Pinecone
Managed vector database for AI applications
Infinity
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.
Overview
Purpose
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.
Capabilities
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.
Deployment
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+.
Highlights
Sub-millisecond hybrid search combining dense vectors, sparse vectors, tensors, and full-text
15K+ QPS on million-scale datasets with 0.1ms query latency
ColBERT reranking and multiple fusion methods (RRF, weighted sum)
Single-binary architecture with Python embedding and intuitive API
Pros
- Exceptional performance with sub-millisecond latency on large-scale datasets
- Unified platform for multiple search modalities eliminates integration complexity
- Zero-dependency single binary simplifies deployment and operations
- Native Python embedding ideal for AI/ML development workflows
Considerations
- Requires x86_64 CPUs with AVX2; no ARM or older architecture support
- Windows deployment requires WSL/WSL2 configuration overhead
- Relatively new project with smaller community compared to established databases
- Limited to Python 3.10+ for client SDK compatibility
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Fit guide
Great for
- RAG applications requiring fast hybrid search across multiple embedding types
- LLM-powered products needing sub-millisecond retrieval (chatbots, copilots, Q&A)
- AI teams wanting embedded database functionality within Python environments
- Production systems demanding high QPS with complex filtering and reranking
Not ideal when
- ARM-based infrastructure or legacy x86 systems without AVX2
- Projects requiring native Windows deployment without WSL
- Teams needing mature ecosystem with extensive third-party integrations
- Applications using programming languages other than Python for primary development
How teams use it
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.
Tech snapshot
C++87%
Python10%
Yacc1%
TypeScript1%
Shell1%
CMake1%
Frequently asked questions
What makes Infinity different from other vector databases?
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.
Can I embed Infinity directly in my Python application?
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.
What are the hardware requirements?
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.
How does Infinity handle reranking in hybrid search?
Infinity supports multiple reranking strategies including Reciprocal Rank Fusion (RRF), weighted sum fusion, and ColBERT reranking to optimize result relevance across different search modalities.
What deployment options are available?
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.
Project at a glance
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LicenseApache-2.0
Repo age3 years old
Last commit2 days ago
Primary languageC++
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