Modern data architectures are evolving rapidly, and Snowflake Cortex AISQL is at the forefront of this change. It lets you query unstructured data—files, images, and text—directly using SQL enhanced with AI capabilities. But here’s the catch: these powerful AI features come with significant computational overhead. If you’re not careful about optimization, you’ll face slow queries and skyrocketing costs.

This guide walks you through practical strategies to get the most out of Cortex AISQL while keeping your warehouse credits in check.

Why Snowflake Cortex AISQL Query Optimization Matters in 2025

The amount of unstructured data in cloud warehouses has exploded. Cortex AISQL makes it easier for developers to work with this data without needing deep data science expertise. That’s great for democratizing AI, but it also puts serious strain on your computational resources.

Here’s what happens when you neglect optimization:

  • Costs spiral out of control – Poorly optimized queries can unexpectedly spike your cloud computing bills
  • Slow results hurt decision-making – Business users need timely insights, not queries that take minutes to complete
  • Limited concurrency – Inefficient queries hog resources, preventing other users from accessing AI insights

The good news? With proper optimization, you can protect your budget, improve performance, and enable more users to leverage AI across your organization.

Understanding How Cortex AISQL Works

Cortex AISQL translates your SQL statements into complex workflows that involve AI models. When you run a query, Snowflake:

  1. Parses your request and identifies which AI functions to call (like CORTEX_ANALYST or embedding generation)
  2. Determines the optimal execution plan, balancing data retrieval with external model calls
  3. Executes the query across both storage and compute layers

The key to optimization is minimizing data movement and reducing the amount of data sent to the AI processing layer. Think of it like this: every row you can filter out before calling an AI function is money and time saved.

Getting Started: Profile Your Queries First

Before you start optimizing, you need to understand where your bottlenecks are. Use Snowflake’s Query Profile feature to identify:

  • Steps that consume the most time
  • External function calls that are slowing things down
  • Massive table scans that could be avoided

Here’s a real example of what NOT to do:

-- ❌ BAD: Passing all documents to the AI function
SELECT
    document_id,
    CORTEX_ANALYST(document_text, 'Summarize key themes') AS summary
FROM
    large_documents;

This query sends every single document through the AI function. If you have millions of documents, you’re looking at a very expensive (and slow) operation.

The Single Most Effective Optimization: Filter Early, Filter Hard

The best way to optimize AISQL queries is brutally simple: reduce your data before calling AI functions. Use standard SQL filtering to narrow down your dataset first.

Here’s the improved version:

-- ✅ GOOD: Filter aggressively before using AI functions
SELECT
    d.document_id,
    d.document_name,
    CORTEX_ANALYST(d.document_text, 'Summarize key themes') AS summary
FROM
    large_documents d
INNER JOIN
    document_metadata m ON d.document_id = m.document_id
WHERE
    m.created_date >= DATEADD(month, -1, CURRENT_DATE())
    AND m.category = 'Financial Reports'
    AND m.status = 'Published'
    AND d.document_text IS NOT NULL
LIMIT 500;

This query only processes recent financial reports that are published and have actual text content. We’ve potentially reduced the dataset from millions to hundreds of rows before the expensive AI operation runs.

Smart Join Strategies

Joins can make or break your AISQL performance. Here’s what works:

Prioritize inner joins over outer joins – They reduce your result set immediately:

-- ✅ GOOD: Inner join reduces data early
SELECT
    c.customer_id,
    c.feedback_text,
    CORTEX_SENTIMENT(c.feedback_text) AS sentiment_score
FROM
    customer_feedback c
INNER JOIN
    active_customers a ON c.customer_id = a.customer_id
WHERE
    c.feedback_date >= '2025-01-01'
    AND a.subscription_status = 'Active';

Filter out test data explicitly – Don’t let test accounts pollute your AI analysis:

-- ✅ GOOD: Exclude test accounts
SELECT
    email,
    message_content,
    CORTEX_ANALYST(message_content, 'Extract action items') AS actions
FROM
    support_messages
WHERE
    email NOT LIKE '%@test.com'
    AND email NOT LIKE '%test%@%'
    AND user_type = 'Production'
    AND created_date >= DATEADD(week, -2, CURRENT_DATE());

Pre-Calculate and Store Embeddings

If you’re doing semantic search or similarity matching, generating embeddings on the fly is expensive. Instead, calculate them once and store them:

-- Step 1: Create a table with pre-calculated embeddings
CREATE TABLE product_descriptions_with_embeddings AS
SELECT
    product_id,
    description,
    CORTEX_EMBED_TEXT('e5-base-v2', description) AS description_embedding
FROM
    products
WHERE
    description IS NOT NULL;

-- Step 2: Use the pre-calculated embeddings for fast similarity search
SELECT
    product_id,
    description,
    VECTOR_COSINE_SIMILARITY(
        description_embedding,
        CORTEX_EMBED_TEXT('e5-base-v2', 'wireless headphones')
    ) AS similarity_score
FROM
    product_descriptions_with_embeddings
ORDER BY
    similarity_score DESC
LIMIT 20;

This approach transforms an expensive embedding calculation into a fast lookup. The difference can be dramatic—queries that took minutes might now run in seconds.

Optimize Your Table Structure

Set up clustering keys that align with your most common query patterns:

-- Cluster by fields you frequently filter on
ALTER TABLE customer_documents
CLUSTER BY (document_type, created_month);

-- Now queries filtering by these fields run much faster
SELECT
    document_id,
    CORTEX_ANALYST(document_content, 'Extract key dates') AS key_dates
FROM
    customer_documents
WHERE
    document_type = 'Contract'
    AND created_month >= '2025-01-01';

Size Your Warehouse Appropriately

AI workloads need more compute power than traditional SQL queries. Don’t be afraid to scale up:

-- Configure a dedicated warehouse for AI workloads
CREATE WAREHOUSE AI_ANALYSIS_WH WITH
    WAREHOUSE_SIZE = 'LARGE'
    AUTO_SUSPEND = 120
    AUTO_RESUME = TRUE
    INITIALLY_SUSPENDED = TRUE
    STATEMENT_TIMEOUT_IN_SECONDS = 7200;

-- Use it for your Cortex queries
USE WAREHOUSE AI_ANALYSIS_WH;

Start with a LARGE warehouse for AI tasks. You can always scale down if it’s overkill, but starting too small will frustrate users and mask optimization opportunities.

Common Mistakes to Avoid

#1: Using AI functions inside loops or repeated operations

-- ❌ BAD: Calling AI function for each row unnecessarily
SELECT
    product_id,
    (SELECT CORTEX_ANALYST(description, 'Extract features')
     FROM products p2
     WHERE p2.product_id = p1.product_id) AS features
FROM
    products p1;

Mistake #2: Not checking for NULL values

-- ❌ BAD: Wasting AI calls on empty data
SELECT
    CORTEX_ANALYST(user_comment, 'Analyze sentiment')
FROM
    feedback;

-- ✅ GOOD: Filter out NULLs first
SELECT
    CORTEX_ANALYST(user_comment, 'Analyze sentiment')
FROM
    feedback
WHERE
    user_comment IS NOT NULL
    AND LENGTH(user_comment) > 10;

Mistake #3: Ignoring warehouse resource monitors

Set up resource monitors to prevent runaway queries from draining your credits:

CREATE RESOURCE MONITOR ai_workload_monitor WITH
    CREDIT_QUOTA = 1000
    TRIGGERS
        ON 75 PERCENT DO NOTIFY
        ON 90 PERCENT DO SUSPEND
        ON 100 PERCENT DO SUSPEND_IMMEDIATE;

ALTER WAREHOUSE AI_ANALYSIS_WH
SET RESOURCE_MONITOR = ai_workload_monitor;

Monitoring and Maintaining Performance

Don’t set it and forget it. Regularly review:

  • Query execution times – Are they trending up?
  • Credit consumption – Any unexpected spikes?
  • Warehouse queuing – Are queries waiting too long to start?

Use Snowflake’s Query History to track these metrics:

-- Find your most expensive AISQL queries
SELECT
    query_text,
    execution_time,
    credits_used_cloud_services,
    warehouse_name
FROM
    SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE
    query_text ILIKE '%CORTEX%'
    AND start_time >= DATEADD(day, -7, CURRENT_DATE())
ORDER BY
    execution_time DESC
LIMIT 20;

Putting It All Together: A Real-World Example

Let’s say you need to analyze customer support tickets to identify trends. Here’s how to do it efficiently:

-- Create a materialized view for frequently accessed metadata
CREATE MATERIALIZED VIEW support_ticket_summary AS
SELECT
    ticket_id,
    customer_id,
    category,
    priority,
    created_date,
    status
FROM
    support_tickets
WHERE
    created_date >= DATEADD(year, -1, CURRENT_DATE());

-- Now run your AI analysis efficiently
SELECT
    s.ticket_id,
    s.category,
    s.priority,
    CORTEX_ANALYST(t.ticket_description, 
        'Extract: 1) main issue, 2) customer sentiment, 3) urgency level'
    ) AS ai_analysis
FROM
    support_ticket_summary s
INNER JOIN
    support_ticket_text t ON s.ticket_id = t.ticket_id
WHERE
    s.created_date >= DATEADD(week, -1, CURRENT_DATE())
    AND s.category = 'Technical'
    AND s.priority IN ('High', 'Critical')
    AND s.status = 'Open'
    AND t.ticket_description IS NOT NULL
LIMIT 1000;

This query:

  • Uses a materialized view for fast metadata access
  • Filters early on date, category, priority, and status
  • Checks for NULL values before calling the AI function
  • Limits results to a reasonable number

Key Takeaways

Optimizing Cortex AISQL queries isn’t rocket science, but it does require discipline:

  1. Filter aggressively before calling AI functions
  2. Pre-calculate embeddings for repeated use
  3. Use appropriate warehouse sizes for AI workloads
  4. Set up clustering keys aligned with your query patterns
  5. Monitor performance regularly and adjust as needed
  6. Exclude test data explicitly from production queries

The combination of traditional Snowflake optimization techniques with AI-specific strategies will give you fast queries and manageable costs. Start with these fundamentals, measure the impact, and iterate from there.

Additional Resources