> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Estimating Snowflake Resource Usage

## Overview

Kumo's Native App uses Snowflake resources depending on the size of the data and the complexity of the models being trained. These resources are primarily consumed in two ways:

1. Running containers in [**Snowpark Container Services (SPCS)**](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview).

2. Executing [**Snowpark queries**](https://docs.snowflake.com/en/developer-guide/snowpark/index) for data processing using Snowflake warehouses

The total resource usage in Snowflake is determined by:

* The number of hours the app is running.
* The selected hardware and warehouse size configuration.
* The number of training or prediction jobs executed.
* The size of the input data for each job.

## SPCS Resource Usage

Kumo runs two key components on Snowpark Container Services:

* **Kumo Control Plane** (UI and API management).
* **Kumo AI Engine** (model training and inference).

**How Resource Consumption is Measured:** As of 11-Feb-2025 Snowflake charges based on:

* **Hardware configuration** (CPU, GPU, Memory allocated to the compute instance used).

* **Duration** for which the compute instance runs

**Compute Resources:**

* Both the **Control Plane** and **AI Engine** run on a single **GPU compute instance** in SPCS.

* As of 11-Feb-2025, the available instance types are **GPU\_NV\_M** and **GPU\_NV\_L**, selected at app launch.

**Managing Resources Efficiently:**

* The compute resource is used continuously while the app is running.

* The app **can be suspended** without losing data to save resources.

* Instructions on suspending the app are available [**here**](https://kumo.ai/docs/installing-kumo-on-spcs#step-3-optional-shutting-down-kumo).

**Estimated Credit Usage:** The table below provides an estimate of Snowflake credits consumed per hour based on the selected container size. The most up to date numbers can be found in the official [<u>Snowflake Credit Consumption Table</u>](https://www.snowflake.com/legal-files/CreditConsumptionTable.pdf).

| **SPCS Container** | **Number of Rows** | **Snowflake Credits Per Hour** |
| ------------------ | ------------------ | ------------------------------ |
| GPU\_NV\_M         | \< 250M            | 2.68                           |
| GPU\_NV\_L         | \< 1B              | 14.12                          |

## Warehouse Resource Usage

To process data for training and predictions, Kumo executes Snowpark queries using Snowflake virtual warehouses. This includes:

* Reading input tables from Snowflake.
* Preparing data for training and prediction.
* Generating artifacts that are transferred to the GPU.

**Factors Affecting Credit Usage:**

* The **shape and structure** of the input data.

* The **complexity** of queries being executed.

* The **configuration of the job** (e.g., if a job processes an expensive Snowflake view, extra compute resources will be used).

* Using the **Graph Snapshot** feature can reduce resource usage across multiple jobs.

The following table provides an estimated credit consumption per job based on data size.

| **Data Size** | **Number of Rows** | **Warehouse Size** | **Snowflake Credits Per Job** |
| ------------- | ------------------ | ------------------ | ----------------------------- |
| 1-9GB         | 1-24M              | S                  | 1-3                           |
| 10-90GB       | 25-240M            | M                  | 3-9                           |
| 100-250GB     | 250M-1B            | L                  | 10-20                         |

## Other Resource Usage

**Storage:**

* Kumo stores various types of data within Snowflake, including:
  * Temporary artifacts from training.
  * Metadata for tables, graphs, and jobs.
  * Model binaries.

* A typical deployment with around **100 jobs** is expected to consume less than 1TB of data.

* This would be billed at your account’s standard rate for storage.

## Example Calculation

**Scenario: Weekly Email Recommendations**

* **Data Size:** 4 tables (users, products, purchases, browsing history) with a total of **100M rows, 50GB**.

* **Training Frequency:** Once per month.

* **Prediction Frequency:** Once per week.

**Estimated Monthly Resource Usage:**

* **SPCS Container:** GPU\_NV\_M
* **App Run Time:** \~12 hours per week
* **SPCS Credits per Month:** 12 \* 4 \* 2.68 = **128.64 credits**
* **Snowpark Credits Per Job:** 6
* **Jobs per Month:** 5
* **Snowpark Credits per Month:** 5 \* 6 = **30 credits**
* **Total Monthly Credit Usage: 158.64 credits**
* **Storage Cost: 500GB**

## Kumo Autoscaling on SPCS

**Dynamic autoscaling** for GPUs in SPCS. Here's what it does:

* **Automatically scale** GPU resources up or down based on demand.
* Eliminate the need for manual app launch and suspension.
* Optimize infrastructure usage, particularly for large workloads.