DEV Community: Tatsuya

Just Before Snowflake Summit 26: From an AI Perspective - The Current State of "Snowflake - The AI Data Cloud"

Tatsuya — Mon, 18 May 2026 01:08:03 +0000

Note:
This article represents my personal views and not those of Snowflake.

Information regarding Snowflake Summit 26

Snowflake Summit 26 will be held in San Francisco from June 1 to June 4, 2026. Various updates are expected to be announced, so please stay tuned.

Snowflake Summit 26

Explore the future of data, AI, and applications at Snowflake Summit 26. Join experts, attend workshops, and get SnowPro certified while learning new data trends.

snowflake.com

I am planning to attend in person, so I look forward to meeting anyone who will be there!

Introduction

As mentioned at the beginning, Snowflake Summit 26 is finally approaching next month. In this article, I would like to provide an overview of the current standing of Snowflake - The AI Data Cloud from an AI perspective and broadly organize the situation as of May 17, 2026. In my professional role, I have had many opportunities to discuss these topics recently, so I decided to verbalize them in an article. Please note that this article is intended to summarize the overall picture and may not cover every single detail. Let us dive in!

There is No AI Strategy Without a Data Strategy

"There is no AI strategy without a data strategy" - Interestingly, the reason I chose to join Snowflake about a year ago was very close to this. Even then, there were many requests to utilize AI in the enterprise, but I felt that a well-organized and easy-to-use data foundation was necessary for that. Recently, keywords like ChatGPT and Claude have become common, but these services—and the models behind them—do not inherently possess the "context of the company trying to use them" in their raw state. They become more useful within a company by combining them with internal data. However, to do so, it is necessary to prepare the data foundation and utilize it as appropriate context.

Given this situation, I believe "There is no AI strategy without a data strategy" is a keyword that truly captures the essential point.

Two Pillars of AI Utilization in Snowflake

Regarding how AI can be utilized in Snowflake under these circumstances, I believe there are two main pillars. These are "Making data AI-Ready/analyzable" and "Using AI-Ready data."
In the diagram above, these correspond to the left and right sides of "AI Ready GOLD." Let me organize these two pillars below.

AI Utilization for Making Data AI-Ready/Analyzable

Snowflake is expanding its features to enable the use of AI in various ways to make data AI-Ready or analyzable.

Data Engineering Perspective

It is said that 80% of the world's data is unstructured data, such as images, videos, and PDFs. Snowflake is also expanding various functions to handle this unstructured data. By using features like AI Functions and the Document Processing Playground, it is possible to convert unstructured data into analyzable data.

AI Functions include the following types of functions:

By using these, for example, it is possible to perform the following processing based on audio recording data:

This allows various data points to be generated from a single recording and treated as analytical dimensions.

Handling Document Data

Furthermore, it is now possible to handle various document data, including PDFs. By using the AI_PARSE_DOCUMENT function, the following conversion process can be performed:

AI_PARSE_DOCUMENT supports various file formats such as PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, and TXT.

An update on May 4, 2026, enhanced the OCR mode of AI_PARSE_DOCUMENT, improving the accuracy of OCR mode and multilingual support. Please give it a try.

May 04, 2026: AI_PARSE_DOCUMENT OCR quality improvements

Additionally, the Document Processing Playground feature has been released. This feature helps you by suggesting what SQL or Python code to write when uploading document data to extract necessary information.

Updates for these functions handling document data are frequent, and features such as figure/table extraction have recently been released. These functions are also introduced in the following blog posts (Note: Links are to Japanese articles).

By vectorizing the data extracted using these functions, semantic search based on meaning becomes possible. This can be used with the Cortex Search feature, which will be described later.

Handling PII (Personally Identifiable Information)

The AI_REDACT AI Function for processing personal information has also been released.
This function identifies and masks PII contained within text. The image below illustrates the concept:

Handling Video and Audio Data

Additionally, as of May 6, 2026, although in Public Preview (PuPr) status, it has become possible to directly handle video and audio data using specific models in the AI_COMPLETE function. While there are limitations on models and file sizes, the patterns for approaching unstructured data have increased further.

May 04, 2026: Multimodal video and audio analysis for marketing and brand insights (Public Preview)

A detailed analysis of the results can be found in the blog post "Beyond Transcription - Multimodal Video and Audio Analysis with Snowflake AI_COMPLETE" so please take a look.

Regarding audio data, it was already possible to convert it to text using the AI_TRANSCRIBE function, but the variations have now expanded.

A great advantage of these AI Functions is that they can be executed as part of SQL close to the data, allowing for efficient processing of large volumes of data.
In this way, Snowflake provides various services for handling unstructured data, enabling conversion into AI-Ready and other easily analyzable formats. I believe this reflects a design philosophy of "AI utilization close to the data."

AI Utilization for People and Systems to Use AI-Ready Data

For data organized in the manner described above, Snowflake provides various ways to utilize that data. While traditional data handling using SQL and BI tools remains possible, AI has come to play a significant role in the data utilization field. Snowflake provides the following major services as related functions:

Services for searching data: Cortex Search, Cortex Analyst
Services for developing arbitrary AI Agents to search data or gain insights: Cortex Agents
Applications for working with data: Snowflake Intelligence, Cortex Code in Snowsight / CLI
Integration with other AI tools: Snowflake-managed MCP Server

Services for Searching Data

Cortex Search

Cortex Search is a hybrid search service supporting both keyword and vector search. Cortex Search is receiving useful updates, such as Cortex Search Multi-index and Index-specific Boosts to enhance search flexibility and accuracy, as well as Cortex Search Batch, which covers use cases for processing large volumes of queries in batches, such as entity resolution, catalog mapping, and deduplication.

Cortex Analyst

Cortex Analyst is a service for achieving Text-to-SQL (converting natural language into SQL). It utilizes defined Semantic Views and leverages AI to perform searches against table data.
In analytical tasks and AI-Ready data utilization, the Semantic Layer is a crucial element. In Snowflake, this feature is named "Semantic View," allowing for the organization of metadata such as logical tables, analytical dimensions, and items to be aggregated. Additionally, Snowflake has a feature called Semantic View Autopilot that automatically generates these "Semantic Views." Currently, generation is in English, so care is needed, but it is a feature that assists in the often labor-intensive task of preparing Semantic Views.

A part of the Semantic View Autopilot feature includes automatically generating Semantic Views by importing definition information from other tools. For example, it can read a Tableau Workbook to automatically generate a Semantic View.

Furthermore, Snowflake is advancing an initiative called the Open Semantic Interchange (OSI) Initiative, working with various partners to standardize the Semantic Layer. The repository for this activity is below:

open-semantic-interchange/OSI

Services for Developing Arbitrary AI Agents to Search Data or Gain Insights

Cortex Agents

Cortex Agents is a service for building AI Agents on Snowflake. It functions as an AI Agent that operates by linking data search services like Cortex Search and Cortex Analyst with custom tools such as stored procedures. In a recent update, Cortex Agents added support for Agent skills, improving execution efficiency for repetitive tasks, and support for MCP Connectors, allowing integration with external tools and services.

Cortex Agents can be used not only within Snowflake Intelligence but also via REST API or through a Snowflake-managed MCP Server, allowing calls from any application or AI Agent.
Examples of integration include:

Integration with Teams/Microsoft 365 Copilot: Cortex Agents for Microsoft Teams and Microsoft 365 Copilot
Integration example with Amazon Q (Simple hands-on): Multi-Agent Supply Chain Orchestrator with Snowflake Cortex MCP and Amazon Q Business
Integration example with Microsoft AI Foundry (Simple hands-on): Multi-Agent Agentic Orchestrator with Snowflake Cortex MCP and Microsoft AI Foundry

Recently, on April 13, 2026, an update was made allowing Cortex Agents to directly reference Semantic Views and generate SQL without going through Cortex Analyst. This update is expected to improve the accuracy of analytical queries and reduce latency.

Apr 13, 2026: Improved SQL generation in Cortex Agents

Applications for Working with Data

Snowflake Intelligence

Snowflake Intelligence is a service that allows users to perform deep dives into data by querying the organized data foundation on Snowflake using natural language. Cortex Agents operate behind this application. With the support for Agent Skills and MCP Connectors mentioned above, these can also be utilized within Snowflake Intelligence. Furthermore, as mentioned in recent press releases, there are many feature updates for business use, such as the DeepResearch feature for in-depth investigations (scheduled for PuPr soon) and the Artifact feature for managing and sharing generated charts and tables (scheduled for GA soon).

Snowflake Intelligence is also used internally at Snowflake, with over 6,000 members across various roles using the tool. A behind-the-scenes look at this build has been published on Medium as "From Data Maze to Intelligence Layer: GTM AI Assistant with Semantic Views on Snowflake Intelligence".

Cortex Code in Snowsight / CLI

Cortex Code in Snowsight / CLI is an AI Agent tool that supports various development tasks on Snowflake. In addition to such tasks, it can be used for data discovery via Horizon Catalog (Snowflake's native catalog feature), allowing for data exploration using Cortex Code against the data organized on Snowflake.

Similar to Snowflake Intelligence, Cortex Code is utilized by internal members at Snowflake. The appearance of these AI tools seems to have increased the utilization of the internal data foundation. In fact, I have personally started using Cortex Code frequently to utilize internal data.

Recent updates for Cortex Code include:

Support for more external data systems like AWS Glue, Databricks, and Postgres
Support for MCP and Agent Communication Protocol (ACP)
New VS Code extension (in Private Preview) and Claude Code plugin
New Agent Software Development Kit (SDK) supporting Python and TypeScript, allowing Cortex Code features to be integrated directly into your own apps or workflows
Updates to Cortex Code on Snowsight:
- Cloud Agents (in Private Preview) allow users to execute code and run workflows directly in the browser
- New Plan Mode allows for previewing and approving workflows before execution
- Snap & Ask feature allows direct interaction with data artifacts like charts and tables

For Claude Code users, please try the snowflake-cortex-code plugin mentioned above.

Snowflake Cortex Code - Claude Marketplace

Integration with Other AI Tools

Snowflake allows several functions to be published as a Managed MCP Server on Snowflake.

This MCP Server can be used as a remote MCP Server. It can be utilized from AI Agent tools acting as MCP Clients, including ChatGPT, Claude Desktop, and Claude Code.

Security and Governance Supporting AI Utilization

The above summary focused primarily on AI for data utilization on Snowflake. Snowflake also covers security and governance elements in this AI utilization. As the use of AI advances further, these elements will become extremely important.

I personally consider Snowflake RBAC (Role-Based Access Control) to be very powerful. As shown in the diagram above, when using various services and features related to AI, the same RBAC managing the data is applied, and access control is performed based on the roles linked to the user. Since all answers reflect only the data the user has permission to view, it is possible to utilize AI and data under appropriate governance.

Furthermore, Snowflake has observability features centered on AI Observability. For example, various logs related to Cortex Agents are managed here. Additionally, a quality evaluation feature called Cortex Agents Evaluation has been released, allowing for the quality assessment of AI Agents using the GPA framework. The GPA framework evaluates AI Agent quality based on Goal (is the user's objective eventually achieved?), Plan (do planning and re-planning provide effective high-level instructions?), and Act (do the agent's actions follow the plan, call tools appropriately, and progress toward the goal?).

From a budget management perspective, budget management features for Cortex Agents / Snowflake Intelligence have been released (GA on April 10, 2026). This allows for:

Flexible tag-based scope settings - management by department or project
Multi-stage threshold settings - defense-in-depth combining ACTUAL and PROJECTED
Automated access control - automatic response when thresholds are reached
Cycle-based management - automatic reset/recovery at the start of the month

Other features like row-level policies also serve as a foundation, with various AI-related elements being built upon them.

Updated on May 17, 2026

On May 14, 2026, Cortex AI Guardrails expanded its support beyond Cortex Code to include Snowflake Intelligence and Cortex Agents. Cortex AI Guardrails is part of the Snowflake Horizon Catalog and is designed to use context-based reasoning to detect and neutralize threats such as indirect prompt injections embedded in tool calls. Although the configuration is applied at the account level, it can be easily enabled by changing the AI_SETTINGS parameter.

May 14, 2026: Cortex AI Guardrails support for Snowflake Intelligence and Cortex Agents (General availability)

This enhancement enables you to use these capabilities even more securely, so we encourage you to consider adopting it as well.

Conclusion

Recently, the paper that marked the beginning of Snowflake, "The Snowflake Elastic Data Warehouse," received the SIGMOD Test-of-Time Award.

I personally feel that the evolution of Snowflake as a service over the years is proving its worth in the current era of AI.
In transforming enterprise operations, data and AI are inseparable. Snowflake provides Cortex AI based on the concepts of being easy, efficient, and trusted. I hope you will fully utilize Snowflake's capabilities as a platform where you can easily and efficiently leverage AI while maintaining governance and security close to your data.
Snowflake has many feature updates, and it is difficult to cover them all, but I hope this article helps you grasp the overall current state of AI utilization in Snowflake.

And as mentioned at the beginning, Snowflake Summit 26 will finally be held from June 1 to June 4, 2026. I am very much looking forward to what will be announced!

Appendix

Snowflake server release notes and feature updates

Recent feature releases are summarized on this page. Please take a look.

Snowflake What's New Distribution (by tsubasa-san)

tsubasa-san distributes update information for Snowflake's "What's New" on X. Please follow to catch up on the latest information.

Japanese Version

Snowflake What's New Bot (Japanese)

x.com

English Version

Snowflake What's New Bot (English)

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Japanese version

Snowflake Summit 26 直前: AI 軸で見る Snowflake - The AI Data Cloud の現在地

zenn.dev

Revision History

May 17, 2026: Initial post

[CKE & Snowflake Intelligence] Smart AI-Powered Search for Snowflake Documentation!

Tatsuya — Mon, 27 Oct 2025 08:15:57 +0000

Introduction

Cortex Knowledge Extensions (CKEs) became Generally Available (GA) on August 12, 2025. This was announced in the official blog.

The Easy Button for Context-Rich AI Agents

Discover how Snowflake Intelligence and Cortex Knowledge Extensions help enterprises unlock insights from data within their organizations as well as third-party expertise.

snowflake.com

This article will provide an overview of CKEs and introduce various ways to utilize them.

Usage Example

After acquiring the CKEs for Snowflake Documentation and configuring Snowflake Intelligence (currently in Public Preview as of October 27, 2025), it can provide answers that reference the CKEs as shown above. The setup is very simple and will be explained later in this article.

What are Cortex Knowledge Extensions (CKEs)?

First, let's explore what Cortex Knowledge Extensions (CKEs), which became GA, actually are. According to the Cortex Knowledge Extensions documentation:

Cortex Knowledge Extensions (CKEs) are Cortex Search Services that can be shared on the Snowflake Marketplace or via private listings or organizational listings. They can be used in a retrieval-augmented generation (RAG) architecture to integrate licensed and proprietary content into Cortex AI applications. For example, CKEs can be used to integrate knowledge from unstructured content, such as articles, market research, books, or forum posts, into Cortex AI applications, such as chatbots and agentic systems.

As stated in the documentation, CKEs are essentially "Cortex Search" services shared via Snowflake Marketplace, private listings, or organizational listings.
CKEs listed on the Snowflake Marketplace can be found here:
Snowflake Marketplace

Various data providers have started offering CKEs, including Snowflake-provided ones like "Snowflake Documentation" and "PubMed Biomedical Research Corpus".

How CKEs Work

As described in the documentation, the mechanism is as follows:

Providers (CKE providers) upload text data to tables in their accounts and create Cortex Search services on those tables. These Cortex Search services are shared on the Snowflake Marketplace. The Cortex Search services shared on Snowflake Marketplace become Cortex Knowledge Extensions (CKEs). When using these CKEs, you can develop applications by combining them with Cortex AI SQL or Cortex Agent API.

As mentioned earlier, CKEs are essentially Cortex Search services, which means they can be used in various ways. Let's explore what forms of utilization are possible.

Use Cases

Here are some examples of how CKEs can be integrated and used.

Snowflake Intelligence

Integration with Snowflake Intelligence is straightforward. Following the procedure outlined in the Quickstart guide below, you can register the CKE's Cortex Search as an "agent" to make it immediately available in Snowflake Intelligence.
Getting Started with Snowflake Intelligence

Snowflake Intelligence is a great option when you want to make Cortex Agent, Cortex Analyst, and Cortex Search widely available within your organization.

Snowflake Cortex AI MCP Server

"Snowflake Cortex AI MCP Server" is available on Snowflake-Labs. By configuring it to use CKE's Cortex Search, you can utilize it with any MCP client (Claude Desktop, Cursor, gemini-cli, etc.).
Snowflake Cortex AI Model Context Protocol (MCP) Server

Snowflake Managed MCP Servers

Snowflake Managed MCP Servers were released as a preview on October 1, 2025. This allows you to host Cortex Analyst / Cortex Search as remote MCP Servers on Snowflake.

Introducing Snowflake Managed MCP Servers for Secure, Governed Data Agents

Snowflake Managed MCP Servers, an open-standards interface for AI agents to access your governed data without custom integrations or separate infrastructure.

snowflake.com

Setting Up for Use with Snowflake Intelligence

In this article, let's configure CKEs for use with Snowflake Intelligence.

Prerequisites

To use CKEs, the easiest way to start is with CKEs published by Snowflake itself, such as their Documentation.
After logging into your Snowflake account, access this URL and acquire it.

Configuring Cortex Agent

To use Cortex Search with Snowflake Intelligence, you need to configure a Cortex Agent. While you can set this up via the GUI in Snowsight, it's also possible to define it using SQL. For documentation on handling Cortex Agent with SQL, please refer to this page.
After acquiring Snowflake Documentation from the Snowflake Marketplace, executing the following SQL will make it available in Snowflake Intelligence.

Note that cross-region inference is enabled in the SQL, so please be aware of this.

details Cortex Agent for CKEs Setup SQL:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';

CREATE DATABASE IF NOT EXISTS snowflake_intelligence;
GRANT USAGE ON DATABASE snowflake_intelligence TO ROLE PUBLIC;

CREATE SCHEMA IF NOT EXISTS snowflake_intelligence.agents;
GRANT USAGE ON SCHEMA snowflake_intelligence.agents TO ROLE PUBLIC;

GRANT CREATE AGENT ON SCHEMA snowflake_intelligence.agents TO ROLE PUBLIC;

CREATE OR REPLACE AGENT SNOWFLAKE_INTELLIGENCE.AGENTS.CKE_SNOWDOC_AGENT
WITH PROFILE='{ "display_name": "Snowflake Document" }'
    COMMENT=$$ This is an agent that can answer questions about Snowflake Documentation. $$
FROM SPECIFICATION $$
{
  "models": {
    "orchestration": ""
  },
  "instructions": {
    "response": "# Persona\nYou are a Snowflake expert consultant. Act as an experienced professional supporting enterprise customers.\n\n# Tone & Manner\n- Always use polite language and provide accurate, concise answers.\n- Add brief explanations for technical terms when necessary, so that even beginners can understand.\n\n# Strict Rules for Response Generation\n1. Strict adherence to sources: All answers must be based on Snowflake's official documentation. Do not include personal opinions or information that cannot be verified in the documentation.\n2. Clear source attribution: At the end of your response, include a section titled [Reference Documentation] and list the URLs of referenced pages in bullet points.\n3. Formatting:\n- Enclose code such as SQL, Python, JSON, etc., in code blocks with language specification (e.g., ```

sql ...

 ```).\n- Organize procedures and lists using bullet points (-) or numbered lists (1.) for readability.\n- Emphasize important keywords such as function names and parameter names with `backticks`.\n4. Handling unclear questions:\n- If an answer to a question cannot be found in the official documentation, honestly respond with \"I was unable to find a direct answer from the official documentation for your question.\"\n- Then, provide related information by saying \"Here are potentially relevant documents\" or suggest next steps like \"You might get answers from other experts by asking in the Snowflake Community.\"\n5. Prohibited actions:\n- Do not answer specific amounts regarding pricing or licenses; always provide a link to the official pricing page.\n- When asked about security-related configurations or personal information, only provide general best practices and never present specific configuration values or prompt for information input.\n\n\n",
    "sample_questions": [
      {
        "question": "Tell me how to set up Snowflake Openflow"
      },
      {
        "question": "What is the current status of Snowflake's Iceberg support?"
      },
      {
        "question": "What LLM models are available in Snowflake?"
      },
      {
        "question": "Which clouds and regions can be used when using Snowflake in Japan?"
      }
    ]
  },
  "tools": [
    {
      "tool_spec": {
        "type": "cortex_search",
        "name": "Search Snowflake Documents",
        "description": "Cortex Search Knowledge Extensions for searching Snowflake documentation"
      }
    }
  ],
  "tool_resources": {
    "Search Snowflake Documents": {
      "id_column": "SOURCE_URL",
      "max_results": 5,
      "name": "SNOWFLAKE_DOCUMENTATION.SHARED.CKE_SNOWFLAKE_DOCS_SERVICE",
      "title_column": "DOCUMENT_TITLE"
    }
  }
}
$$;

Using with Snowflake Intelligence

After executing the above SQL, navigate to "AI & ML" > "Snowflake Intelligence" as shown below to access Snowflake Intelligence.

After launching Snowflake Intelligence, select "Snowflake Document" and ask a question to receive results.

Conclusion

CKE is a service that allows you to easily acquire and utilize knowledge bases provided on the Snowflake Marketplace. When utilizing created Cortex Search services, you can choose different approaches based on your use case: Streamlit for building custom UIs, Snowflake Intelligence for deploying to various users within your organization, or MCP Server for data scientists, data engineers, and application developers. We hope you'll utilize CKE and Cortex Search according to your specific use cases.

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Update History

October 27, 2025: New post

gpt-oss, GPT-5 is here!: A Guide to Snowflake's Cross-Region LLM Inference

Tatsuya — Thu, 14 Aug 2025 10:50:03 +0000

Introduction

Hello everyone. I work as a Partner Solution Engineer at Snowflake, and I plan to use this dev.to platform to share things I've tried and insights I've gained in my daily work. Today, I'd like to organize my thoughts on Snowflake's "Cross-region inference" feature.

Note:
This article represents my personal views and not those of Snowflake.

`gpt-oss` and `GPT-5` Released!

As you may have seen on social media, these models have been making waves. These models are also available for use on Snowflake.
Announcing OpenAI GPT-5 on Snowflake Cortex AI

🚀 OpenAI released GPT-5 Today! And Snowflake️ started providing Day-0 support! ❄️ Give it a try GPT-5 family in Snowfake! Read more about here https://lnkd.in/gRE3Ucpa | Sho Tanaka

🚀 OpenAI released GPT-5 Today! And Snowflake️ started providing Day-0 support! ❄️ Give it a try GPT-5 family in Snowfake! Read more about here https://lnkd.in/gRE3Ucpa

linkedin.com

Let's Try Using Them on Snowflake Right Away

Let's try the following on a freshly created Snowflake account in the AWS US East (N. Virginia) region:

SELECT AI_COMPLETE('openai-gpt-5', 'Please tell me three advantages of Snowflake');

Note:
The AI_COMPLETE is a function that generates responses from text or images using supported LLMs.

However, the following error message appears and it cannot be executed:

100351 (P0000): Request failed for external function COMPLETE$V6 with remote service error: 400 '"The model you requested is unavailable in your region. To access it, enable cross region inference with AZURE_US, ANY_REGION. For more information, see https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference."

Actually, these gpt-oss and GPT-5 models currently have limited availability in terms of cloud providers and regions on Snowflake. That's why the above error occurs. However, by using the "cross-region inference" feature, it becomes possible to use them across cloud providers and regions.

What is Cross-Region Inference?

On Snowflake, various LLMs (Large Language Models) can be used through Cortex AI, a service that enables the use of generative AI on Snowflake. However, some of these models are available only on specific cloud providers or regions, and there may be cases where they cannot be used directly on the cloud provider or region where your Snowflake account is running.

Therefore, Snowflake provides a feature called cross-region inference, which enables the use of LLMs across cloud providers and regions. "Cross-region inference" is a feature announced on August 9, 2024. The announcement blog can be found here: Announcing Cross-region Inference on Snowflake Cortex AI.

This feature specifies a region that can process inference requests when the inference request cannot be processed by the cloud provider and region where the Snowflake account is running.
This is controlled by a parameter called CORTEX_ENABLED_CROSS_REGION. The default value (i.e., immediately after creating a Snowflake account) is DISABLED. In this state, only models available on the cloud provider and region where the Snowflake account is running can be used.

This parameter can be changed with the following command:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AZURE_US';

With the above setting, in addition to the cloud provider and region where the Snowflake account is running, models available in AZURE_US can also be used.

For example, even if a Snowflake account is running in the AWS US East (N. Virginia) region, this setting makes it possible to use models available in AZURE_US. The documentation explaining the cross-region inference feature can be found here.

OpenAI Model Releases on August 5 and 7, 2025, and Their Use on Snowflake

As mentioned at the beginning of this article, OpenAI released models consecutively on August 5 and 7, 2025, and these became available on Snowflake immediately. Snowflake has also made announcements about this.

August 5, 2025: OpenAI announced gpt-oss
- OpenAI's announcement: Introducing gpt-oss
- Snowflake's announcement: LinkedIn post
August 7, 2025: OpenAI announced GPT-5
- OpenAI's announcement: GPT-5
- Snowflake's announcement: Announcing OpenAI GPT-5 on Snowflake Cortex AI

These models can be easily used with syntax like the one mentioned at the beginning.
However, as of August 14, 2025, these models have limited availability in terms of cloud providers and regions. The regional support status is specifically documented here.
For example, looking at the gpt-oss and GPT-5 mentioned in this article, the support status is as follows:

openai-gpt-5, openai-gpt-5-mini, openai-gpt-5-nano are available in "Cross Cloud (Any Region)" and Azure US (Cross-Region)
openai-gpt-oss-120b, openai-gpt-oss-20b are available in "Cross Cloud (Any Region)"

Note that each is in "In preview" status, and for gpt-oss, as of August 14, 2025, only "Cross Cloud (Any Region)" is available.

Configuration Method for Cross-Region Inference

The configuration method for using cross-region inference is as follows, as mentioned earlier:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AZURE_US';

There are variations for the AZURE_US part, as follows. The documentation is summarized here.

DISABLED: Default value. Only models available on the cloud provider and region where the Snowflake account is running can be used
ANY_REGION: All models available in all regions that Snowflake supports, including the cloud provider and region where the request is made, can be used

It's also possible to specify specific regions without specifying ANY_REGION. In that case, as of August 14, 2025, the following variations are available:

AWS_APJ, AWS_EU, AWS_US, AZURE_EU, AZURE_US: These values can be specified simultaneously with comma separation. For example, they can be set as follows:

USE ROLE ACCOUNTADMIN;
ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'AWS_US,AZURE_US';

Which regions become available when each option is specified is documented here.

For example, when specifying AWS_APJ, the following regions may be used:

The region where the request is placed.
AWS Asia Pacific (Tokyo) ap-northeast-1
AWS Asia Pacific (Seoul) ap-northeast-2
AWS Asia Pacific (Osaka) ap-northeast-3
AWS Asia Pacific (Mumbai) ap-south-1
AWS Asia Pacific (Hyderabad) ap-south-2
AWS Asia Pacific (Singapore) ap-southeast-1
AWS Asia Pacific (Sydney) ap-southeast-2
AWS Asia Pacific (Melbourne) ap-southeast-4

The current configuration status of CORTEX_ENABLED_CROSS_REGION can be checked with the following SQL:

SHOW PARAMETERS LIKE 'CORTEX_ENABLED_CROSS_REGION' IN ACCOUNT;

Considerations and Important Points

Cross-region inference has several considerations and important points, which I'd like to organize by extracting from the documentation.

Configuration Scope

First, this parameter can only be set at the Snowflake account level and cannot be set at the user level or session level. Therefore, if you plan to use Cortex AI on that account, you need to decide at the account level whether to perform cross-region inference.

Latency between regions

Latency depends on the cloud provider's infrastructure and network conditions. It's recommended to test in advance whether it can withstand your expected use cases.

Data Handling During Cross-Region Inference

User input, service-generated prompts, and output are not stored or cached during cross-region inference.

Regarding data movement, data required for inference requests is handled as follows:

When both the request source and destination regions are within AWS, data remains within the AWS global network. All data flowing through the AWS global network that interconnects data centers and regions is automatically encrypted at the physical layer
When both the request source and destination regions are within Azure, traffic remains within the Azure global network. It does not enter the public internet
When the source and destination regions are on different cloud providers, data travels over the public internet using Mutual Transport Layer Security (mTLS)

As supplementary information, let's also look at AWS and Azure information regarding data movement.
Regarding AWS communication, the "Amazon VPC FAQs" states the following:
Amazon VPC FAQs

Packets that originate from the AWS network with a destination on the AWS network stay on the AWS global network, except traffic to or from AWS China Regions.In addition, all data flowing across the AWS global network that interconnects our data centers and Regions is automatically encrypted at the physical layer before it leaves our secured facilities. Additional encryption layers exist as well; for example, all VPC cross-region peering traffic, and customer or service-to-service Transport Layer Security (TLS) connections.

Regarding Azure communication, "Microsoft global network" states the following:
Microsoft global network

So, does that mean all traffic when using Microsoft services? Yes, any traffic between data centers, within Microsoft Azure or between Microsoft services such as Virtual Machines, Microsoft 365, XBox, SQL DBs, Storage, and virtual networks routes within our global network and never over the public Internet. This routing ensures optimal performance and integrity.

Pricing

Using LLMs consumes credits. Credits are considered consumed in the requesting region. For example, if you call an LLM function from the us-east-2 region and the request is processed in the us-west-2 region, credits are considered consumed in the us-east-2 region.
For information on how many credits are used for each LLM, please refer to the Snowflake Service Consumption Table.

Also, using cross-region inference does not incur data egress charges.

The original text is as follows, so please also refer to this:
Cross-region inference | Cost considerations

Please Also Refer to the Documentation for Other Points!

The above is an excerpt of perspectives, so please also check this documentation for other points!

Conclusion

This time, I summarized Snowflake's cross-region inference in conjunction with the release of gpt-oss and GPT-5. I hope you will make full use of the power of LLMs after carefully reviewing the considerations and important points.

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Link Summary

Parameter documentation CORTEX_ENABLED_CROSS_REGION
Cross-region inference announcement blog Announcing Cross-region Inference on Snowflake Cortex AI
Cross-region inference feature documentation Cross-region inference
Regional support status for each LLM
Credit consumption for each LLM Snowflake Service Consumption Table

Update History

August 14, 2025: New post

Japnese original version

https://zenn.dev/tatsu_tech/articles/c9a3e09f3964de

DEV Community: Tatsuya

Just Before Snowflake Summit 26: From an AI Perspective - The Current State of "Snowflake - The AI Data Cloud"

Information regarding Snowflake Summit 26

Snowflake Summit 26

Introduction

There is No AI Strategy Without a Data Strategy

Two Pillars of AI Utilization in Snowflake

AI Utilization for Making Data AI-Ready/Analyzable

Data Engineering Perspective

Handling Document Data

Handling PII (Personally Identifiable Information)

Handling Video and Audio Data

AI Utilization for People and Systems to Use AI-Ready Data

Services for Searching Data

Cortex Search

Cortex Analyst

Services for Developing Arbitrary AI Agents to Search Data or Gain Insights

Cortex Agents

Applications for Working with Data

Snowflake Intelligence

Cortex Code in Snowsight / CLI

Integration with Other AI Tools

Security and Governance Supporting AI Utilization

Conclusion

Appendix

Snowflake server release notes and feature updates

Snowflake What's New Distribution (by tsubasa-san)

Japanese Version

English Version

Japanese version

Snowflake Summit 26 直前: AI 軸で見る Snowflake - The AI Data Cloud の現在地

Revision History

[CKE & Snowflake Intelligence] Smart AI-Powered Search for Snowflake Documentation!

Introduction

The Easy Button for Context-Rich AI Agents

Usage Example

What are Cortex Knowledge Extensions (CKEs)?

How CKEs Work

Use Cases

Snowflake Intelligence

Snowflake Cortex AI MCP Server

Snowflake Managed MCP Servers

Introducing Snowflake Managed MCP Servers for Secure, Governed Data Agents

Setting Up for Use with Snowflake Intelligence

Prerequisites

Configuring Cortex Agent

Using with Snowflake Intelligence

Conclusion

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Update History

gpt-oss, GPT-5 is here!: A Guide to Snowflake's Cross-Region LLM Inference

Introduction

gpt-oss and GPT-5 Released!

🚀 OpenAI released GPT-5 Today! And Snowflake️ started providing Day-0 support! ❄️ Give it a try GPT-5 family in Snowfake! Read more about here https://lnkd.in/gRE3Ucpa | Sho Tanaka

Let's Try Using Them on Snowflake Right Away

What is Cross-Region Inference?

OpenAI Model Releases on August 5 and 7, 2025, and Their Use on Snowflake

Configuration Method for Cross-Region Inference

Considerations and Important Points

Configuration Scope

Latency between regions

Data Handling During Cross-Region Inference

Pricing

Please Also Refer to the Documentation for Other Points!

Conclusion

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Link Summary

Update History

Japnese original version

`gpt-oss` and `GPT-5` Released!