Infino AI

Unified retrieval layer for SQL, vector, and graph search on Parquet files

Coding & Development AI Models & APIsFreemium

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$75/moStarting price

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About Infino AI

Infino provides a single retrieval layer that combines full-text, vector, and graph search capabilities on Parquet files stored in object storage. It enables agents to express complex queries in a single SQL statement, integrating keyword, semantic, and graph-based retrieval. The system supports hybrid search by combining multiple retrieval modes to improve accuracy. Data is stored in Parquet format, allowing for scalable and cost-effective analytics without requiring separate databases for different search types. Infino is designed for use cases involving large-scale logs, events, or structured datasets where retrieval speed and cost efficiency are critical. It is built to handle both small and large document sizes efficiently, with pricing based on storage, write tokens, read tokens, and returned bytes.

Key features

Full-text search on Parquet files
Vector search with embeddings
Graph search for linked data relationships
Hybrid search combining multiple retrieval modes
SQL-based query interface for agents
Automated vector tuning for improved accuracy
Parquet hydration for data preparation
Multi-node distributed query support (enterprise only)

Use cases

Log and event analysis with hybrid search queries
Agentic retrieval for answering complex questions in a single SQL statement
Cost-efficient analytics on large-scale datasets stored in Parquet

Pros

Combines full-text, vector, and graph search in a single system
Uses Parquet files on object storage for cost-effective scalability
Supports hybrid search by combining multiple retrieval modes
Designed for low-latency queries across large datasets
Offers open-source, cloud, and enterprise deployment options

Cons

No free tier beyond the open-source single-node version
Enterprise plan required for high availability and multi-node queries
Limited to Parquet file format for data storage
Cloud version is multi-tenant, which may not suit all compliance needs

Frequently asked questions about Infino AI

What does Infino AI do?

Infino AI provides a single retrieval layer that combines full-text, vector, and graph search capabilities on Parquet files stored in object storage. It allows agents to express complex queries in a single SQL statement, integrating keyword, semantic, and graph-based retrieval for improved accuracy and efficiency.

Who is Infino AI designed for?

Infino AI is designed for use cases involving large-scale logs, events, or structured datasets where retrieval speed and cost efficiency are critical. It suits teams needing scalable search and analytics infrastructure on object storage, such as government agencies, enterprises, and engineering teams.

How does Infino AI handle pricing?

Infino AI's pricing is based on storage, write tokens, read tokens, and returned bytes. The cost model measures ingest work with write tokens, query work with read tokens, and charges for the volume of data returned. There is an account minimum, and costs scale with usage.

Can Infino AI replace multiple search and database tools?

Yes, Infino AI can replace Elasticsearch, vector databases like Qdrant or Pinecone, graph databases like Neo4j, and analytics warehouses like Snowflake or Databricks. It consolidates these functions into a single system operating on Parquet files in object storage.

What integrations does Infino AI support?

Infino AI supports real-time ingest of Parquet files from object storage systems like S3, GCS, and Azure Blob. It also integrates with table formats such as Iceberg and Hudi, and allows SQL-based hybrid search combining keyword, semantic, and graph retrieval methods.

How do I get started with Infino AI?

Users can get started with Infino AI by signing up for a free tier, which does not require a credit card. The platform offers live demos and documentation to guide integration and setup, including replacing existing search and database tools with Infino's unified retrieval layer.