|
By Harshal Pimpalkhute
At Snowflake, we are committed to providing our customers with industry-leading LLMs. We’re pleased to bring Meta’s latest Llama 4 models to Snowflake Cortex AI!
|
By Marketing AI Council
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.
|
By Snowflake
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.
|
By Carlos Nai
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.
|
By Vinay Sridhar
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.
|
By Snowflake
Nearly nine out of 10 business leaders say their organizations’ data ecosystems are ready to build and deploy AI, according to a recent survey. But 84% of the IT practitioners surveyed spend at least one hour a day fixing data problems. Seventy percent spend one to four hours a day remediating data issues, while 14% spend more than four hours each day.
|
By Florian Delval
As privacy standards continue to evolve, businesses face a dual challenge: to uphold ethical standards for data use while seizing the opportunities offered by data collaboration. Enter data clean rooms: a privacy-enhancing solution that allows organizations to share valuable insights without compromising compliance.* If you're new to data clean rooms, our recent blog post “Data Clean Rooms Explained: What You Need to Know About Privacy-First Collaboration” breaks down the fundamentals.
|
By Tony Cyriac
Many financial services companies are experimenting with AI through pilot programs, but several challenges remain for adoption. Key concerns include data security, the accuracy of large language models (LLMs) and the rigorous scrutiny from regulators regarding AI’s role in financial decision-making. Current use cases are largely internal, with some customer-facing chatbot solutions addressing noncritical service inquiries.
|
By Julian Forero
Unstructured text is everywhere in business: customer reviews, support tickets, call transcripts, documents. Large language models (LLMs) are transforming how we extract value from this data by running tasks from categorization to summarization and more. While AI has proved that real-time conversations in natural language are possible with LLMs, extracting insights from millions of unstructured data records using these LLMs can be a game changer. This is where batch LLM inference becomes essential.
|
By Snowflake
Some 70% of organizations are actively exploring or implementing large language model (LLM) use cases, but fewer than a third of generative AI experiments have made it into production. A common hurdle? The inability to access and leverage the data crucial for running AI applications effectively. Snowflake’s Accelerate 2025 virtual events dive into the challenges and myriad opportunities offered by AI.
|
By Snowflake Inc.
Stop switching tools. Start getting work done. Snowflake Intelligence is a personal work agent that helps you analyze data, generate insights, and take action—all in one place. Ask questions, automate workflows, and connect to the tools you already use, all within Snowflake’s governed platform. Learn how teams are using Snowflake Intelligence to move faster, collaborate better, and work at the speed of AI.
|
By Snowflake Inc.
See how Snowflake Intelligence transforms everyday work with a personal work agent built on your enterprise data. In this demo, a sales leader goes from insights to action in minutes—analyzing accounts, preparing meeting briefs, collaborating via Slack, and uncovering root causes with Deep Research—all in one seamless, governed experience.
|
By Snowflake Inc.
Welcome to the April 2026 Snow Report — your monthly rundown of the latest from Snowflake. This month we're covering major product launches, recapping two landmark events, and loading you up with everything on the calendar. In this episode: Cortex Code is Generally Available — Now live in the Snowsight UI and available to Windows users via the CLI. Build ML pipelines, run complex analytics, and manage admin tasks using natural language — all directly inside Snowflake.
|
By Snowflake Inc.
TS Imagine eliminated 6,000 hours of effort in customer inquiry handling while increasing speed by 10 times. Using AI has resulted in a five-fold increase in developer productivity; for example, a data engineer's output increased from three pull requests per two-week cycle to fifteen.
|
By Snowflake Inc.
Cortex Code is not more widely available and ready for bigger tasks with the lates updates. e. This announcement brings four major updates: Cortex Code in Snowsight is now generally available interface; the CLI now supports native Windows environments; Agent Teams make it easier to break large assignments into coordinated parallel work; and new agent skills standardize how Cortex Code helps build on data.
|
By Snowflake Inc.
Project SnowWork empowers business teams to automate multi-step workflows end-to-end, and drive real outcomes. Create revenue snapshots, diagnose missed forecasts, and generate summary slides with next steps — all without any coding experience needed.
|
By Snowflake Inc.
Introducing Project SnowWork. An autonomous AI platform that embeds intelligence directly into your business workflows and tools. Project SnowWork brings Snowflake's vision for the agentic enterprise to life, where enterprise data, intelligence, and action are connected in a governed way. Launching in research preview to a limited set of customers, Project SnowWork handles complex, multi-step tasks and delivers real, data-driven outcomes to business users.
|
By Snowflake Inc.
Hear what’s new at Snowflake in March, from major product launches to upcoming community events, and more. Next generation Snowflake Notebooks are now generally available, delivering a familiar Jupyter-based experience directly in Snowflake workspaces. Online model inference in the online feature store is also generally available, enabling millisecond predictions for real-time use cases like fraud detection and personalized recommendations, with no extra infrastructure to manage.
|
By Snowflake Inc.
Cortex Code CLI is expanding capabilities to accelerate your enterprise data lifecycle inside Snowflake! Introducing dbt and Apache Airflow support, expanded model choice across Claude Opus 4.6, Sonnet 4.6, and GBT 5.2. New enterprise-grade governance controls, and a self-serve subscription option. See how Cortex Code CLI helps you ship workflows faster, integrate data systems, and build with confidence using natural language.
|
By Snowflake
There's never been a better time to be an entrepreneur looking for investment funding. Global venture capital activity grew mightily in the first half of 2021, and the trend appears to be continuing as we head into 2022. However, that doesn't mean building a new company is any easier. The same inherent resource and growth challenges exist, and venture capitalists still want to see value creation and strong indicators for future success before they invest.
|
By Snowflake
Data scientists require massive amounts of data to build and train machine learning models. In the age of AI, fast and accurate access to data has become an important competitive differentiator, yet data management is commonly recognized as the most time-consuming aspect of the process. This white paper will help you identify the data requirements driving today's data science and ML initiatives and explain how you can satisfy those requirements with a cloud data platform that supports industry-leading tools.
|
By Snowflake
Many organizations struggle to share data internally across departments and externally with partners, vendors, suppliers, and customers. They use manual methods such as emailing spreadsheets or executing batch processes that require extracting, copying, moving, and reloading data. These methods are notorious for their lack of stability and security, and most importantly, for the fact that by the time data is ready for consumption, it has often become stale.
|
By Snowflake
Most companies that build software have a strong DevOps culture and a mature tool chain in place to enable it. But for developers that need to embed a data platform into their applications to support data workloads, challenges emerge. DevOps for databases is much more complex than DevOps for code because database contain valuable data, while code is stateless. Instantly creating any number of isolated environments Reducing schema change frequency with variant data type
|
By Snowflake
DELIVER ALL YOUR DATA WORKLOADS WITH SNOWFLAKE Gartner predicts that 75% of all databases will be deployed or migrated to a cloud platform by 2022. But how does e a cloud data platform enable a long-term strategy for maximizing all of an organization's data assets? Snowflake's cloud data platform is a highly extensible, multi-region and multi-cloud platform that powers all types of data workloads. Specifically, Snowflake: To learn everything Snowflake offers today's, forward-looking organizations, download our white paper, Snowflake: One Cloud Data Platform for All Your Analytic Needs.
|
By Snowflake
Companies are moving workloads to the cloud as they seek to improve speed, scale, and agility. Today's data warehouse managers want to boost analytics productivity, increase the ability to scale instantly, and ingest and support a diverse set of data without bottleneck delays. In this white paper, we explain how Snowflake delivers the speed, scale, and agility organizations need for data-driven decision-making.
|
By Snowflake
Financial institutions are embracing cloud-based data technologies to improve their service and product offerings, streamline operations, and gain deeper customer insights. This ebook features success stories about the many ways financial services companies are leveraging Snowflake Cloud Data Platform to build a 360 degree view of customers, accelerate financial analysis with unlimited scale, and keep sensitive and regulated data secure.
|
By Snowflake
As companies have recognized the importance of unifying customer data to obtain business insights, customer data platforms that consolidate and activate known customer information have become ubiquitous. Customer data platforms enable marketers to segment and share customer profiles with marketing systems to personalize the content of email campaigns, digital ads, and other channels. In this ebook, we explore how marketers can launch and operate a customer data platform successfully, with a focus on how to.
- April 2026 (3)
- March 2026 (5)
- February 2026 (3)
- January 2026 (7)
- December 2025 (9)
- November 2025 (14)
- October 2025 (6)
- September 2025 (6)
- August 2025 (6)
- July 2025 (9)
- June 2025 (12)
- May 2025 (4)
- April 2025 (11)
- March 2025 (11)
- February 2025 (19)
- January 2025 (22)
- December 2024 (9)
- November 2024 (15)
- October 2024 (14)
- September 2024 (8)
- August 2024 (15)
- July 2024 (17)
- June 2024 (27)
- May 2024 (21)
- April 2024 (17)
- March 2024 (22)
- February 2024 (16)
- January 2024 (18)
- December 2023 (15)
- November 2023 (21)
- October 2023 (15)
- September 2023 (12)
- August 2023 (10)
- July 2023 (6)
- June 2023 (12)
- May 2023 (5)
- April 2023 (5)
- March 2023 (6)
- February 2023 (13)
- January 2023 (16)
- December 2022 (9)
- November 2022 (14)
- October 2022 (11)
- September 2022 (10)
- August 2022 (12)
- July 2022 (11)
- June 2022 (12)
- May 2022 (15)
- April 2022 (11)
- March 2022 (18)
- February 2022 (15)
- January 2022 (13)
- December 2021 (9)
- November 2021 (10)
- October 2021 (11)
- September 2021 (8)
- August 2021 (12)
- July 2021 (6)
- June 2021 (11)
- May 2021 (3)
- April 2021 (8)
- March 2021 (6)
- February 2021 (7)
- January 2021 (16)
- December 2020 (18)
- November 2020 (22)
- October 2020 (20)
- September 2020 (8)
- August 2020 (8)
- July 2020 (15)
- June 2020 (16)
- May 2020 (11)
Snowflake’s mission is to enable every organization to be data-driven. Our cloud-built data platform makes that possible by delivering instant elasticity, secure data sharing and per-second pricing, across multiple clouds. Snowflake combines the power of data warehousing, the flexibility of big data platforms and the elasticity of the cloud at a fraction of the cost of traditional solutions.
Conventional data platforms and big data solutions struggle to deliver on their fundamental purpose: to enable any user to work with any data, without limits on scale, performance or flexibility. Whether you’re a data analyst, data scientist, data engineer, or any other business or technology professional, you’ll get more from your data with Snowflake.
What Makes Snowflake Unique:
- A Multi-Cluster Shared Data Architecture Across Any Cloud: Easily scale up and down any amount of computing power for any number of workloads or users and across any combination of clouds, while accessing the same, single copy of your data but only paying for the resources you use thanks to Snowflake’s per-second pricing.
- Secure Data Sharing and Collaboration: Eliminate the cost and headache of static data sharing methods by easily sharing any amount of live structured and semi-structured data, without having to move data, whether it be across your enterprise, with customers and business partners, or to monetize your data.
- One, Near-Zero Maintenance Platform Delivered as a Service: Choose any combination of infrastructure providers, enable your workloads where you want, rely on Snowflake to manage the data platform, and deploy across and between different clouds and regions to support business efficiencies and data sovereignty.
A Modern Data Platform Built For Any Cloud.