Billion-scale embedding retrieval at sub-millisecond latency.
Purpose-built vector database for production RAG and semantic search. Hybrid BM25 + dense retrieval, GPU-accelerated HNSW indexing, real-time upserts, and a query API designed to feel as simple as a SQL SELECT statement.
Index construction and nearest-neighbour search are GPU-accelerated using cuVS (RAPIDS). Build a 100M-vector HNSW index in minutes, not hours.
Combine sparse keyword matching with dense semantic similarity in a single query. Reciprocal rank fusion merges results with configurable blend weights.
Vectors are queryable within milliseconds of insertion — no batch ingestion jobs, no index rebuild downtime. Designed for live document pipelines.
Attach arbitrary JSON metadata to every vector. Pre-filter by metadata before the ANN search to dramatically reduce search space and improve relevance.
Namespace isolation allows one deployment to serve thousands of tenants with strict data separation. No cross-tenant data leakage at any layer.
Start with the serverless tier (pay per query) for development. Migrate to dedicated nodes for predictable latency and higher QPS guarantees in production.
| Index type | HNSW (GPU-accelerated via cuVS) |
| Scale | Billion-vector per namespace |
| Query latency | < 1 ms (p99, dedicated tier) |
| Hybrid search | BM25 + dense, RRF fusion |
| Real-time upserts | Yes, queryable in < 10 ms |
| Embedding dimensions | Up to 65,536 |
| Metadata filtering | JSON, pre-filter before ANN |
| Multi-tenancy | Namespace-level isolation |
Vector Store is currently planned — estimated Q3 2026.
No pricing yet. We offer tailored solutions only.
CogniCloud is in active development. Join the waitlist to get early access and stay updated on our roadmap. No pricing yet — we'll work with each team to find the right fit.
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