: pa.RecordBatch) -> pa.RecordBatch:
    return pa.RecordBatch.from_arrays(
        [pc.multiply(x["id"], 2)], ["two"]
    )
ds.add_columns(multiply_by_two)

Explore, curate, and analyze with ease

High performance SQL for multimodal data.

db.sql("SELECT decode('audio_track', 'wav') "
       "FROM table WHERE id in ('1', '5', '324')")

Optimized training pipelines

Faster dataloading, global shuffling, and integrated filters for large scale training using pytorch or JAX.

for batch in DataLoader(table.where("video_height>=720").shuffle()):
  inputs, targets = batch["description"], batch["title_frame"]
  outputs = model(inputs)
  ...

Create Your First Project

How LanceDB Works

From prototype to production.

For Developers

01

Connect to LanceDB

Get started fast with a simple install and intuitive interface.
02

Ingest Data

Grow your project to petabyte scale without worrying about infrastructure.
03

Build and Index

Streamline your workflow and focus on high-value experimentation.

Try LanceDB Cloud

For Enterprises

01

Choose Deployment Model

Unlock the value in your sales calls, decks, contracts, and more.
02

Data Lake Compatible

Keep you data private and secure. Works with your existing data lake.
03

Build and Scale

Unlock massive scalability and unmatched price-performance.

Contact Sales

Built for Enterprise Scale

Highest search QPS on a single table

Massive scalability at a fraction of the cost

Largest table under management

Enterprise-Grade Compliance

Safety and security guaranteed for your data.

SOC2 SOC2 Type II

GDPR GDPR compliant

HIPAA HIPAA compliant

Trusted By The Best

"Lance has been a significant enabler for our multimodal data workflows. Its performance and feature set offer a dramatic step up from legacy formats like WebDataset and Parquet. Using Lance has freed up considerable time and energy for our team, allowing us to iterate faster and focus more on research."

"Law firms, professional service providers, and enterprises rely on Harvey to process a large number of complex documents in a scalable and secure manner. LanceDB’s search/retrieval infrastructure has been instrumental in helping us meet those demands."

Gabriel Pereyra, Co-Founder

"Lance transformed our model training pipeline at Runway. The ability to append columns without rewriting entire datasets, combined with fast random access and multimodal support, lets us iterate on AI models faster than ever. For a company building cutting-edge generative AI, that speed of iteration is everything."

Kamil Sindil, Head of Engineering

noize

vectors

Start Your Multimodal
Transformation Today

Designed for Multimodal Data. Built for Production Scale.