Pinecone
Managed vector database for AI applications
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AI-native database delivering millisecond hybrid search for LLM applications
High-performance database built for RAG and LLM applications, combining dense vectors, sparse vectors, tensors, and full-text search with sub-millisecond query latency.
Infinity is a cutting-edge AI-native database engineered specifically for next-generation LLM applications. It addresses the complex search requirements of RAG (Retrieval-Augmented Generation), conversational AI, recommenders, question-answering systems, and copilot applications by unifying multiple search modalities in a single platform.
The database delivers exceptional performance with 0.1ms query latency and 15K+ QPS on million-scale vector datasets, plus 1ms latency and 12K+ QPS for full-text search across 33M documents. It supports hybrid search across dense embeddings, sparse embeddings, tensors (multi-vector), and full-text with advanced reranking methods including RRF, weighted sum, and ColBERT. The platform handles diverse data types—strings, numerics, vectors—enabling rich, filtered queries.
Infinity offers flexible deployment options: Docker containers for client-server architectures, binary installations, or embedded directly in Python as a module. The single-binary architecture eliminates dependencies, simplifying production deployments. It runs on Linux (glibc 2.17+), Windows 10+ with WSL/WSL2, and MacOS, requiring x86_64 CPUs with AVX2 support and Python 3.10+.
When teams consider Infinity, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Retrieval-Augmented Generation (RAG) Pipeline
Enable LLMs to retrieve relevant context from millions of documents in under 1ms, improving answer accuracy while reducing hallucinations through hybrid dense/sparse vector search with ColBERT reranking.
Conversational AI with Semantic Memory
Power chatbots and virtual assistants with fast semantic search across conversation history and knowledge bases, delivering contextually relevant responses at 15K+ queries per second.
Multi-Modal Recommendation Engine
Combine text, image embeddings, and structured metadata in hybrid queries to deliver personalized recommendations with sub-millisecond latency, supporting real-time user interactions.
Enterprise Document Search
Search 33M+ documents using full-text and semantic vector search simultaneously, with advanced filtering and reranking to surface the most relevant results in 1ms.
Infinity combines dense vectors, sparse vectors, tensors (multi-vector), and full-text search in a single hybrid query with sub-millisecond latency. It also supports advanced reranking methods like ColBERT, purpose-built for LLM applications.
Yes, Infinity can be embedded as a Python module, eliminating the need for separate server processes during development. It also supports traditional client-server deployments via Docker or binary for production.
Infinity requires x86_64 CPUs with AVX2 support, Python 3.10+, and runs on Linux (glibc 2.17+), MacOS, or Windows 10+ with WSL/WSL2. ARM architectures are not currently supported.
Infinity supports multiple reranking strategies including Reciprocal Rank Fusion (RRF), weighted sum fusion, and ColBERT reranking to optimize result relevance across different search modalities.
You can deploy Infinity via Docker containers (recommended for production), standalone binaries, or embed it directly in Python applications. The single-binary architecture has no external dependencies.
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