Modern AI applications rarely need just one thing from their data. They need a feature store for millisecond context and vector search for similarity and the usual answer is two specialised databases bolted together. That means two systems to operate, a sync pipeline that drifts, and costs that compound. What if one database did both?
Join us as a developer and a database expert collapse a feature store and vector search into a single ScyllaDB Cloud layer. We'll serve fresh feature point-reads and approximate-nearest-neighbour vector queries from the same engine and the same data to keep both fast while the write volume spikes.
In this webinar, you'll learn how to:
- Reduce the complexity of running multiple specialised databases
- Unify feature store and vector search workloads with ScyllaDB's architectural patterns
- Cut synchronization lag out of your ML-driven pipelines
- Sustain ultra-low latencies during massive traffic spikes
