Build Features. Not Pipelines.
At 100TB+, feature engineering isn’t about the feature anymore. It’s an infrastructure problem.
You manage clusters, debug GPU jobs, and rewrite code just to get features to run.
LanceDB runs your Python UDFs at petabyte scale. You write the function. We handle the rest.
You manage clusters, debug GPU jobs, and rewrite code just to get features to run.
LanceDB runs your Python UDFs at petabyte scale. You write the function. We handle the rest.
Write Python, Run at Scale.
Define your feature as a Python function and run it unchanged from a notebook to production.
Declare GPU and memory requirements once. LanceDB provisions compute, handles orchestration, and scales automatically.
No pipeline rewrites. No cluster management.
Just write the feature and run it.
Declare GPU and memory requirements once. LanceDB provisions compute, handles orchestration, and scales automatically.
No pipeline rewrites. No cluster management.
Just write the feature and run it.
Define Once. Keep it Up to Date.
Define a transformation once and materialize it as a versioned table.
Small changes shouldn’t trigger full recomputes. When new data arrives, only the affected rows are processed.
No orchestration tools. No full recomputes.
Your features stay in sync with the source data, automatically.
Small changes shouldn’t trigger full recomputes. When new data arrives, only the affected rows are processed.
No orchestration tools. No full recomputes.
Your features stay in sync with the source data, automatically.
When Jobs Fail, Work Doesn't.
Long-running GPU jobs fail at 80%. That shouldn’t mean starting over.
LanceDB checkpoints progress at the fragment level and resumes from the last successful step.
No wasted compute. No restarting from zero.
Finish the job without redoing the work.
LanceDB checkpoints progress at the fragment level and resumes from the last successful step.
No wasted compute. No restarting from zero.
Finish the job without redoing the work.
Change Logic, Recompute Only What Matters.
Updating a model or parameter shouldn’t mean rerunning everything.
Feature logic evolves. Recompute only affected rows. Add new data without touching existing results.
No wasted GPU hours.
Feature logic evolves. Recompute only affected rows. Add new data without touching existing results.
No wasted GPU hours.
From Features to Training Without Extra Pipelines.
The features you compute are immediately usable downstream.
Use them for training, evaluation, or retrieval without exporting or rebuilding anything.
Define features once, and use them wherever you need them.
Use them for training, evaluation, or retrieval without exporting or rebuilding anything.
Define features once, and use them wherever you need them.
Ship Features, Not Infrastructure.
Contact Sales
