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Infino AI System Properties

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Name

Description

Infino is an open-source retrieval engine for full-text, vector, and SQL search, with your data stored as Apache Parquet on object storage.

Primary database model

Search engine

Secondary database models

Relational DBMS
Vector DBMS

DB-Engines Ranking

measures the popularity of database management systems

Score	0.00
Rank	#386	Overall
	#26	Search engines

Website

infino.ai

Technical documentation

infino.ai/docs

Developer

Infino AI, Inc. Founded by creators of OpenSearch

Initial release

2026

Current release

v0.1.0, June 2026

License

Commercial or Open Source

Open Source (Apache License 2.0)

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

Server operating systems

Linux, macOS. Data can reside on Amazon S3, Azure Blob Storage, or local disk.

Data scheme

yes (Arrow/Parquet schemas defined at table creation)

Typing

predefined data types such as float or date

yes (Apache Arrow data types)

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes. BM25 full-text indexes and IVF vector indexes, declared per column at table creation and stored inside the Parquet files alongside the data.

SQL

Support of SQL

yes. Full SQL including joins across tables, with search exposed as SQL table functions (bm25_search, vector_search, hybrid_search) so ranked retrieval composes with filters, joins, and aggregations in one query.

APIs and other access methods

Rust API (Arrow-native), Python API, Node.js API. Data files remain valid Apache Parquet and are directly readable by any Parquet-compatible engine (Snowflake, Databricks, BigQuery, DuckDB, Spark, etc.).

Supported programming languages

Rust, Python, JavaScript (Node.js)

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning across immutable Parquet files, with manifest-based pruning (bloom filters, value ranges, vector centroids) to skip non-matching files

Replication methods

Methods for redundantly storing data on multiple nodes

none built in; durability and redundancy delegated to the underlying object store

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

snapshot isolation with atomic commits; readers always see a consistent published snapshot

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

atomic multi-row commits with snapshot-isolated reads; no multi-statement ACID transactions

Concurrency

Support for concurrent manipulation of data

yes (concurrent readers and writers via manifest snapshots)

Durability

Support for making data persistent

yes (data persisted as Parquet on object storage or local disk)

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes (in-process memory:// mode for ephemeral workloads)

User concepts

Access control

RBAC (cloud/enterprise version)

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