Snowflake is introducing two new tools to modernize analytics at the Snowflake Summit. Cortex AISQL and SnowConvert AI bring generative AI directly to SQL queries and automate migrations from legacy systems. According to Snowflake, companies can save up to 60 percent on data analysis costs.
Cortex AISQL is currently in public preview, enabling the direct use of generative AI in SQL queries. At the same time, SnowConvert AI introduces an automated approach to migrating legacy data infrastructure to Snowflake.
AI functions directly in SQL
Traditionally, SQL users have been limited to structured and semi-structured data. Cortex AISQL addresses this limitation by integrating AI capabilities directly into the familiar SQL syntax. Analysts can also query unstructured sources such as text, images, and audio.
The technology uses models from Anthropic, Meta, Mistral, and OpenAI. This combination enables teams to leverage their existing SQL knowledge while gaining access to advanced AI functionality. According to Snowflake, this delivers performance improvements of 30 to 70 percent, depending on the dataset.
The platform enables analysts to enrich customer tables with chat history, link sensor data to inspection images, and combine sales figures with social media sentiment. These capabilities were previously reserved for developers with specialized AI knowledge.
Automated migrations
SnowConvert AI focuses on automating complex migration processes. The platform can convert code, BI reports, and ETL tools from legacy systems such as Oracle, Teradata, and Google BigQuery. The AI agents automatically validate the converted code and migrated data.
The system uses agents powered by Snowflake Cortex AI. This approach is designed to make the conversion and testing phases two to three times faster than traditional methods. For organizations, this means less manual work and shorter migration times.
The solution goes beyond database migrations. Complete data ecosystems, including BI tools and ETL processes, can be transferred without disrupting critical workflows. This reduces risk and complexity during large-scale modernization projects.
Performance and cost benefits
Snowflake claims significant cost savings with the new features. Organizations can save up to 60 percent when filtering or merging data. These efficiency gains are in addition to the performance improvements already offered by the platform.
Head of Analytics Carl Perry at Snowflake emphasizes that the solutions remove practical obstacles. “We’re removing those barriers, whether it’s enabling anyone to analyze and act on all their data with Cortex AISQL or accelerating migrations off legacy systems through SnowConvert AI.”
Snowflake also announces Snowflake Standard Warehouse – Generation 2, which is now generally available. This new version uses hardware and software optimizations to deliver 2.1 times faster analytics performance. This enables organizations to extract AI-driven insights from all their data faster.
Broader adoption expected
Several organizations are already taking advantage of the new capabilities. Companies such as Hex, Sigma, and TS Imagine are experimenting with Cortex AISQL to expand their data capabilities. The focus is on combining traditional analytics with AI functionality.
This development is part of a broader trend in which data platforms are democratizing AI capabilities. By extending familiar tools, such as SQL, with AI functions, organizations can avoid the need to overhaul their existing workflows completely. Teams can transition in phases and continue to use their current knowledge.
For many companies, this is an important step toward a more modern data infrastructure. Legacy systems often form a bottleneck when implementing AI applications. By automating migrations and making AI available directly in queries, Snowflake significantly shortens the path to modernization.
Tip: Snowflake makes AI Data Cloud the brain of any business