Snowflake Cortex
Snowflake Cortex is a tool that allows individuals without extensive knowledge of AI or cloud infrastructure to incorporate Language Model ...
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What is Snowflake Cortex?
Snowflake Cortex is a fully managed AI service that enables organizations to quickly analyze data and build generative AI applications directly within Snowflake's secure perimeter. It provides access to industry-leading large language models (LLMs) including Meta AI's Llama 2, Anthropic Claude, and Mistral Large 2 through serverless functions that can be invoked with single-line SQL or Python commands. The service eliminates the need for users to manage GPU infrastructure or bring their own AI models.
Key features include Cortex LLM functions for tasks like text summarization, sentiment analysis, translation, answer extraction, and text-to-SQL conversion; vector search functionality with Embed Text and Vector Distance functions for retrieval-augmented generation (RAG); Snowflake Copilot for natural language SQL generation; Universal Search for discovering data and apps; Document AI for extracting data from PDFs and documents; Cortex Analyst for converting natural language to accurate SQL; and Cortex Agents for orchestrating insights across structured and unstructured data. Developers can also use Streamlit in Snowflake to build LLM app interfaces in minutes without front-end experience, and Snowpark Container Services to deploy custom containerized workloads with GPU instances for fine-tuning open-source LLMs.
Snowflake Cortex is designed for data analysts, data engineers, developers, and business teams of all skill levels who want to incorporate AI into their analytical workflows. It is particularly valuable for organizations that already use Snowflake and want to leverage their enterprise data with generative AI without moving data outside Snowflake's governed and secure boundary. The service enables anyone who knows Python or SQL to securely build powerful LLM apps in minutes or hours instead of days or weeks.
Snowflake Cortex pricing
Pricing model: Free
Snowflake Cortex uses a purely usage-based pricing model with no fixed monthly plans or seat-based fees. Costs are measured in Snowflake credits with rates varying by service and model. Cortex LLM Functions (lightweight models) cost $0.12 per 1M tokens, while advanced models cost $5.10 per 1M tokens. Cortex Search indexing costs $0.06 per credit. Cortex Fine-Tuning costs $6.00-$12.00 per credit. Cortex Analyst is included with Snowflake Enterprise Edition at the standard $3.00/credit base rate. There is no free tier for Cortex. Teams should set up resource monitors and credit quotas to avoid surprise bills. Warehouse compute charges, data storage, and transfer fees are additional costs outside Cortex rates.
Snowflake Cortex pros
- Fully managed service with no GPU infrastructure management required
- Access to industry-leading LLMs including Llama 2, Claude, and Mistral Large 2
- Single-line SQL or Python commands to invoke AI functions
- Data stays within Snowflake's secure perimeter for enterprise governance
- Built-in vector search and semantic search functionality for RAG
- Text-to-SQL generation using the same model as Snowflake Copilot
- Streamlit integration allows building LLM app interfaces without front-end experience
- Task-specific models for summarization, sentiment detection, translation, and classification
- Snowpark Container Services enables fine-tuning open-source LLMs with NVIDIA GPUs
- Unified governance framework for securing and managing data access
- No custom integrations or front-end development required
- Serverless functions always available without provisioning infrastructure
- Cortex Analyst provides accurate natural language to SQL conversion
- Document AI processes PDFs, Word documents, and screenshots for data extraction
- Universal Search finds database objects, Marketplace data products, and documentation
- Support for prompt engineering with custom prompts via Complete function
- Native vector data type supports vector embeddings directly in Snowflake
- Three distance functions available: cosine similarity, L2 norm, and inner product
Snowflake Cortex cons
- Private preview features require contacting account team for access
- No dedicated free tier for Cortex specifically - even small experiments incur charges
- Usage-based pricing can cause bill fluctuations month to month
- Cortex LLM functions incur separate compute costs based on tokens processed
- Warehouse compute charges still apply when running Cortex functions
- Fine-tuning jobs consume credits quickly especially with larger models
- Snowpark Container Services public preview limited to select AWS regions initially
- Larger warehouses do not increase performance for Cortex LLM function queries
- Data storage and transfer fees add up for teams working with large datasets
- Credit consumption varies by model making cost estimation complex
Frequently asked questions about Snowflake Cortex
What is Snowflake Cortex?
Snowflake Cortex is an intelligent, fully managed service that offers access to industry-leading AI models, LLMs and vector search functionality to enable organizations to quickly analyze data and build AI applications. It provides a growing set of serverless functions that enable inference on industry-leading generative LLMs, task-specific models to accelerate analytics, and advanced vector search functionality - all within Snowflake's secure perimeter without requiring users to manage GPU infrastructure.
Do I need AI expertise to use Snowflake Cortex?
No, Snowflake Cortex is designed for users of all skill sets. Anyone who knows Python or SQL can securely build powerful LLM apps in minutes or hours, not days or weeks. The service abstracts away complexities like how AI models work, how to deploy LLMs, and how to manage GPU infrastructure. Analysts can instantly access specialized ML and LLM models tuned for specific tasks with just a single line of SQL or Python.
What LLM models are available in Snowflake Cortex?
Snowflake Cortex provides access to industry-leading large language models including Meta AI's Llama 2 (with 7B, 13B, and 70B model sizes available in private preview), Anthropic Claude, and Mistral Large 2. Users can choose between different models via the Complete function for prompt engineering. The service also includes task-specific models for summarization, sentiment detection, translation, classification, forecasting, anomaly detection, and contribution exploration.
How do I build an LLM app with Snowflake Cortex?
Developers can build LLM apps in minutes using Snowflake Cortex Functions with SQL or Python, Streamlit in Snowflake for creating interfaces without front-end experience, and Snowpark Container Services for custom containerized workloads. For RAG applications, use Embed Text and Vector Distance functions for vector embeddings and semantic search. Apps can be securely deployed and shared via unique URLs leveraging existing role-based access controls in Snowflake, generated with just a single click.
Is my data secure with Snowflake Cortex?
Yes, every interaction with Snowflake Cortex is processed within Snowflake's secure perimeter and governance model. The service uses Snowflake's unified security, governance and data access controls with built-in policies, access controls and end-to-end observability. Users can securely manage access to their data through Snowflake's unified governance framework with role-based access, providing explainability for audit purposes and enabling compliance with data policies without moving data outside Snowflake.
What is the difference between Cortex LLM Functions and Snowflake Copilot?
Cortex LLM Functions are serverless functions that can be called via SQL or Python for tasks like summarization, sentiment analysis, translation, text completions, and text-to-SQL generation. Snowflake Copilot is an LLM-powered assistant with a full-fledged user interface that generates and refines SQL with natural language through conversation. Copilot takes advantage of Universal Search to identify relevant tables and columns, while Cortex Functions provide programmatic access for building custom applications.
Can I fine-tune LLMs with Snowflake Cortex?
Yes, Snowpark Container Services enables developers to deploy, manage and scale custom containerized workloads and models for tasks such as fine-tuning open-source LLMs using secure Snowflake-managed infrastructure with GPU instances. Pairing GPU-powered infrastructure with Snowpark Model Registry makes it easy to deploy, fine-tune and manage any open source LLM. Fine-tuning costs $6.00-$12.00 per credit depending on the model, and public preview is available in select AWS regions.
What is Cortex Analyst and how does it work?
Cortex Analyst uses AI to accurately convert natural language to SQL queries. It is simple to use and cost-efficient to scale, empowering employees to run analytics on enterprise data at scale and take actions getting reliable insights they can trust. Cortex Analyst comes bundled with Snowflake Enterprise Edition at the standard $3.00 per credit base rate, making it effectively free for existing Enterprise customers who already pay for compute. It displays suggested questions to guide users in three modes including LLM-generated suggestions.
How do I get access to private preview features?
To access any of the private preview features including Snowflake Cortex, Snowflake Copilot, Universal Search, Document AI, and various Cortex functions, you need to reach out to your Snowflake sales team or account team. They can help you request access to private preview features and guide you through the setup process for your Snowflake account in regions where your choice of LLMs are available.
How are costs calculated for Cortex LLM functions?
Cortex LLM functions incur compute cost based on the number of tokens processed, with rates varying by model. Tokens represent approximately four characters of text, and the equivalence of raw input or output text to tokens varies by model. Lightweight models cost $0.12 per 1M tokens while advanced models cost $5.10 per 1M tokens. Snowflake recommends executing queries with a smaller warehouse (no larger than MEDIUM) since larger warehouses do not increase performance. You can track credit consumption using Snowflake's account usage metering data queries.