Infinity is an AI-native inference engine purpose-built for high-performance embeddings, reranking, and classification workloads. It delivers low-latency, high-throughput inference for the most commonly used AI model types in retrieval-augmented generation and search applications through a unified API.
Key Features
- Optimized inference for popular embedding models and rerankers with millisecond-level query latency
- Support for multiple data types including dense vectors, sparse vectors, tensors, full-text, and structured fields
- Developer-friendly Python SDK and single-binary deployment for quick integration
- Built-in observability and benchmarking tools designed for high-QPS production workloads
- Hybrid index architecture that unifies vector, sparse, and full-text indexes
Use Cases
- Powering vector search and retrieval-augmented generation (RAG) systems with low-latency inference
- Building similarity recommendation engines for e-commerce, content, and media platforms
- Deploying classification models at scale for enterprise applications
- Private deployment for compliance-sensitive workloads that cannot use external inference services
Technical Highlights
- Achieves high QPS throughput through a hybrid index architecture with smart resource management
- Can run as a standalone binary or be embedded directly in Python processes for flexible deployment
- Released under the Apache-2.0 license for both community and enterprise adoption