# Snowflake Savings Estimate Instructions

Using workload metadata, we simulate your environment and produce an estimate of how much we can save you. The estimate looks like this:

<figure><img src="https://1611507959-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FRex2SPQ8eJti1DS0eWrK%2Fuploads%2FdfqDVfmyznMcCKOPfSc0%2FDatabricks%20(14).png?alt=media&#x26;token=82ecdee3-3378-4eea-8cd3-cf0ea23c5894" alt=""><figcaption></figcaption></figure>

## How do I get an estimate?

We need a few things to generate the estimate: query metadata, warehouse metadata, and Snowflake usage metadata.

The fastest way to share those is to [set up a Snowflake account for Espresso](https://espresso.ai/snowflake-optimizer-onboarding).

If you'd prefer to share your metadata without setting up an account, you can also use a Snowflake Python worksheet. Log into the [Espresso AI dashboard](https://dashboard.espressocomputing.com) with your work email to generate a command you can run. This creates a secure upload token tied to your email address.

## How do I know the savings are accurate?

Our models are continuously calibrated with production Snowflake data to ensure our savings numbers are accurate.

The best way for you to judge accuracy is to compare our upfront savings estimate to the savings you see in production when we first turn on.

We also encourage users to run A/B tests once we've been on for a few months: shut Espresso off for a week and see how your actual spend compares to our savings calculation.

## NDA and support

Espresso AI is happy to sign an NDA. Contact <savings@espresso.ai> with your NDA or any questions.


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# 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://docs.espresso.ai/snowflake-optimizer/savings-estimate.md?ask=<question>
```

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.