Overview
In today’s data-driven world, businesses are constantly seeking faster, smarter ways to extract insights from their data. One of the most promising innovations in this space is Chat Analytics—a solution that transforms natural language queries into actionable SQL, enabling users to interact with data intuitively.
The CloudEQS team has been downloading the requirements from our enterprise clients who have been asking a simple question: how do we enable non-technical users to ask questions about their data using natural language?
Thus, the purpose of this article is two fold:
- Share our comparison of 3 options on the market within the Snowflake ecosystem: (1) Snowflake Cortex Analyst, (2) Snowflake Cortex with Claude, (3) Snowflake integration with OpenAI GPT-5.
- Showcase our Streamlit Application architecture
What Is Chat Analytics?
Chat Analytics is a cutting-edge solution designed to simplify data exploration by allowing users to ask questions in plain English and receive SQL-generated answers, complete with tabular results, summaries, and visualizations. This approach bridges the gap between business users and complex data systems, making analytics more accessible and efficient.
Architecture Overview
The Chat Analytics solution is built on a robust, multi-layered architecture:
- Streamlit UI: A user-friendly interface for entering queries.
- Data Access Layer: Connects to Snowflake DB, fetches metadata, and formats table descriptions.
- Core Logic Layer: Handles session initialization, table selection, SQL generation, and cleaning.
- LLM Integration Layer: Executes SQL, rephrases answers, and updates history using GPT-5 or Cortex Claude.
This modular design ensures scalability, flexibility, and seamless integration with enterprise data environments.
Comparative Analysis: Cortex Analyst vs. Snowflake Cortex Claude vs. GPT-5
| Criteria | Cortex Analyst | Snowflake Cortex + Claude | Azure GPT-5 |
|---|---|---|---|
| Semantic Layer | Snowflake Semantic Views | Schema Objects | Schema Objects |
| Accuracy | Medium | Mid-High | High |
| Performance | Slow with complex queries | Similar | Consistent |
| Cost per Query | ~$0.10 | ~$0.10 | ~$0.016 |
| Operational Efficiency | High | Mid | Mid-High |
| Customization | None | High | High |
| Pros | – Easy to deploy – Primary Designed for Business Analyst to create SQLs | – Data stays in Snowflake – Integrate with any LLM – Customizable UI | – Low cost – Supports any DB – Customizable UI |
| Cons | – Limited control – Higher costs | – Higher cost | – Requires more coding – Data shared with OpenAI |
Recommendations
If your organization already has a secured instance of GPT-5, we recommend leveraging it due to its superior accuracy with complex queries and significantly lower cost per query. For teams operating fully within the Snowflake ecosystem, Snowflake Cortex with Claude is a strong second choice, offering native integration and access to schema-level semantic layers. Both options outperform Cortex Analyst in flexibility and depth of context, making them better suited for enterprise-grade analytics.
Summary
Chat Analytics empowers businesses to transform natural language queries into actionable SQL insights using advanced LLMs like GPT-5 and Snowflake Cortex Claude. The solution integrates seamlessly with Snowflake, offering customizable interfaces, accurate query generation, and cost-efficient performance.
Ready to explore how Chat Analytics can transform your data strategy? Contact the CloudEQS team today to learn how we can tailor a solution that fits your enterprise needs.
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