Systems | Development | Analytics | API | Testing

Choosing The Right LLM: Why Flexibility Is Key

In this Cortex AI roundtable, Snowflake's Arun Agarwal, Yusuf Ozuysal, and James Cha-Earley highlight why AI model flexibility is crucial for real-world applications. Recognizing that no single large language model excels at everything — coding, reasoning, extraction, or conversation — Snowflake Cortex AI brings the strengths of different LLMs together by integrating leading LLMs like OpenAI, Mistral, Anthropic, and Meta into one secure platform.

Unstructured Data Analytics For Enterprises

Get insights from your unstructured data in minutes without wrestling with pipelines or infrastructure. Using simple SQL, enterprises can translate multi-language text, understand and extract sentiment from reviews, classify key themes, and summarize millions of records faster than ever. With built-in governance, enterprise-grade security, and production scalability, you can trust the speed and reliability of your intelligence. Join Snowflake’s Arun Agarwal, Renee Huang, and Vino Duraisamy to see how Snowflake Cortex AI makes this easy.

Beyond the Hype: Putting Enterprise AI To Work

In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Sridhar Ramaswamy, CEO of Snowflake. They discuss the impactful integration of AI with enterprise data, Snowflake's efforts to demystify AI for its customers, and future prospects for agentic AI and its potential to transform business processes.

AI-Driven ABM: Scaling Precision and Impact for B2B Growth

We’ve seen how Snowflake AI tools are transforming outcomes for our customers. From saving 4,000 hours a year on manual email intake to treating more patients in emergency rooms to saving 75% of costs, AI in Snowflake is making a real impact on businesses around the world. That same transformative power is at work within Snowflake, too.

Key Takeaways from Accelerate: How Financial Services and Manufacturing Companies Leverage Data and AI for Measurable ROI

For many organizations across industries, the era of experimental AI has given way to the era of practical implementation. Even those companies still testing and evaluating AI solutions are shifting away from the art of the possible to focus more closely on what will soon produce measurable ROI. “It will no longer be enough for your organization to merely use AI to win the approval of company leadership,” says Samuel Lee, Product Marketing Director for Financial Services at Snowflake.

The Apache Iceberg Avalanche: How the Open Table Format Changes the Face of Data Lakes

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems.

Break Data Silos: Build, Deploy and Serve Models at Scale with Snowflake ML

Despite the best efforts of many ML teams, most models still never make it to production due to disparate tooling, which often leads to fragmented data and ML pipelines and complex infrastructure management. Snowflake has continuously focused on making it easier and faster for customers to bring advanced models into production.