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Harnessing LLM Chatbots To Monitor Snowflake

Updated: Sep 7

Author: Gaurav Bhandari


Monitoring the performance and cost efficiency of a Snowflake data platform is crucial for maintaining optimal operations and managing expenses effectively. Large Language Models (LLMs) integrated into chatbots can provide a powerful solution for Snowflake monitoring, offering real-time insights, proactive recommendations, and cost optimization strategies. In this blog, we will explore the benefits of using LLM chatbots for Snowflake monitoring, specifically focusing on performance optimization and cost efficiency and also around our journey using LLM-based chatbots integrated with Snowflake.

Understanding LLM Chatbots

LLM chatbots leverage the capabilities of Language Models to engage in natural language conversations and provide intelligent responses. By integrating LLM chatbots with snowflake monitoring, organizations can gain actionable insights, optimize performance, and enhance cost efficiency through interactive and intuitive conversational interfaces.

Performance Monitoring

LLM chatbots can analyze Snowflake query performance metrics such as long-running queries, partitions scanned & type of caching used for a particular query in near real-time by leveraging the metadata KPI views in Snowflake like Query_History & Access_History. Chatbot can integrate with Snowflake to inform users about the metrics related to the tables and suggest recommendations.

Cost Optimization Strategies

LLM chatbots can analyze Snowflake cost metrics and account-specific historical usage data for the warehouses, users & roles over a specified period of time to provide insights into cost drivers, usage patterns and optimization opportunities. The chatbot can offer recommendations for optimizing query efficiency, reducing storage costs, warehouse resizing, multi clustering or adopting cost-saving strategies tailored to the organization's specific needs.

Intelligent Recommendations and Guidance

Proactive Performance Recommendations

LLM chatbots are capable of analyzing Snowflake performance data and generating proactive recommendations for query optimization, indexing strategies or data model adjustments. By providing prompt guidance, the chatbot enables organizations to address performance issues promptly, improving overall system efficiency.

Contextual Cost Optimization Suggestions

LLM chatbots can efficiently understand the context of an organization's Snowflake usage and can provide personalized cost optimization suggestions. Whether it's identifying underutilized resources, recommending data archiving strategies, or suggesting cost-effective storage configurations, the chatbot assists organizations in making informed decisions to optimize costs.

Continuous Learning and Improvement

LLM chatbots have the ability to continuously learn and improve over time. By analyzing user interactions, performance data, and cost optimization outcomes, the chatbot can refine its recommendations and adapt to evolving Snowflake environments. This iterative learning process enhances the chatbot's effectiveness in providing real-time insights and valuable guidance.

At, we have developed LLM-based chatbots using streamlit and open source bot frameworks, which help the users to interact with Snowflake in natural language and help get recommendations based on the use case.

Here are a few of those interesting bots that serve a specific purpose:


Watchkeeper is a accelerator that uses Snowflake’s metadata tables, account usage statistics & third-party data to generate KPIs around Compute Cost, Storage Cost, Security, Performance & ETL Jobs.

Kippo is an LLM-based chat model that uses table schema definitions to generate SQL responses based on the user's natural language input. Kippo is currently trained on Watchkeeper tables in the Snowflake account, hence the name "Kippo Watchkeeper".

The idea behind this bot is to use LLM to train it on the schema definition of Watchkeeper tables. The bot then generates a SQL code via OpenAI's GPT model based on a question asked about the learned schema definitions. This query is run in Snowflake using the Python connector, and the result is displayed in the Streamlit application.

The above diagram depicts how each of the user conversations would flow. Once the prompt is received from the user, NLP techniques would be applied through openAPI, it would then be converted into a Snowflake-specific query. Once the query is generated, it would be fired in the Snowflake database using the Snowflake Python connector, and the result would be displayed to the user.

Here is a snippet of how the bot works:

Let’s say if a user wants to know the top 10 warehouses with the most credit usage, they can utter the question in natural language format (plain English), this will be taken by the bot and converted into a SQL statement that Snowflake understands, thereafter will be displayed in the bot UI and fired in snowflake. This will display the results which the user can further analyze. The bot is also capable of reframing the query in case the query encounters any error in Snowflake.


Kippo Optimizer is a versatile chatbot designed to be an all-in-one solution for users who want to scrape text, documents and websites to receive recommendations on Snowflake best practices. One of the key features of the bot is its ability to understand natural language, making communication with users easier. Additionally, it can handle dialog flows and conduct sentiment analysis of conversations, providing users with a personalized experience.

To ensure that bot understands user intent, it is trained with various phrases and entities. This allows it to pick up on important information provided by the user and tailor its recommendations accordingly. Kippo Optimizer also has a built-in small talk feature that enables it to engage in chit-chat conversations with users.

To provide users with the most relevant recommendations, the bot is integrated with various best practice documents that it can scrape. This ensures that users receive the most up-to-date and accurate information possible. The bot is available on both Slack and a web-based UI, providing users with a seamless and user-friendly experience.

In the above example when the user asked the bot ways he could tune the query performance, the bot scraped through the various sources it was trained with, identified the intent, summarized the response and delivered it back to the user so that the user could take measures accordingly to tune the query.


The WatchKeeper bots can provide a complete solution for any issues related to cost, performance or security optimization. Through the Kippo WatchKeeper bot, users can just ask the issue in natural language format which is then converted into a SQL query and the bot produces the desired results. After analyzing the results, users can use the Optimizer bot to troubleshoot the exact issue and get recommendations around optimization techniques related to the issue.

Integrating LLM chatbots into Snowflake monitoring processes unlocks new possibilities for optimizing performance and cost efficiency. These intelligent chatbots enable monitoring of key metrics and provide recommendations for optimization, leading to better resource utilization and cost management. By leveraging the power of LLM-based chatbots that can interact with Snowflake, organizations can resolve cost and performance issues much more quickly, cutting down on the operational toil that so often plagues engineering teams.

Snowflake is expanding its footprint in this space with the launch of ‘Snowpark Container Services’, a lot is expected to happen around how LLM’s work with and within Snowflake, so stay tuned!

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