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You’re Ready. Is Your Data?

Enterprises hold vast amounts of untapped data—images, transcripts, contracts, and more—with huge AI potential. But outdated infrastructure forces teams to patch together storage and search tools, slowing progress and driving up costs.

LanceDB solves this with a unified system for all your multimodal data, built for AI workloads. It accelerates deployment and scales effortlessly with your needs.

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Why Multimodal Data Needs a Better Lakehouse? — Download the Research Study.

Designed for Multimodal. Built for Scale.

AI thrives on more than text; it needs sight, voice, and vectors too. New data and workloads need more than just another database. They need a new foundation for AI data.

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Tomorrow's AI is built on LanceDB today

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The AI-Native Multimodal Lakehouse

From agents to models, from search to training, LanceDB is a unified data platform designed for multimodal data and built for enterprise scale.

Ad-hoc scripts
Chunking
Vector storage
Model training

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.

Hybrid search
Embedding pipelines
Multimodal data

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

Search

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 at petabyte-scale. 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 processing for faster feature engineering iterations and experimentation. Native support for UDFs.

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')")

Optimize 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 It Works

From prototype to production

For Developers
01

Connect to LanceDB

Get started in seconds with a simple install and intuitive interface

02

Ingest Data

Grow your project to petabyte scale without worrying about infrastructure

03

Ship, Rinse, and Repeat

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

20,000
+

Highest search QPS on a single table

100
x

Massive scalability at a fraction of the cost

20
PB

Largest table under management

Enterprise-Grade Compliance

Safety and security guaranteed for your data, every time.

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."

Fei-fei Li
,
Co-founder / CEO

"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

Official LanceDB Blog

4
 min read, 
June 23, 2025
Newsletter

What is the LanceDB Multimodal Lakehouse?

4
 min read, 
March 25, 2024
Engineering

Designing a Table Format for ML Workloads

4
 min read, 
April 10, 2025
Engineering

A Practical Guide to Training Custom Rerankers

Go to blog

Start Your Multimodal
Transformation Today

Designed for Multimodal Data. Built for Production Scale.

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