Define once. Use everywhere.

An OSI-native semantic layer for metrics across every data platform

One open model in, correct warehouse-specific SQL out — so revenue means the same thing in DuckDB, Postgres, Snowflake, ClickHouse and StarRocks.

Install Run your first query Why Dosi

The problem it solves

Every dashboard, query, and notebook re-implements what a metric means — and the copies drift. One filters cancelled orders, another doesn't; a stray join quietly doubles the total. You end up with two "correct" numbers that disagree.

Dosi gives you one place to define each metric and generates the SQL for you — the same definition, correct on every warehouse, with the double-counting mistakes engineered out.

See it in 30 seconds

Define metrics once in a model, then ask for them by name:

$ dosi list metrics --model fixtures/orders/model.yaml
NAME              KIND        DATASETS          DESCRIPTION
revenue           aggregate   orders            Total order amount
order_count       aggregate   orders            Number of orders
unique_customers  aggregate   orders            Distinct purchasing customers
avg_order_value   ratio       orders            Revenue per order (ratio)
total_margin      expression  orders, products  Revenue minus cost (expression over two aggregates)

$ dosi query --model fixtures/orders/model.yaml \
    --metrics revenue --group-by orders.status --execute --db orders.db
status     revenue
completed  350
cancelled  100
2 rows

Change --dialect snowflake and the SQL changes — your model doesn't.

Why teams use it

• One model, many warehouses

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  The same OSI model compiles to correct SQL for 13+ dialects. Switch warehouses with a flag, not a rewrite.

• Correct by construction

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  Fan-out protection means it never silently double-counts. If a number can't be computed safely, it tells you — it doesn't guess.

• Open standard, no lock-in

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  The input is pure OSI YAML — a vendor-neutral format. No proprietary semantic layer to buy into.

• Fast, and Arrow-native

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  A compact Rust engine with an in-process DuckDB and zero-copy Arrow result streaming into DuckDB, Polars, or pyarrow.

• Use it your way

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  A CLI, a REST + Arrow server, and Python bindings — same engine, same answers.

• Built for automation

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  Structured, machine-readable errors with stable codes and suggested fixes — so scripts and AI agents can self-correct.

Start here

• Install

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  Get the dosi binary and verify it — a couple of minutes, no database required.

• Run your first metric query

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  A hands-on, 10-minute tutorial from a model to real results you can verify by eye.

• Why Dosi

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  What OSI is, what problem a semantic layer solves, and why the numbers are trustworthy.

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Open source under Apache-2.0. Source, issues, and design notes live on GitHub.