For most of its life, Snowflake was where data lived and got queried - a warehouse, full stop. Machine learning happened somewhere else: you exported data, trained models in a separate environment, and shipped predictions back. That round trip is exactly the friction Snowflake has spent recent years removing. With Snowpark, native ML functions, and built-in access to large language models, Snowflake increasingly aims to be the place where the whole ML lifecycle runs - data, features, training, and inference - without the data ever leaving.

Why keeping ML in the warehouse matters

Every time data leaves the warehouse for a separate ML system, you pay a tax: pipelines to move it, copies to govern, security boundaries to cross, and drift between the data scientists' environment and production. Running ML where the data already sits collapses that. Governance and access controls stay in one place, there's no export step to secure or maintain, and the gap between "works in a notebook" and "runs in production" narrows because both use the same engine over the same data. The pitch isn't that Snowflake's ML is the most advanced anywhere - it's that proximity to governed data removes a category of work and risk.

Snowpark: code, not just SQL

Snowpark is the foundation. It lets you write data transformations and logic in Python (and other languages) that execute inside Snowflake's compute, rather than pulling data out to process elsewhere. For feature engineering, that means the transformations that build your training data run next to the data, in the same governed environment, at warehouse scale. Snowpark also runs Python functions and stored procedures server-side, so model-related logic lives in the platform instead of a separate orchestration layer.

Native ML and feature management

On top of Snowpark, Snowflake has added higher-level ML capabilities - functions for common tasks like forecasting and anomaly detection that you call much like SQL, and a feature store and model registry to manage the artifacts of an ML workflow within the platform. The effect is that more of the lifecycle - defining features, training, storing models, serving predictions - can happen inside Snowflake, governed and versioned alongside the data, rather than scattered across external tools.

LLMs without moving data

The newer frontier is bringing large language models to the data through managed AI functions (Cortex). Instead of exporting text to an external API, you run tasks like summarization, classification, sentiment, and embedding generation as functions over data already in Snowflake - keeping sensitive data inside the governance boundary while still using modern models. For organizations that can't freely send data to outside services, that in-platform access is the difference between using LLMs on their data and not.

Where this fits and where it doesn't

The honest framing is fit, not supremacy. If your data lives in Snowflake and your priorities are governance, simplicity, and avoiding data movement, running ML in-platform is compelling - fewer systems, one security model, less plumbing. If you need the cutting edge of model development, deep customization, or a specialized ML platform's full toolkit, a dedicated environment like Databricks or a standalone MLOps stack may serve better. Many teams split the difference: heavy research and training elsewhere, feature engineering and inference close to the governed data in Snowflake.

The takeaway

Snowflake's expansion from storage toward an end-to-end ML platform is less about any single feature and more about a principle: do the work where the governed data already is. Snowpark, native ML, a feature store and registry, and in-platform LLM functions together let a growing share of the ML lifecycle run without exporting data - removing pipelines, copies, and risk in the process. Whether it replaces a dedicated ML platform depends on your needs, but for Snowflake-centric teams, "keep the ML next to the data" is an increasingly practical default rather than an aspiration.