Why Snowflake Cortex AISQL Matters for the Next Decade of Data Work
I’ve been following Snowflake’s evolution for years, from a warehouse upstart to a full platform play. But Cortex AISQL marks a foundational shift. This isn’t just another feature rollout. It’s a realignment of how we ask questions of our data.
Let me break down why it’s important and where it breaks away from tradition.
1. SQL Isn’t Just for Numbers Anymore
Most organizations still treat SQL as a gateway to numeric and tabular insight. If you want sentiment from customer reviews or tags from an image catalog, you either build a separate AI pipeline or give up.
Cortex AISQL breaks this barrier. It introduces AI-native SQL operators like AI_FILTER, AI_JOIN, AI_CLASSIFY, and AI_AGG. These aren’t wrappers around Python models. They’re core SQL primitives designed to work on unstructured data: text, image, audio. You can ask questions like “Summarize all negative feedback from customers who viewed this image”, done in one query, inside Snowflake.
That’s not a convenience upgrade. It’s an operating model shift.
2. Architecture That Understands the Cost of AI
What surprised me wasn’t just the features, it was how intelligently they’re deployed.
Cortex uses a tiered model approach: lightweight models for cheap, fast jobs; heavy models only when necessary. This dynamic selection brings up to 70% runtime reduction and 60% cost savings, compared to bolting on LLMs manually through APIs or ML pipelines. And since it all runs within the Snowflake engine, no data leaves the platform, no proxies, no third-party exposure, no "Frankenstein" integrations.
In short: secure, optimized, and embedded. Not a sidecar.
3. Unified Security Across Structured and AI Workloads
Governance is not an afterthought here. Snowflake’s existing controls, RBAC, masking, auditing, till extend to AISQL usage as well. This matters in regulated industries. If your LLM inference runs on vendor X’s GPU in region Y with questionable data handling? You’ve just broken compliance without knowing it.
With Cortex, inference never leaves the Snowflake trust boundary.
For many teams, this is the difference between “experiment” and “production”.
4. Why This Isn’t Just a Fancy SQL Extension
Traditional RDBMS can’t keep up. Not because they’re bad, but because they were never built for AI integration. Even the best Postgres or SQL Server setups need a tangle of ETL jobs and external AI orchestration to match what Cortex does in one query.
Snowflake is effectively turning SQL into a programming interface for agents, pipelines that can reason, filter, generate, and classify (all in a composable, declarative style). Imagine a multi-step workflow where customer sentiment, image tags, and past purchase behavior are analyzed and summarized, all in SQL. No Python. No Airflow. No GPU provisioning.
That’s Cortex.
5. The Real Bet: AI-Native Data Workflows
This isn’t just about getting more from your warehouse. It’s about asking new kinds of questions, the ones that cross data modalities and business boundaries. Sales, support, product, marketing all deal with noisy, multimodal inputs. Cortex unifies those inputs into something you can actually query.
If we treat data as the raw material for intelligence, then Cortex makes Snowflake the workbench for the AI-native enterprise.
Closing Thought
If your SQL today can’t parse meaning from a sentence, classify a complaint, or semantically join two concepts, it’s already behind. Cortex AISQL is not optional innovation. It’s an early signal of what the next 10 years of data systems will require by default.
And it's already here.
Bonus
I’ve seen plenty of rebrands and relaunches in the data space. Most of them are wrappers, not reinventions. But Snowflake Cortex AISQL is different. It doesn’t just enhance SQL, but it fundamentally expands what SQL is allowed to do.
Here’s why this matters and what it actually looks like in action.
1. SQL That Thinks Beyond Rows and Numbers
Until now, SQL was blind to meaning. You could count and group, but try to ask SQL: “Which reviews are sarcastic?” and it’s game over. Cortex adds AI-native SQL functions that understand semantics, not just schema.
Example: Semantic filtering on unstructured text
SELECT
FROM customer_reviews
WHERE AI_FILTER('Reviews with sarcastic tone', review_text);No ML pipeline. No vector database. Just SQL.
With operators like AI_FILTER, you can run inference as part of your query. It’s like turning every table into a reasoning surface, not just a ledger.
2. AI Where It Belongs: Inside the Engine
We’ve spent years pushing data out of warehouses into AI services, only to drag results back in. It’s inefficient and risky. Cortex runs AI in situ, right inside Snowflake, so the data never moves.
And it’s smart about cost: Snowflake dynamically selects the most efficient LLM for your use case. So a summarization doesn’t fire up the biggest model by default.
Example: Summarize feedback directly in a SQL group
SELECT product_id,
AI_SUMMARIZE_AGG(review_text) AS summary
FROM customer_reviews
GROUP BY product_id;This kind of aggregation once needed Python notebooks or batch jobs. Now? Inline.
3. Unstructured Data Becomes First-Class
Images, PDFs, even audio, they’re no longer afterthoughts. Cortex introduces a FILE data type, and that changes the game. You can store and query media natively in tables, just like text.
Example: Image classification from a SQL table
SELECT
image_id,
AI_CLASSIFY(
image_file,
ARRAY_CONSTRUCT('shirt', 'pants', 'hat', 'shoes')
) AS product_type
FROM product_images;This lets teams run computer vision workflows inside analytics queries, without building a separate inference stack.
4. Secure, Compliant, Production-Grade AI
This isn’t a hackathon demo. Cortex keeps everything within Snowflake’s governance boundaries. So if you're in finance, healthcare, or any regulated space, this is your compliance-safe route to AI operations.
You can audit, monitor, and permission AI functions like any other query, because they’re part of the same stack, not some loosely coupled add-on.
5. A Single Query for a Multi-Agent Workflow
With traditional tools, pulling together user intent, document extraction, sentiment analysis, and recommendation logic required five systems and a DevOps headache.
Cortex compresses all of that into one expressive, declarative format: SQL.
Example: Orchestrating multiple AI steps inline
WITH extracted AS (
SELECT
document_id,
EXTRACT_ANSWER(content, 'What are the key complaints?') AS complaint
FROM support_tickets
),
joined AS (
SELECT
e.*,
u.segment
FROM extracted e
JOIN users u
ON e.document_id = u.last_ticket_id
)
SELECT
segment,
AI_SUMMARIZE_AGG(complaint) AS top_issues
FROM joined
GROUP BY segment;This isn’t just AI at the edge of the system. It’s AI as the system.
Final Thought: This Isn’t Optional
Every org that works with unstructured data, which is every org will eventually need to reason over it at scale. Cortex AISQL is the first serious, secure, and scalable way to do that without leaving SQL behind.
We’re not just seeing AI inside Snowflake, we’re seeing SQL evolve into a language of meaning.
If you lead data, analytics, or AI platforms: this is your early signal. The age of “extract to analyze” is ending. The future is: query to understand.
And Cortex makes that future feel surprisingly… writeable.
Ref:
[1] https://www.snowflake.com/en/product/features/cortex
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