LanceDB
The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster.
About LanceDB
The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster.
Products
Unified vector, full-text, and hybrid search with SQL filters for production-ready retrieval.
A unified platform for managing AI data from raw files to production-ready features.
Find optimal distributions, deduplicate massive datasets, and surface edge cases—all in one place.
Build and scale features with Python UDFs, automatic updates, and no table rewrites.
Train directly from curated data with up to 70% MFU and no data movement bottlenecks.
Enterprise tier of LanceDB, referenced as providing an Enterprise feature engineering platform and enterprise updates.
Cloud deployment of LanceDB, referenced in a case study where Second Dinner leveraged LanceDB Cloud for game development workflows.
What customers say
“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.”
“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.”
“With pgvector, every change meant a new migration. LanceDB lets us avoid the overhead and just point to a directory for local development. It's helped our developers build, test, and ship faster!- Pavitra Bhalla, Engineer @ Dosu”
- LanceDB Cloud
Second Dinner uses LanceDB Cloud for internal RAG tools supporting game prototypes and automated QA tests. The jointly contributed vendor-hosted story reports production readiness in two weeks and prototype cycles…
Case study - LanceDB Multimodal Lakehouse
The vendor describes Netflix's Media Data Lake as built on LanceDB and the Multimodal Lakehouse, combining media assets, metadata, and embeddings in Media Tables for interactive exploration and machine-learning training.
Case study - Case study
CodeRabbit uses LanceDB to retrieve code-review context from pull requests, issues, dependencies, and team knowledge. The vendor reports searches across hundreds of thousands of tables and query performance below 150 ms…
Case study
Security and compliance
As reported by LanceDB. Ask for audit reports before you buy.
- SOC 2 Type IICompliant
- HIPAACompliant
SubprocessorsCodeRabbit
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