Get billion-scale vector search with real-time retrieval, without the cost and complexity of a standalone vector database.
Building vector search at scale shouldn’t mean sacrificing the reliability and speed of your operational database. We solved the trade-offs so you don’t have to.
Tuned ANN indexes maintain recall (e.g. 98%) across billions of vectors with high dimensionality (e.g., 1536+ dimensions).
Online rebuilds keep queries serving while you refresh your embeddings — without downtime or performance degradation.
New embeddings become searchable in real time without traditional ML pipeline lag or batch processing delays.
They’re handled via incremental index maintenance on dedicated vector nodes with sub millisecond write performance.
Vector and operational workloads run on dedicated nodes so neither can impact the other’s latency, even during spikes.
Independently scale compute for search vs. storage for data, ensuring you only pay for the resources you actually need.
Our benchmarks are grounded in realistic, high-demand scenarios. We don’t hide behind small datasets or quantization tricks.
ScyllaDB shows up to 14x higher efficiency depending on competitor and workloads.
| Platform | Workload Type | Throughput (QPS) | P99 Latency |
|---|---|---|---|
| MongoDB | 15.3M / 2048D / k=10 | ||
| Pinecone | 10M / 768D / k=10 | ||
| OpenSearch | 10M / 768D / k=100 | ||
| ScyllaDB | 10M / 768D / k=10 |
ScyllaDB results in markedly lower costs per query than competitors.
| Platform | Cost per Hour | $/QPS/Hour |
|---|---|---|
| MongoDB | ||
| Pinecone | ||
| OpenSearch | ||
| ScyllaDB |
The secret to our efficiency is total isolation. By decoupling the vector indexing engine from the main database storage engine, we eliminate the “noisy neighbor” effect that plagues integrated vector solutions
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.
Explore tradeoffs and strategies related to real-time AI at scale – including high-volume feature ingestion, fast retrieval, and low-latency vector search.
ScyllaDB Vector Search enables real-time similarity search at scale. It allows you to store high-dimensional embeddings alongside operational data and query them with millisecond latency within a single database, eliminating the need for a standalone vector database.
No. ScyllaDB integrates vector search directly into the database. This eliminates data duplication, synchronization pipelines, extra infrastructure, and operational complexity.
Common workloads include:
Learn more: https://www.scylladb.com/scale-real-time-ai/
Yes. You can store embeddings alongside user data, product data, and features. This allows you to retrieve the vector and its associated metadata in a single query, significantly simplifying application logic.
Learn more: https://docs.scylladb.com/manual/stable/features/vector-search.html
ScyllaDB is designed for extreme-scale AI workloads, supporting 1B+ vectors. Benchmarks show P99 latency as low as ~1.7 ms and throughput up to ~252,000 queries per second.
Benchmark: https://www.scylladb.com/2025/12/01/scylladb-vector-search-1b-benchmark/
In high-demand workloads (10M vectors, 768D), ScyllaDB offers up to 14x higher efficiency.
ScyllaDB supports high-dimensional embeddings up to 16,000 dimensions. It uses tuned ANN (Approximate Nearest Neighbor) indexes to maintain high recall (95%+) even at billion-scale.
Docs: https://docs.scylladb.com/manual/stable/features/vector-search.html
The secret is total isolation. By decoupling the vector indexing engine from the main database storage engine on dedicated vector nodes, ScyllaDB eliminates the “noisy neighbor” effect. This ensures that heavy vector workloads do not impact standard operational database performance.
