# Overview The Alvin Query Optimizer is built in pure, high performance [Rust](https://www.rust-lang.org/) and leverages [Apache DataFusion](https://datafusion.apache.org/) to parse and transpile SQL. Cool tech aside, the most important thing is the results we are achieving — on average saving our customers 52% on their BigQuery spend, automatically though: * **Query acceleration:** Our powerful query accelerator, built on Apache DataFusion, uses semantic analysis and logical plan rewriting to optimize your queries for maximum performance. * **Resource optimization:** Take advantage of the most cost-effective pricing model for every query. Alvin dynamically routes your queries to the cheapest and most efficient compute. * **Intelligent caching:** BigQuery's cache can be fragile. Alvin optimizes your workloads to maximise cache hits. More cache hits means near-instant results with zero cost. **How to get started?** Connect your BigQuery environment at connect.alvin.ai to generate a cost saving report. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://dev.alvin.ai/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.