Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. As researchers, we have grappled with the constraints of efficiently training and inferencing LLMs for years.

Magnite stands as the largest independent sell-side advertising platform, providing an essential bridge between publishers and advertisers. At its core, Magnite streamlines the advertising process, facilitating the buying and selling of advertising space across various channels, including connected TV (CTV), mobile, and desktop environments.

Legacy security information and event management (SIEM) solutions, like Splunk, are powerful tools for managing and analyzing machine-generated data. They have become indispensable for organizations worldwide, particularly for security teams. But as much as security operation center (SOC) analysts have come to rely on solutions like Splunk, there is one complaint that comes up for some: Costs can quickly add up.

In today’s data-driven world, developer productivity is essential for organizations to build effective and reliable products, accelerate time to value, and fuel ongoing innovation. To deliver on these goals, developers must have the ability to manipulate and analyze information efficiently. Yet while SQL applications have long served as the gateway to access and manage data, Python has become the language of choice for most data teams, creating a disconnect.

Marketers everywhere are anxious about what fortune the cookieless future holds — but I think we should embrace what will be a refreshing change.

Today Snowflake is launching and open-sourcing with an Apache 2.0 license the Snowflake Arctic embed family of models. Based on the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard, the largest Arctic embed model with only 334 million parameters is the only one to surpass average retrieval performance of 55.9, a feat only less practical to deploy models with over 1 billion parameters are able to achieve.

In March, Snowflake announced exciting releases, including advances in AI and ML with new features in Snowflake Cortex, new governance and privacy features in Snowflake Horizon, and broader developer support with the Snowflake CLI. Read on to learn more about everything we announced last month.

Data is the lifeblood of modern businesses, but unlocking its true insights often requires complex SQL queries. These queries can be time-consuming to write and challenging to maintain. At Snowflake, we believe in making the power of data accessible to all. That’s why we prioritize simplicity, governance and quality in everything we build – including our AI-powered tools.

As Snowflake continues to expand our commitment to compliance, we are pleased to announce that we have successfully completed both C5 and TISAX attestations in Germany.

Snowflake and Salesforce are happy to share that bidirectional data sharing between Snowflake, the Data Cloud company and Salesforce Data Cloud is now generally available. In September, we proudly announced that organizations could begin leveraging Salesforce data directly in Snowflake via zero-ETL data sharing to unify their customer and business data, accelerate decision-making and help streamline business processes.