Real-time AI starts with real-time data. ScyllaDB delivers the ultra low-latency, high-throughput performance you need to power real-time AI systems at scale. From thousands of vector search queries to millions of feature requests per second at billion-scale – ScyllaDB keeps your AI data pipeline real-time and bottleneck-free.
The foundations of ScyllaDB – predictable low latency at scale with unmatched efficiency – pair well with many AI pipelines. If your AI workload requires petabytes of storage, live-streaming data, millions of features, or billions of vector embeddings, ScyllaDB might be a great fit. ScyllaDB is commonly used for:
Instantly adapt to user behavior across millions of live events.
Anticipate outcomes with ultra-low latency across devices.
Contextual vector search at scale across billions of embeddings.
ScyllaDB Vector Search indexes and retrieves billions of embeddings while sustaining hundreds to thousands of queries per second – at predictable, low millisecond latency. Dedicated nodes ensure high availability, deliver high throughput under heavy load and minimize impact on existing ScyllaDB workloads. This makes ScyllaDB optimal for large-scale semantic search and retrieval-augmented generation workloads.
ScyllaDB is frequently used as a high-performance backend for feature stores. Its shard-per-core design scales linearly with both data and queries. Features like time-series compaction for temporal data, tunable consistency for read/write paths and workload prioritization (to balance training and inference SLAs) ensure your feature store has the latest data in real-time.
ScyllaDB serves embeddings and precomputed results directly from memory with predictable low latency for semantic search and caching. Frequently accessed vectors or query results are cached in place – keeping a lid on recomputation costs and response time.
5x ScyllaDB demonstrated up to 5x better P99 latency numbers than native Vector Search databases such as BLAH and BLAH
6x ScyllaDB outperformed NNN queries with up to 6X better P99 query response time compared to Redis with a BLAH BLAH dataset.
8x ScyllaDB achieved 8x better performance than Cassandra when doing BLAH BLAH for a BLAH BLAH workload.
Tripadvisor uses ScyllaDB on AWS to power real-time ML personalization. At peak, they handle ~500K ops/sec with P99 latencies of 1-3 ms. Their feature store serves up to 5 million static features/sec and 0.5 million user features/sec.
Nauto applies AI to camera and sensor data for fleet safety. ScyllaDB provides the fast, unified data layer needed for on-the-fly facial recognition and driver behavior analysis. ScyllaDB replaced a fragmented stack of Redis, Elasticsearch, Kafka, and Postgres.
Medium built a fast, scalable feature store on ScyllaDB to drive its content recommendations. ScyllaDB powers the “lists” data layer in their ML infrastructure, enabling rapid retrieval of personalized story lists and features for users.
TRACTIAN uses ScyllaDB to handle continuous streams of time-series sensor data for real-time ML in industrial IoT. After replacing MongoDB, they achieved 10x better throughput and latency, enabling faster predictive maintenance analytics across their customer base.
Get practical tips for building fast feature stores that scale. We’ll walk you through how to build a real-time ML app with Python, Feast, and ScyllaDB.
