Train on the right data faster.
Search, curate, and train at petabyte scale.
One table, all your multimodal data.
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Accelerate the Data Flywheel for Model Research
One table for your AI data, with isolated dataset branches for agent-driven experimentation. Find the failure, fix the data, retrain.
Find a curated training subset for every failure case
Start from one failed grasp or eval, search multimodal data, metadata, and embeddings together, and retrieve all similar examples to build a targeted training subset.
Data sandboxes for safe agentic experimentation
Don’t let an agent silently corrupt your data and ruin your training run. Give each agent its own zero-copy branch to experiment and review changes.
A single dataset for every modality
Keep episodes, frames, metadata, features, and embeddings together, without pipelines to maintain. Get clear lineage to trace every feature and training run back to its source.
More hill climbing, less babysitting preprocessing jobs
Run feature extraction, embedding generation, and backfills without debugging job configs or tuning clusters. Define the transformations your experiment needs, and LanceDB handles the distributed execution.
Higher GPU utilization,
faster convergence
faster convergence
Keep GPUs fed. Use global shuffles for faster convergence, stream multimodal training data directly into GPUs, cached across clouds.
Iterate on your training dataset, not your Backfill Pipeline
Curate, enrich, search, and prepare training data at dataset scale without stitching together pipelines, maintaining indexes, or leaving GPUs waiting on data.
Curation
Turn messy multimodal datasets into training-ready sets. Find failure modes and edge cases, remove duplicates, and balance the data you need. Define the result as a materialized view that LanceDB backfills, indexes, and keeps current so you can improve model quality and reach convergence faster.
Feature Engineering
Write a Python UDF; LanceDB runs it across a distributed cluster and writes the result back as a new column. Backfill existing rows with checkpointing, automatically process new rows as they arrive, and add embeddings without rewriting the table.
Built for
petabyte
exabytescaleProduction-proven infrastructure powering the world’s most demanding AI training workloads.
130k
QPS
50b
Rows
in a single table
14
min
Backfill
1B rows
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.
LanceDB transformed how we handle context at scale. While other vector databases hit cost and performance walls, LanceDB scales effortlessly with our growth—from startup to enterprise. Its multimodal capabilities and deployment flexibility were game-changers, enabling us to deliver the depth of analysis our customers expect while maintaining sub-second response times across millions of code reviews.
Midjourney generates breathtaking imagery for millions of users worldwide. Vector search is critical infrastructure that allows us to better serve our users. We evaluated multiple solutions and LanceDB was the only one that could meet the high-traffic and large scale requirements we had. We couldn't be happier with our decision.
