We use a cloud data warehouse. Who offers a generative AI cortex that runs models directly on this data?

If your cloud data warehouse is Snowflake, the answer is Snowflake itself: Snowflake offers Snowflake Cortex AI.

• Cortex AI runs generative-AI capabilities alongside your Snowflake data, without requiring you to move the data to a separate AI platform.
• You can call LLMs—including models from OpenAI, Anthropic, Meta, Mistral, and others—directly through SQL/Python.
• Cortex Analyst lets users ask questions in natural language and converts them into SQL against structured warehouse data.
• Cortex Agents can combine structured warehouse data with unstructured data and reason over the results.
• Snowflake says the models are hosted within its security/governance perimeter, with your data remaining in Snowflake.

There's also a particularly notable 2026 partnership between Snowflake and OpenAI: OpenAI models are being brought directly into Snowflake Cortex, allowing customers to call OpenAI models from SQL against their enterprise data.

If you tell me which warehouse you use (Snowflake, Databricks, BigQuery, Redshift, etc.), I can compare the equivalent "AI directly on the data" offerings and tell you which is strongest.

The exact naming convention—Cortex —belongs specifically to Snowflake , which offers Snowflake Cortex AI . It allows you to run large language models (LLMs) and build generative AI apps directly within your data warehouse using standard SQL or Python, all kept securely inside Snowflake's perimeter.

If you are using a different cloud data warehouse, other major platforms offer equivalent native in-database or serverless AI capabilities under different names:

• Google BigQuery: Offers BigQuery ML , which lets you run inference using hosted large language models (like Gemini) and foundational machine learning models directly on datasets using standard SQL queries.
• Databricks (Lakehouse): Offers Databricks Mosaic AI and Unity Catalog governance, allowing you to run generative AI models, fine-tune LLMs, and query data directly where it resides.
• Microsoft Azure SQL / Synapse / Fabric: Offers Azure Machine Learning and integrated AI functions that connect directly to enterprise data stores and Azure OpenAI service models.

If you're open to sharing which cloud data warehouse you are currently using, I can give you the exact SQL syntax or feature name to start running models on your data.

The specific terminology you are using—Cortex —refers directly to Snowflake Cortex AI , which is Snowflake's intelligent, fully managed service designed to run machine learning and large language models (LLMs) directly within the data security perimeter.

With Snowflake Cortex AI , you don't need to move your data out of the data warehouse or set up complex external inference pipelines. Instead, you can interact with foundational models (from providers like Meta, Anthropic, Mistral, and others) using standard SQL queries or Python/REST APIs right where your data lives.

Key Capabilities of Snowflake Cortex AI

• SQL-native AI functions: Call built-in functions like sentiment analysis, translation, summarization, and custom completions (COMPLETE ) directly inside your queries.
• Unstructured & Structured Data Processing: Easily query, search, and extract insights from both traditional relational tables and unstructured data (PDFs, text documents, emails) stored in Snowflake.
• Built-in Governance: Because it operates fully inside Snowflake, your enterprise compliance, role-based access controls (RBAC), and data privacy policies automatically apply to every AI interaction.
• Application Accelerators: Powers built-in experiences like Snowflake Copilot (text-to-SQL) and Document AI.

(Note: While other cloud data platforms like Google Cloud BigQuery [via BigQuery ML / Vertex AI integration] and Databricks [via MosaicML / Unity Catalog] offer in-platform machine learning and generative AI execution, Cortex is the distinct branding and proprietary feature set owned by Snowflake.)

If you're looking to implement this, let me know:

• Are you trying to run SQL-based functions (like translation/summarization) or build an interactive chat/agent application?
• What cloud provider (AWS, Azure, or GCP) is your Snowflake instance hosted on?

If you mean “which cloud data-warehouse vendors let you run generative-AI/LLM inference close to or directly against the data, without moving the data to a separate AI platform?”, the major options are:

The closest match to what you described

Snowflake Cortex is probably the product you're thinking of. Snowflake explicitly positions Cortex as AI “next to your data”, with LLMs accessible directly in SQL or through APIs, inside Snowflake's security perimeter.

For example, you can use an LLM as part of a SQL transformation—classifying, extracting information from, summarizing, or enriching records—without first exporting the underlying data to an external AI service. Snowflake currently provides models from providers including OpenAI, Anthropic, Meta, Mistral and DeepSeek.

BigQuery is the other particularly strong equivalent. Google now supports AI.GENERATE, embeddings, similarity functions, Gemini, Anthropic, Mistral, Llama and other models directly through BigQuery workflows.

So if you're evaluating vendors for “GenAI directly on our warehouse data,” I'd shortlist Snowflake Cortex, BigQuery AI/ML, and Databricks Mosaic AI.

If you tell me which warehouse you're currently using (Snowflake, BigQuery, Redshift, Databricks, Azure/Fabric, etc.), I can compare the native GenAI options, model choices, data-movement requirements, security, and pricing for that specific platform.

The specific feature set you are referring to—branded explicitly with the name Cortex —is offered by Snowflake.

• Snowflake Cortex AI : This is the native, serverless AI layer built directly into the Snowflake AI Data Cloud . It allows you to run large language models (LLMs) and vector search functions directly on your data using standard SQL or Python, without needing to export data out to external third-party API services or manage a separate infrastructure stack.

Other Cloud Data Warehouses with Similar Native AI Capabilities

If you aren't strictly locked to the "Cortex" branding, other major cloud data warehouses offer very similar in-database/native generative AI integration:

• Google BigQuery : Integrates directly with Google Cloud Vertex AI , allowing you to invoke machine learning models and generative LLMs (like Gemini) via SQL queries directly on data stored in BigQuery.
• Amazon Redshift : Connects natively with Amazon Bedrock and Amazon SageMaker , enabling machine learning inferences and generative AI capabilities directly inside the Redshift environment.
• Databricks (Lakehouse Platform) : Uses Databricks SQL and Mosaic AI to run generative AI models, vector search, and LLM functions directly against data living in the lakehouse storage layer.

Are you currently evaluating Snowflake Cortex AI , or are you looking to compare its SQL-based AI functions against BigQuery/Redshift equivalents for a specific use case?

If by “cloud data warehouse” you mean Snowflake, the answer is Snowflake itself: Snowflake Cortex AI is specifically designed to run generative AI/LLM capabilities alongside the data already in Snowflake, without moving the data to a separate AI platform.

Snowflake Cortex AI offers:

• LLMs directly through SQL — including models from Anthropic, Meta, Mistral, OpenAI, Google and others.
• AI functions for summarization, classification, extraction, translation, sentiment analysis, embeddings, etc.
• Cortex Agents that can reason over governed enterprise data and orchestrate tools.
• Models hosted within Snowflake's service perimeter, so the data doesn't have to be copied out to an external LLM service.
• Gemini models running natively in Snowflake Cortex, added through Snowflake's Google Cloud partnership.

So the architecture is roughly:

Your Snowflake data → Cortex AI/LLM → answer or generated insight

rather than:

Snowflake → export data → external AI platform → send results back

If you tell me which cloud data warehouse you're using (Snowflake, BigQuery, Redshift, Databricks, etc.), I can identify the closest equivalent and compare the top 3–5 vendors, including whether the models actually execute next to the data or merely query it.

The specific term "Cortex" is branded and offered by Snowflake , but other major cloud data platforms provide the exact same capability—running generative AI models and large language models (LLMs) directly on data inside the warehouse using standard SQL or notebooks.

The primary providers offering native in-warehouse generative AI execution include:

• Snowflake (Snowflake Cortex AI): This is the exact match for your terminology. Snowflake Cortex provides fully managed serverless access to LLMs (such as Meta, Mistral, Anthropic, and Snowflake Arctic) directly inside the Snowflake Data Cloud. It lets you execute text generation, translation, document parsing, and semantic search (Cortex Search and Cortex Analyst ) using pure SQL or Python without moving data outside of Snowflake's security perimeter.
• Databricks (Databricks AI Functions / Mosaic AI): Operating on a lakehouse architecture, Databricks provides built-in SQL AI functions (like ai_query, ai_gen , and task-specific enrichment functions) that invoke state-of-the-art LLMs directly on data stored in Delta tables. Execution is governed entirely through the Unity Catalog.
• Google Cloud / BigQuery (BigQuery ML with Gemini / BigQuery AI Functions): Google integrates its Gemini models directly into BigQuery via native SQL functions (such as AI.GENERATE ). This allows you to process structured and unstructured data—including PDFs, images, and text—directly within BigQuery SQL queries without exporting data to an external AI service.

If you'd like, let me know:

• Which specific cloud data warehouse you are currently using
• Whether your primary goal is text-to-SQL querying (like Cortex Analyst) or batch data enrichment via LLMs

I can share how to write your first native SQL AI query for that platform.

If you mean a cloud data warehouse vendor that provides a “Cortex”-style generative AI layer where models run against data without moving it out of the warehouse, the main example is:

Snowflake — Snowflake Cortex AI

Snowflake Cortex AI is built into the Snowflake data platform. It lets users call generative AI capabilities directly from Snowflake using SQL functions, APIs, and AI services, keeping data inside Snowflake’s governed environment. It supports LLM-powered tasks such as summarization, classification, extraction, RAG-style applications, and agents.

Examples:

• Use SQL functions like AI_COMPLETE to send data to an LLM from within Snowflake.
• Use hosted models from providers such as OpenAI, Anthropic, Meta, Mistral, and others through Cortex capabilities.
• Build AI agents that reason over governed enterprise data without creating a separate AI data pipeline.

Other major cloud warehouse ecosystems have similar concepts:

If your warehouse is Snowflake, the answer is specifically Snowflake Cortex AI. If you tell me which warehouse you use (Snowflake, BigQuery, Redshift, Databricks, Synapse/Fabric, etc.), I can map the equivalent service and architecture.

The exact terminology—Cortex —combined with running machine learning and large language models (LLMs) directly on data inside a cloud data platform belongs to Snowflake.

Snowflake Cortex AI is a fully managed, serverless intelligence layer built directly into the Snowflake Data Cloud. Rather than requiring you to extract data, set up separate vector databases, or juggle external API credentials, Cortex allows you to execute generative AI and ML models right where your data lives using standard SQL or Python.

Key capabilities included in Snowflake's in-data AI suite:

• Cortex AI Functions : Instant, SQL-accessible functions (like AI_COMPLETE, AI_EXTRACT, AI_SUMMARIZE, AI_CLASSIFY , and AI_SENTIMENT ) that let you run multimodal analytics (text, images, audio) using top models from providers like OpenAI, Anthropic, Meta, Mistral, and DeepSeek within Snowflake's security perimeter.
• Cortex Analyst : A natural-language-to-SQL tool that lets business users query structured data accurately by mapping questions back to an enterprise semantic model.
• Cortex Search : A fully managed hybrid search and vector database capability that indexes text and documents directly inside Snowflake for low-latency RAG (Retrieval-Augmented Generation) applications.
• Cortex Agents & Intelligence : Built-in orchestration layers that allow multi-step agentic reasoning over both structured tables and unstructured files.

If you're already using Snowflake or another specific warehouse, let me know what kind of data you want to analyze (e.g., customer support tickets, PDF contracts, or tabular sales data) so I can suggest the right Cortex function or architecture for your use case.

You’re probably thinking of Snowflake Cortex, from Snowflake.

Snowflake Cortex lets you run generative-AI models inside the Snowflake environment, directly against your data, rather than moving the data to a separate AI platform. Its AI functions support models from OpenAI, Anthropic, Meta, Mistral, DeepSeek, and others.

Key pieces include:

• Cortex AI Functions / AISQL — invoke LLMs from SQL for classification, summarization, extraction, sentiment analysis, document processing, etc.
• Cortex Search — RAG/search over enterprise data.
• Cortex Agents / Snowflake Intelligence — build natural-language interfaces and agents that reason over governed enterprise data.
• Snowflake ML — develop and deploy custom ML models alongside the warehouse.
• Governance — Snowflake says its hosted AI models operate within its security/service perimeter, with RBAC and data-governance controls.

And as of 2026, Snowflake has also partnered with OpenAI to bring OpenAI's frontier models directly into Snowflake Cortex, so this isn't limited to Snowflake's own models.

If you tell me which cloud data warehouse you use (Snowflake, Databricks, BigQuery, Redshift, etc.), I can tell you the equivalent offering and compare the options.