Infino AI — AI for Data Science & Advanced Predictive Analytics

Infino addresses the fundamental infrastructure bottleneck of managing separate clusters for vector search, keyword search, and analytical queries by storing data directly as standard Parquet files in object storage. By eliminating the need for expensive disk replicas and redundant systems, you can achieve up to a tenfold cost reduction compared to traditional search stacks like Elasticsearch or Qdrant. For data scientists and engineers building retrieval-augmented generation (RAG) applications or operational agents, Infino provides native integration with Model Context Protocol (MCP) and agent-to-agent (A2A) frameworks, enabling automated context ingestion from raw logs, documents, and snapshots. The open-source core engine is available under an Apache-2.0 license, giving you the flexibility to run single-node instances locally or via Python, Rust, and Node packages. However, transitioning from traditional Lucene-based setups requires accepting specific trade-offs, such as the initial lack of custom analyzers, fuzzy search syntax, and certain update write paths. If you manage large-scale logs, enterprise documents, or heavy vector workloads that incur unsustainable cloud bills on traditional storage architectures, Infino provides a streamlined architectural alternative centered on object storage and open file formats.