Cortex Search
What is Cortex Search?
Cortex Search is Snowflake’s managed semantic search capability inside the Snowflake Cortex suite.
Teams use Cortex Search to retrieve relevant text, documents, and records from Snowflake tables using meaning-based queries rather than exact keyword matching.
Search runs directly on data stored in Snowflake. No external vector databases. No separate retrieval services.
Where Cortex Search fits in the Snowflake stack
Cortex Search sits between raw data storage and AI applications.
It indexes text data stored in Snowflake tables and exposes search endpoints that applications, notebooks, and AI workflows can query.
Because Snowflake hosts the data, indexing, and execution, access controls and security policies remain unchanged.
Search does not introduce a parallel system.
How Cortex Search works
Cortex Search follows a structured flow:
- Data ingestion
Teams store text data such as documents, logs, tickets, or descriptions in Snowflake tables. - Index creation
Cortex Search builds a semantic index on selected columns. - Query execution
Applications or analysts submit meaning-based queries rather than keyword filters. - Result retrieval
Snowflake returns the most relevant rows based on semantic similarity.
All computation stays inside Snowflake’s environment.
What Cortex Search is good at
Cortex Search works well for retrieval-heavy use cases:
- Searching knowledge bases stored in Snowflake
- Powering retrieval for RAG pipelines
- Finding relevant support tickets or incidents
- Enabling semantic lookup across documents
- Improving search accuracy where keywords fall short
Teams often pair Cortex Search with LLM workflows that require reliable context retrieval.
What Cortex Search does not replace
Cortex Search does not replace full-featured search engines.
Advanced ranking customization, complex query syntax, and ultra-low-latency global search still belong to dedicated search platforms.
The tool also does not clean or curate content. Poor data quality leads to poor retrieval.
Cost and execution behavior
Cortex Search introduces two main cost drivers:
- Index creation and storage
- Query execution during search requests
Costs scale with data volume, index refresh frequency, and query volume.
Without monitoring, teams struggle to understand which applications drive search spend and which indexes deliver real value.
Governance and security model
Cortex Search inherits Snowflake’s governance model.
Search results respect:
- Role-based access
- Row-level and column-level policies
- Data sharing boundaries
Users cannot retrieve content they lack permission to access, even through semantic queries.
Cortex Search vs external vector databases
| Area | Cortex Search | External Vector DBs |
|---|---|---|
| Data location | Snowflake tables | Separate system |
| Governance | Snowflake-native | Tool-specific |
| Setup | SQL-driven | Infrastructure-heavy |
| Integration | Snowflake Cortex | App-managed |
| Cost visibility | Warehouse-based | Usage-based APIs |
Many teams choose Cortex Search to reduce architectural sprawl when Snowflake already acts as the system of record.
Operational risks teams overlook
Several issues appear after adoption:
- Indexes created on low-value text
- Stale indexes serving outdated content
- Multiple teams building overlapping indexes
- Search workloads consuming unexpected compute
Search feels lightweight. At scale, it behaves like any other warehouse workload.
How SeemoreData complements Cortex Search
Cortex Search retrieves relevant data.
SeemoreData explains what search workloads cost and how teams use them.
With SeemoreData, teams can:
- Attribute search queries to applications and teams
- Track index-related warehouse usage
- Identify unused or low-impact indexes
- Connect search results to upstream data pipelines
That visibility keeps semantic search sustainable as usage grows.
When Cortex Search makes sense
Cortex Search fits when:
- Snowflake already stores large text datasets
- Teams build RAG or AI retrieval workflows
- Governance must stay centralized
- Search accuracy matters more than keyword matching
For ultra-low-latency or internet-scale search, other tools remain a better fit.
Bottom line
Cortex Search brings semantic retrieval directly into Snowflake.
But indexes, queries, and refresh cycles still consume compute and budget.
Teams that pair Cortex Search with usage attribution and cost visibility gain search capabilities without losing operational control.
