Infinity AI-native Database is built for LLM-powered applications and modern AI systems, combining performance with flexible search and practical data handling for production workloads.
Hybrid search for AI applications
Infinity supports hybrid retrieval across multiple data representations, with advanced filtering and reranking to improve relevance for LLM use cases.
- Hybrid search over dense and sparse embeddings
- Search over tensors and full-text data
- Complex filtering
- Reranking options: RRF, weighted sum, ColBERT
- Common use cases: RAG pipelines, LLM apps, and intelligent assistants
Speed, scale, and data types
Infinity targets low-latency, high-throughput search on large vector datasets, making it suitable for high-load services where response time matters.
- Around 0.1 ms query latency on million-scale vector datasets
- Up to 15,000 QPS
- Supports strings, numeric fields, vectors, and other data types
Easier integration
A straightforward interface and architecture help teams integrate Infinity into existing infrastructure and ship AI search features faster.
