Open Semantic Interchange (OSI)
A vendor-neutral, Apache-2.0-licensed standard (v0.1, January 2026, co-founded by Snowflake) for exchanging semantic models — metrics, dimensions, organizational relationships — across BI and AI platforms.
Summary
A vendor-neutral, Apache-2.0-licensed standard (v0.1, January 2026, co-founded by Snowflake) for exchanging semantic models — metrics, dimensions, organizational relationships — across BI and AI platforms.
The logical-layer analog of what Iceberg did for physical tables: portable business meaning. For AI agents querying lakehouse data on S3, OSI is what lets the model inherit governed definitions instead of inferring them — removing a hallucination class that comes from un-governed retrieval.
- v0.1 is a founding spec, not an adopted ecosystem — coverage currently traces mainly through Snowflake's own materials; watch for independent adoption before treating it as settled.
- OSI does not replace data contracts (schema/quality/SLA); it carries the meaning layer above them.
scoped_toLakehouse / LLM-Assisted Data Systems — the semantic layer over lake dataenablesRAG over Structured Data — governed definitions ground agent queries
Definition
A vendor-neutral open standard (v0.1 finalized January 2026, Apache 2.0 license) for exchanging semantic models — metrics, dimensions, and organizational relationships — across BI and AI platforms. Co-founded by Snowflake. Where Apache Iceberg unified the physical storage of tables, OSI aims to unify the logical business meaning of the data those tables hold.
An AI agent querying an S3 bucket can read the bytes but not the business context — what "net revenue" means, which dimension is canonical, how entities relate. Every BI tool historically encoded that semantic layer in a proprietary format, so meaning was lost at each platform boundary. OSI's bet: a portable, machine-readable semantic model lets an LLM querying lakehouse data understand it the way a human analyst does — removing a class of hallucination risk that comes from un-governed retrieval, not from the model.
Portable semantic layers across BI/AI platforms, grounding AI agents in governed business definitions before they query object-storage-resident data, semantic-model interchange between lakehouse vendors.
Connections3
Outbound3
scoped_to2enables1