The hard part of production ML is the data infrastructure, not the modeling: point-in-time-correct training data over years of events, and the same features served live in milliseconds. Most teams build that twice, once by researchers for training and again by an engineering team for production. Zipline generates the production half from the same definition, so features are written once and never diverge.
No handoff to a serving team.
Automate privacy and compliance.
Drift alerts catch issues in minutes.
In lockstep with your source control.
No waiting for logs to accumulate.
1M+ QPS at <10ms p95.
Founders on every POC
Questions go to the people who built the system, and answers come back in minutes. Our customers will tell you the same.
We stay past the sale
We advise your ML team on architecture well beyond the parts Chronon touches.
No lock-in
Everything runs in your own cloud, on the object store and KV store you already operate. There is no data to migrate in, and nothing of yours to extract if you ever leave.
Zipline runs entirely inside your own cloud, or fully air-gapped. Your data, code, and metadata never leave your environment, which satisfies data residency and sovereignty requirements without relying on contractual controls. Zipline is SOC 2 Type II compliant.
Zipline reads from your warehouse rather than replacing it. It covers the part a warehouse-native feature store leaves thin: real-time serving, reach across everything you run, and deployment inside your own cloud.
Built for real-time
Warehouse feature stores are strongest at offline, batch-computed features. Sub-second freshness and millisecond lookups at high QPS are a different problem, and in a warehouse-native stack that is usually a separate layer. Zipline produces batch and streaming features from one definition and serves them online itself.
Works across every source
Your features live across Snowflake, BigQuery, Databricks, Kafka, and your own services. A built-in feature store ties you to one platform’s data and compute. Zipline connects to all of them and stays cloud- and warehouse-agnostic, so adopting it doesn’t deepen lock-in to any single vendor.
Runs in your cloud, on your stores
There is no data to migrate and no new datastore to buy: Zipline writes to the object store and KV store you already run. You are not paying a second platform to hold a copy of your data.
Proven at the top end
The engine is Chronon, which we built at Airbnb and which now runs business critical ML at Stripe, Netflix, Uber, and OpenAI. It is infrastructure that has carried production traffic at that scale for years, not a feature bolted onto a data platform.
