Cortex AI SQL
What is Cortex AI SQL?
Cortex AI SQL is Snowflake’s SQL-native interface for invoking AI and LLM capabilities directly inside queries.
Data teams use Cortex AI SQL to summarize text, classify records, extract entities, translate content, or generate responses without moving data outside Snowflake or calling external APIs.
Everything runs where the data already lives, inside Snowflake, using SQL.
Where Cortex AI SQL fits in the Snowflake stack
Cortex AI SQL sits at the intersection of analytics and machine learning inside Snowflake Cortex.
Instead of building pipelines that export data to Python services or third-party models, teams call AI functions inline as part of SELECT, INSERT, or UPDATE statements.
The warehouse executes the query. Snowflake handles model access, security, and execution context.
That architecture keeps sensitive data inside Snowflake’s boundary.
How Cortex AI SQL works
Cortex AI SQL extends standard SQL with AI functions.
A typical flow looks like this:
- Input data selection
A query selects text, logs, tickets, documents, or descriptions from Snowflake tables. - AI function call
The query applies a Cortex AI function, such as summarization or classification. - Model execution
Snowflake routes the request to managed models within the Cortex framework. - Result materialization
The output returns as structured columns that downstream queries can reuse.
The entire flow stays declarative. No SDKs, no orchestration layers, no outbound data movement.
Common Cortex AI SQL functions
Cortex AI SQL supports several high-impact patterns:
- Text summarization for tickets, emails, and logs
- Classification for tagging, routing, or prioritization
- Entity extraction from unstructured text
- Language translation
- Content generation for descriptions or responses
Teams often chain these functions with existing SQL logic to enrich tables at scale.
What Cortex AI SQL is good at
Cortex AI SQL works best for batch or semi-batch enrichment tasks.
Strong use cases include:
- Enriching support tickets before analytics
- Tagging product feedback at ingestion time
- Classifying logs or incidents for downstream alerts
- Preparing text data for BI and search
The SQL-first approach fits teams that already operate warehouse-centric pipelines.
What Cortex AI SQL does not solve
Cortex AI SQL does not replace full ML workflows.
Training custom models, tuning prompts interactively, or building agent-style systems still require external tools.
Latency-sensitive, real-time inference also sits outside its sweet spot.
And SQL authors still need to understand query scope, cost, and execution behavior.
Cost and execution behavior
Every Cortex AI SQL function call consumes Snowflake compute and AI service credits.
That leads to a few realities:
- Large tables multiplied by AI calls can explode cost
- Re-running enrichment queries repeats AI spend
- Poor filtering pushes unnecessary rows through models
Without usage tracking, teams struggle to connect AI enrichment to actual warehouse spend.
Governance and security model
Cortex AI SQL inherits Snowflake’s governance controls.
Queries respect:
- Role-based access
- Masking and row-level policies
- Data sharing constraints
No raw data leaves Snowflake for unmanaged endpoints, which reduces compliance risk compared to external AI APIs.
Cortex AI SQL vs external AI pipelines
| Area | Cortex AI SQL | External AI Pipelines |
|---|---|---|
| Data movement | In-warehouse | Data exported |
| Interface | SQL | SDKs and APIs |
| Governance | Snowflake-native | Tool-specific |
| Latency | Batch-oriented | Real-time capable |
| Cost visibility | Warehouse-driven | API-driven |
Many teams combine both, using Cortex AI SQL for enrichment and external services for interactive workloads.
Operational risks teams miss
Several issues surface after rollout:
- Analysts apply AI functions across entire tables by mistake
- Enrichment jobs rerun without deduplication
- Multiple teams duplicate similar AI logic
- Costs rise without clear ownership
AI inside SQL feels simple, but misuse scales fast.
How SeemoreData complements Cortex AI SQL
Cortex AI SQL enriches data.
SeemoreData explains the operational impact.
With SeemoreData, teams can:
- Attribute AI SQL queries to teams and pipelines
- Track AI-driven warehouse spend over time
- Identify repeated or wasteful enrichment jobs
- Connect enriched columns back to upstream sources
That visibility helps teams use Cortex AI SQL intentionally instead of discovering cost spikes after the fact.
When Cortex AI SQL makes sense
Cortex AI SQL fits when:
- Snowflake already powers analytics and pipelines
- Teams prefer SQL over custom services
- Data sensitivity limits outbound AI calls
- Enrichment runs at scale, not per-request
For interactive chat or real-time inference, other approaches fit better.
Bottom line
Cortex AI SQL brings AI enrichment directly into SQL workflows inside Snowflake.
But AI inside queries still behaves like queries, with cost, scale, and ownership attached.
Teams that pair Cortex AI SQL with usage attribution and cost visibility avoid surprises and keep enrichment work sustainable as adoption grows.
