Snowflake Intelligence: 2025’s Complete Guide to AI-Powered Data Analytics

The business analysis landscape has fundamentally changed. What once took data teams days to build custom dashboards, write complex SQL queries, and hunt down answers hidden in warehouses, now can happen in seconds through simple conversations. 

How? When it comes to Snowflake, it’s thanks to Snowflake Intelligence. Every employee now has the power to engage in natural language conversations with their data and access accurate insights, all while maintaining enterprise-grade security and governance. 

But Snowflake Intelligence still has its limits. It introduces new complexities around compute costs, warehouse optimization, and AI/ML resource management. This guide walks you through everything you need to know about Snowflake intelligence: 

• What Snowflake Intelligence is
• How to setup Snowflake intelligence
• Best practices
• Keeping your implementation cost effective 

What is Snowflake Intelligence?

Snowflake Intelligence is an agentic AI experience built directly into Snowflake Data Cloud. It allows business users to use natural language conversations to interact with enterprise data. Basically, it allows anyone to use the vast data resources you have stored in your Snowflake platform to form actionable business insights, no SQL knowledge or technical expertise needed. 

How Does Snowflake Intelligence Work? 

To use Snowflake Intelligence effectively, you’ll need to fully understand the architecture behind it. These are the three pieces that make up this feature: 

1. Agents: AI models with access to specific data sources and tools. Can be customized with semantic views or models, Cortex Search services, and custom tools. 
2. Supported models and regional availability: Snowflake Intelligence supports multiple language models (i.e., Claude 4.0, GPT 4.1). If your preferred model isn’t available in your region, Cortex Cross-region inference automatically routes your queries to access it, so you can maintain consistent AI capabilities regardless of where your data lives. 
3. Integration with Snowflake’s security framework: All queries from Snowflake Intelligence use the user’s credentials, which means that role-based access controls (RBAC), data masking policies, and row-level security are carried over. 

Why Use Snowflake Intelligence 

There’s always been friction between business analysts and managers. Data is hard to find, scattered across CRMs, support systems, spreadsheets, and Slack conversations, making getting answers a matter of navigating endless dashboards. Snowflake fundamentally transformations this dynamic by: 

• Democratizing data access: Every business user can independently explore your data and get immediate answers. 
• Improving data team productivity: Data teams can focus their expertise on true priorities, not endlessly answering business user answers. 
• Maintaining security and governance: Unlike standalone AI tools that require replicating security controls and permissions, Snowflake Intelligence inherits all pre-existing role-based access controls, data masking policies, and governance rules. 
• Preventing data siloes: A single conversation can pull insights from unstructured customer support tickets, product usage metrics, and external market research, providing holistic context nearly impossible to achieve by human standards. 

Snowflake Intelligence Real-World Use Cases

Snowflake Intelligence can be applied in many different ways across different types of organizations and different roles. Here are a few examples of how Snowflake Intelligence can change the game for: 

• Sales managers: Can now get answers to complex, previously days-long questions like “what were my top product sales in the West region last quarter, and why did product X outperform product Y?”. 
• Financial analysts: Can combine portfolio performance with real-time market news. 
• Product managers: Can analyze customer feedback paired with industry research to identify market trends and competitive dynamics.

And all of this can be done through natural conversations instead of SQL-queries or complex dashboard creation. 

How to Set Up Snowflake Intelligence: Step-by-Step Implementation

You now understand the wide appeal of using Snowflake Intelligence. Now you just need to set it up. 

Plan for two to four hours to complete this initial setup, budgeting more depending on your data complexity. Most organizations can get this first agent running within a single workday. 

Here’s how: 

Step One: Initial Configuration

Before you get into creating your own agent, you’ll need to establish a foundational database and schema structure: 

-- Create the main database for Snowflake Intelligence
CREATE DATABASE IF NOT EXISTS snowflake_intelligence;
GRANT USAGE ON DATABASE snowflake_intelligence TO ROLE PUBLIC;

-- Create the schema for agent storage
CREATE SCHEMA IF NOT EXISTS snowflake_intelligence.agents;
GRANT USAGE ON SCHEMA snowflake_intelligence.agents TO ROLE PUBLIC;

-- Grant agent creation privileges to appropriate roles
GRANT CREATE AGENT ON SCHEMA snowflake_intelligence.agents TO ROLE <your_role>;

This structure means your agents are kept organized and easily accessible. 

Step Two: Create Your First Agent

The easiest way to get started with Snowflake Intelligence once you have an established base is by creating a general knowledge agent. This will help you understand the interface before connecting your data. 

This basic agent will be limited in business usage, but it can still answer general questions and help you get started. 

Step Three: Connect Your Agents to Structured Data with Cortex Analyst

Next you’ll need to create a semantic view that defines your data model and business context to pull any business-relevant insights from your structured data. Create this agent with Cortex Analyst. 

Once you’ve finished, your agent can generate and execute SQL queries using natural language queries against your structured data. 

Step Four: Enable Unstructured Data Search with Cortex Search

If you’re querying documents, support tickets, or other text-heavy content, you’ll want to configure it with Cortex Search services. Set this up using the same Tools section. 

Once you’ve finished, your agent can now search and retrieve information from documents and unstructured sources. 

Step Five: Add Custom Tools 

The addition of custom tools will get you extended functionality. Custom tools allow agents to call any stored procedures and functions, enabling actions like sending notifications, updating external systems, or performing complex calculations. 

Note: Custom tools only support simple types (numbers, strings) as parameters. Arrays, maps, and JSON objects aren’t supported. 

Step Six: Configure Agent Access and Permissions

Control who can use which agents with role-based access. 

Pro tip: Even with agent access, users can only query data they have permissions to access thanks to Snowflake’s underlying security framework. 

Managing Costs with Snowflake Intelligence at Scale

Snowflake Intelligence might dramatically improve data accessibility and user productivity, but it comes with new and more complex compute costs. Every natural language query translates directly into warehouse compute – a.k.a., generating SQL, executing queries, processing search results, and rendering credit-costly visualization. 

Without proper management, these costs can spiral quickly. A well-intented rollout to hundreds of users can easily add up to tens of thousands of monthly unexpected compute expenses. 

Let’s break down the five biggest cost drivers so you can prevent bill shock: 

• Warehouse compute for Cortex Analyst: Each time an agent generates and executes a SQL query against structured data, it has to use the warehouse you’ve configured for that semantic view. Complex queries joined with multiple large tables can consume significant compute – especially if users follow up with multiple questions. 
• Cortex Search operations: Searching unstructured data via Cortex Search services means compute costs for indexing, similarity matching, and embedding generalization – and that cost will only continue to scale with the volume of documents, query complexity, and frequency of searches. 
• AI model inference: Cortex AI pricing might be consumption-based, but lots of users asking lots of questions throughout the day adds up. Consider which model (Claude 4.0 vs. GPT 4.1) as that can also impact costs. 
• Cross-region inference: If you’re using Cortex Cross-region inferences to access models not available in your region, additional data transfer costs apply for cross-region responses or moving requests. 
• Idle warehouse time: Any warehouses left running between sporadic queries result in a bill for unused computes. On the other hand, if you start aggressively suspending the warehouse, frequent resume operations leave you with a poor and slow user experience and resume overhead costs. 

Snowflake Intelligence Cost Optimization Best Practices

So, knowing that these are the biggest cost drivers, how can you adjust your best practices to keep operations and budget fully optimized? 

You’ll find that most of the best practices listed below overlap with common snowflake cost management tips. There are a few differences – especially when it comes to agents – so read carefully. 

• Right-size warehouses for agent queries: Match your warehouse sizes to typical query complexity, and reserve larger warehouses for agents handling complex queries or large datasets. 
• Use warehouse auto-suspend policies: Configure aggressive auto-suspend policies to minimize idle compute costs while maintaining a quality user experience. 
• Implement semantic view optimization: Create well-designed semantic views that pre-aggregate data, use materialized views, or restrict data scans to reduce the compute required per query. 
• Educate users on cost-effective practices: Train users on how to ask specific questions instead of broad queries, and use filters to narrow data ranges. 
• Set up agent-specific cost tracking: Create dedicated warehouses for different agents or business units so you can get granular cost attribution and chargebacks and help stakeholders understand the true ROI behind their queries. 

These best practices work… but there’s one problem: implementing them and maintaining them manually is a full-time job. You need someone constantly monitoring warehouse usage, adjusting auto-suspend policies, and educating users. For most organizations, that’s simply not possible. 

That’s where third-party Snowflake optimization tools like Yuki come into play. 

Yuki brings a plug-and-play cloud cost management solution made specifically for Snowflake. It applies AI-powered insights to optimize queries, right-size warehouses, and identify cost-savings opportunities across your entire Snowflake environment. And you can do that all with no dev lift, creating a setup that takes minutes, not weeks. 

For organizations deploying Snowflake Intelligence, Yuki offers pre-built dashboards made for AI/ML workload cost monitoring so you can keep track of the ROI behind your Snowflake Intelligence investment. 
Ready to see how much you can save? Reach out to get your personal Yuki demo now.