If you heard that Snowflake now runs machine learning models and vector searches inside its SQL engine, you might wonder what’s actually ready to use today versus what’s still on the horizon. Snowflake Cortex functions promise a leap toward blending data and AI workflows tightly together, but the details behind those promises often blur.
I’ve seen teams struggle to separate the hype from the practical tools they can rely on right now. You want to avoid chasing features that are not production-ready but also not miss out on leveraging what’s truly available to speed up your data and ML workflows. So what Cortex functions can you plug into your Snowflake pipelines today? And which ones should you hold off on until they’re fully baked?
Snowflake Cortex functions became generally available starting with the 2023.01 release. This means they are no longer in preview—they’re production-ready and supported. The headline features include vector similarity search, text embedding generation, and model inference directly within SQL queries. This is a big shift from the traditional approach of pulling data out for ML tasks, enabling much faster, more scalable AI-powered analytics inside Snowflake itself.
When you hear about Cortex, the first concrete functions you can call today are VECTOR_SIMILARITY, TEXT_EMBEDDING, and MODEL_INFERENCE. Let’s unpack what each offers and how you might start using them right away.
VECTOR_SIMILARITY
VECTOR_SIMILARITY lets you perform large-scale similarity searches on vector data stored in Snowflake tables. This is key for semantic search, recommendation engines, or any use case where you match items based on their “distance” in some feature space.
Here’s how you might find the top 5 most similar product embeddings to a query vector:
-- Demonstrates using VECTOR_SIMILARITY to find similar vectors in a table
SELECT
id,
VECTOR_SIMILARITY(query_vector, embedding_vector) AS similarity_score
FROM
product_embeddings
ORDER BY
similarity_score DESC
LIMIT 5;This SQL snippet highlights the power of running vector operations inside Snowflake. You avoid exporting large embedding tables or building external search indexes. Snowflake scales those computations under the hood, leveraging its MPP architecture and, where available, GPU acceleration for even faster throughput.
TEXT_EMBEDDING
TEXT_EMBEDDING takes raw text and converts it into numerical vectors based on pre-trained models. This enables semantic analysis directly within your SQL queries, simplifying ETL pipelines that need to enrich data with embeddings for search or ML downstream.
For example, generating an embedding for a sentence looks like this:
-- Generate embeddings for a given text using a pre-trained model
SELECT
TEXT_EMBEDDING('The quick brown fox jumps over the lazy dog', 'sentence-transformers/all-MiniLM-L6-v2') AS embedding_vector;Snowflake provides a curated library of popular pre-trained models such as ‘sentence-transformers/all-MiniLM-L6-v2’, so you don’t have to bring your own model artifacts just to get started with embeddings.
MODEL_INFERENCE
MODEL_INFERENCE enables running predictions from custom or pre-trained models deployed inside Snowflake. This turns Snowflake from a data warehouse into an active AI platform where real-time or batch inference can be part of your SQL workflows.
Say you want to generate product recommendations for a user based on their features stored in Snowflake:
-- Use MODEL_INFERENCE to perform prediction with a deployed model
SELECT
user_id,
MODEL_INFERENCE('recommendation_model_v1', user_features) AS recommended_items
FROM
user_feature_table
WHERE
user_id = 12345;If you’re used to pushing features out to a separate ML service, this feels like magic. The inference happens entirely inside Snowflake, minimizing data movement and latency.
Current State and Future of Cortex
But before you start redesigning your pipelines around Cortex, here is what you need to know about its current state and future trajectory.
Why should you use Snowflake Cortex functions today rather than waiting?
They are production-ready and integrated directly into Snowflake SQL, so adoption won’t disrupt your existing data workflows. Cortex functions are optimized for high performance inside Snowflake’s compute engine, taking advantage of CPU and GPU acceleration where available. This is no gimmick or experiment: powerful vector and ML operations are fully supported on Business Critical and Enterprise editions.
Snowflake recommends Cortex for semantic search, recommendation systems, and real-time inference use cases. If your team struggles with data movement between Snowflake and ML platforms, Cortex functions can reduce complexity and improve latency.
What’s not ready yet?
The Cortex SDK, which aims to make model deployment and management easier, is still in private preview. This means you can’t yet build custom Cortex functions or deploy new model types through a streamlined SDK interface. For now, you rely on the pre-trained models and existing deployment mechanisms Snowflake supports.
Also, while Cortex functions integrate cleanly with SQL, advanced customization or non-standard ML workflows may still require external tooling. But this will improve rapidly as Snowflake expands Cortex capabilities.
How to start integrating Cortex functions into your data pipelines
- Identify key use cases where combining vector search, embedding, or inference inside Snowflake would cut down data movement and latency.
- Test the VECTOR_SIMILARITY function on your embedding tables to validate performance and accuracy. Start with small-scale queries to establish baselines.
- Experiment with TEXT_EMBEDDING using Snowflake’s pre-trained models to generate embeddings for your textual data. This can replace slow batch embedding jobs outside Snowflake.
- Deploy or migrate your models to Snowflake’s ML model registry and invoke them via MODEL_INFERENCE within your SQL queries.
- Monitor compute costs and performance, especially if you can enable GPU acceleration on your Snowflake edition.
What can go wrong when adopting Cortex functions prematurely?
– Mistaking private preview capabilities like the Cortex SDK for being GA can lead to blocked projects or stalled workflows.
– Overloading Snowflake with complex model inference without appropriate resource planning may cause cost or latency surprises.
– Treating Cortex functions as black-box magic without validating model accuracy or similarity metrics risks poor data product quality.
– Relying solely on pre-trained models without room for customization may limit your ability to address specific domain challenges.
We tend to forget that technology readiness is a spectrum, not a binary. Cortex functions sit firmly on the practical side today for many AI-enabled SQL workflows, yet the broader ecosystem around them is evolving fast.
*”It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change.”* – Charles Darwin
I’ve watched teams that took early but measured steps with Cortex reap tangible gains: faster semantic search queries, simplified pipelines, and real-time recommendations without costly data shuffles. Others who chased SDKs and future promises ended up stuck waiting, frustrated, and circling back to manual integrations.
The key is to work with what is GA today: VECTOR_SIMILARITY, TEXT_EMBEDDING, and MODEL_INFERENCE. Build confidence and outcomes with those before trying to leap ahead. That pragmatic approach lets you harness Snowflake Cortex’s power without tripping over incomplete features.
Snowflake Cortex is a bridge between data and AI that finally feels real. Use it to embed intelligence in your SQL, but don’t mistake the bridge for the full road just yet. With steady steps on that bridge, you’ll be ready when the rest arrives.
🐍❄️🤖
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