sqlsure

What is sqlsure?

sqlsure vs Similar AI Tools

	sqlsure	DeepSQL	xarray-sql	Polygres
Pricing Model	Free	Custom Pricing	Free	Free, Freemium
Free Credits
Key Features	Detects fan-out double-counting (FANOUT rule) Detects chasm traps from multiple fan-out joins (CHASM rule) Validates additivity of measures (ADDITIVITY rule)	Workload monitoring and anomaly detection Slow query identification and optimization Natural language querying to SQL translation	SQL interface for Xarray datasets Round-trip between Xarray and SQL Supports GROUP BY, JOIN, window functions, and more	Full PostgreSQL 17 instance with ACID compliance Native graph traversal via pgGraph HNSW vector search with scalar filtering via pgVector
Pros	Deterministic and auditable results Zero false positives on benchmark audits	Quick one-line installation and setup in 15 minutes Self-hosted ensures data stays within your infrastructure	Bridges SQL and array-based scientific data Open source and free	Combines relational, graph, and vector capabilities in one database Open source core components (PostgreSQL, pgGraph, pgVector)
Cons	Requires a semantic model or rulebook to be defined Currently limited to rule set v0.1	Requires self-hosting and VPC setup No free tier or trial mentioned	Experimental project Limited documentation and community	Relatively new platform with limited community support Requires learning pgGraph syntax for graph traversal
Best For	Data engineers ensuring query correctness AI developers building text-to-SQL systems	Database administrators Data engineers	Data scientists and researchers working with geospatial or climate arrays Users who prefer SQL over array programming	AI agents needing structured, relational, and semantic data Developers building knowledge graphs with vector search

How to use sqlsure?

1. 1Install: pip install sqlsure
2. 2Define a semantic model from dbt, PK/FK, or introspect from database
3. 3Check queries with check() or CLI: python -m sqlsure.cli --model model.json query.sql

sqlsure Key Features

• Detects fan-out double-counting (FANOUT rule)
• Detects chasm traps from multiple fan-out joins (CHASM rule)
• Validates additivity of measures (ADDITIVITY rule)
• Checks join keys against declared relationships (JOIN_KEY rule)
• Warns on cross joins and undeclared joins
• Policy checks for sensitive columns (SENSITIVE_COLUMN)
• Provides machine-actionable fix suggestions
• Deterministic, offline, no data access required

sqlsure Use Cases

• CI/CD gate for SQL queries in data pipelines
• Guardrail for AI text-to-SQL agents
• Auditing dbt repos for semantic correctness
• Embedding into data products for query safety

sqlsure Pricing & Free Credits

sqlsure currently operates on a Free model.

sqlsure Pros & Cons

What is sqlsure best for?

• Data engineers ensuring query correctness
• AI developers building text-to-SQL systems
• Analytics teams preventing silent data errors

sqlsure FAQ