Snowflake Cortex AI is Snowflake’s integrated generative AI suite that runs directly within the Snowflake Data Cloud. It is a fully managed, serverless platform that allows organizations to analyze data and build AI applications where their data already lives. With Cortex AI, companies gain instant access to powerful large language models (LLMs) – from Snowflake’s own enterprise models to offerings from leading AI labs – all accessible through Snowflake’s familiar environment. In simple terms, Cortex AI brings advanced AI capabilities inside the data platform instead of requiring external services or complex infrastructure.
Why Embed AI Inside Snowflake?
Embedding AI functions directly in Snowflake offers significant advantages for enterprises looking to leverage AI quickly and securely. Some key benefits include:
- No Data Movement: All analysis happens in-platform, so there’s no need to export or copy data to external AI services. This reduces latency, cost, and compliance risk since data stays in the Snowflake cloud where it already resides.
- No Infrastructure to Manage: Cortex AI is fully managed by Snowflake. Data teams don’t have to provision or maintain separate AI servers or ML infrastructure. Snowflake handles the underlying model hosting and performance scaling, allowing teams to focus on insights instead of ops.
- Secure and Governed: Because data and AI processing remain within Snowflake, organizations maintain their existing security controls and governance. Sensitive data isn’t sent to third-party platforms. All AI queries and outputs are subject to the same access permissions and auditability as the rest of your data cloud.
- SQL-Native Access: Cortex AI’s functions can be invoked with simple SQL commands (as well as via Snowpark for Python). This SQL-native approach means analysts and developers can incorporate AI into queries, dashboards, and applications without learning new tools. It brings AI into the familiar workflows of the data warehouse, lowering the barrier to entry.
With these benefits, Snowflake Cortex AI turns the data warehouse into an AI powerhouse. Teams can perform text analysis, generate content, summarize documents, run natural language searches, and more – all by using built-in functions in their Snowflake instance. The result is faster development of AI features and insights, since everything happens in one environment.
Bringing AI to the Data vs. Moving Data to AI
Snowflake’s in-platform approach stands in contrast to external AI services offered by cloud providers such as AWS or Google. Those solutions (like AWS Bedrock or Google’s Gemini models on Vertex AI) typically require sending data out to a separate service for AI processing. That “move the data to the AI” approach introduces additional complexity – data pipelines to external systems, extra copies of data, and multiple platforms to manage and secure. It can also conflict with data gravity, the principle that large datasets are expensive and inefficient to move.
Bringing AI to the data, as Cortex AI does, flips this dynamic. Snowflake already holds vast amounts of enterprise data (the data gravity is in Snowflake), so it makes sense to bring the AI models into Snowflake’s orbit. This means minimal data movement, which translates to simpler architecture and faster results. Governance is stronger too: by keeping everything in one platform, companies ensure consistent compliance and monitoring. In short, Cortex AI provides a simpler and safer path to generative AI. Organizations get the flexibility of modern LLMs similar to external platforms, but with the simplicity of one unified system and the peace of mind of Snowflake’s data governance.
A Glimpse Ahead: Cost and FinOps Considerations
Snowflake Cortex AI opens the door to powerful in-cloud AI capabilities, but it also introduces new considerations for cost management. Running large language model functions on your data can have significant usage costs, and businesses will need to approach this strategically. In Part 2 of this series, we will explore the cost model of Cortex AI and discuss FinOps best practices to ensure that adopting embedded AI in Snowflake remains as efficient and cost-effective as it is innovative. Stay tuned for a deep dive into managing the economics of Cortex AI in the next installment.
