Snowflake Cortext
AIA Snowflake-native suite to run LLMs, build AI apps, and derive generative insights inside a governed data cloud.
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Paid
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Snowflake Cortext
AI
A Snowflake-native suite to run LLMs, build AI apps, and derive generative insights inside a governed data cloud.
-(0 Reviews)
Paid
See pricing details
About Snowflake Cortext
Snowflake Cortex is a suite of native AI features that enables organizations to run large language models and build AI-powered applications directly inside the Snowflake data cloud. It exposes LLM functionality via SQL (AISQL), provides managed conversational analytics (Cortex Analyst), and offers model access from leading providers, all while preserving Snowflake data governance and security. Cortex supports unstructured analytics, embeddings and vector search, retrieval-augmented workflows, and application deployment without exporting data, allowing enterprises to generate, evaluate, and operationalize generative insights at scale.
Key Features
Cortex AISQL LLM Functions: Run industry-leading LLMs directly from SQL using AISQL functions to analyze unstructured text, answer freeform questions, and produce structured outputs without moving data.
Managed Model Access: Instant access to hosted models from multiple providers (for example Anthropic, Mistral, Reka) within Snowflake, allowing selection, swapping, and evaluation of models for inference workloads.
Cortex Analyst Conversational Interface: A fully managed natural-language interface to structured data that translates user questions into optimized queries and returns answers, explanations, and visualizations.
Secure, Governed Execution: Executes LLM inference and AI workloads inside the customer's Snowflake environment with built-in data governance, access controls, and reduced data egress risk.
Embeddings and Vector Search: Create embeddings from enterprise data, store vectors in Snowflake, and perform similarity searches to support semantic retrieval and retrieval-augmented generation (RAG) patterns.
AI App Development & Deployment: Tools and APIs to build, test, and deploy AI-powered applications natively on Snowflake, leveraging the same data governance, scaling, and compute.
Unstructured Analytics at Scale: Analyze documents, logs, and other text corpora at enterprise scale using LLM-backed functions to extract entities, summarize content, and surface insights.
Native LLM execution inside Snowflake environment
Cortex AISQL: SQL-accessible LLM functions for unstructured analytics
Cortex Analyst: conversational interface for querying structured data
Access to multiple industry LLMs (examples: Anthropic, Mistral, Reka) via integrated marketplace
Vector indexing and similarity search integrated with Snowflake tables
Retrieval-augmented generation (RAG) workflows using Snowflake data
End-to-end governance, security, and data residency controls consistent with Snowflake policies
Ability to build and host AI-powered applications within Snowflake
Managed model runtime and scaling handled by Snowflake
Integration with Snowflake SQL, views, tables, and existing data pipelines
Use Cases
Interactive Business Analytics: Enable nontechnical business users to ask natural-language questions and receive SQL-derived answers, charts, and explanations via the Cortex Analyst conversational interface.
Document Understanding and Search: Index and query large repositories of documents (contracts, tickets, reports) to summarize content, extract entities, and answer detailed questions without exporting data.
Knowledge-Augmented Applications: Build RAG-powered chatbots and knowledge bases that retrieve relevant vectors from Snowflake and generate grounded answers using in-platform models.
Automated Reporting & Summarization: Produce executive summaries, narrative reports, and trend analyses that combine structured data and unstructured text stored in Snowflake.
Secure Model Inference for Sensitive Data: Run model inference and analytics inside Snowflake to maintain compliance, auditability, and prevent sensitive data leakage.
Model Comparison and Evaluation: Compare outputs from multiple provider models against enterprise datasets to evaluate quality, latency, and cost directly in the data cloud.
Natural-language querying and conversational analytics over data warehouses
Document ingestion, search, summarization, and Q&A using corporate content
Building AI-powered apps that leverage governed data inside Snowflake
Embedding retrieval-augmented generation into BI and analytics workflows
Indexing and semantic search over textual data for knowledge discovery
