How much compute are you burning
on features you already have?
Adjust your numbers. Watch the math materialize.
Based on 540 total features across your fleet · Updated in real time
Sub-5ms responses.
Even at 100K RPS.
The Featurize online store pre-materializes features at write time, not read time. When your model calls get_features(entity_id), the answer is already there — no joins, no Spark job, no cache miss spiral. Backed by a tiered storage engine: Redis L1 → Cassandra L2 → object store for history.
Training and serving
read the same bytes.
Training-serving skew is the silent model killer — your metrics look fine in notebooks but degrade in production because the feature pipeline drifted. Featurize enforces a single materialized store with schema versioning and point-in-time retrieval. Your training job and your inference server call the same API with the same contract. Drift is impossible by construction.
Define once.
Reuse forever.
Stop rewriting the same Spark aggregation in your training notebook, your streaming job, and your REST endpoint. Define a feature once with the Featurize SDK. Promote it with one call. It materializes automatically across batch, streaming, and online stores — versioned, tested, and ready for your next model.
We had 14 different implementations of the same rolling-window feature. After Featurize, we have one. The model performance improvement was a side effect — the real win was deleting 8,000 lines of duplicated Spark code.
We migrated 230 features in a weekend. Zero serving incidents on Monday. The point-in-time retrieval alone would have saved us three production fires last quarter.
Our data scientists stopped filing tickets to the platform team. They can define, test, and promote features themselves. That alone freed up 40% of my team's sprint.
Stop rewriting features.
Start shipping models.
Every week you wait, your feature debt compounds. Your best engineers are stuck debugging schema mismatches instead of building the next model. Featurize ends that cycle.