We unify your streaming and batch data to optimize for latency, cost, and correctness.
We’re still building, but we can keep you updated.
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Latency, cost, and correctness
Data teams often start with batch processing because of cost and correctness. Over time, streaming is added for latency.
Claypot works with both streaming and batch data, allowing you to choose to the best-performing data for each use case. If one-hour delay is good enough, we'll switch to batch. Things are changing fast and you need reaction in milliseconds? We'll switch to streaming.
Our unified data abstraction makes it easy for you to leverage any data source to quickly experiment and deploy new AI models. We especially shine for use cases where latency is critical such as customer support, fraud detection, dynamic pricing, and personalization.
Improve business metrics, enable more use cases, while reducing computation and operational cost!
Real-time Machine Learning: Challenges and Solutions