On July 23, 2026, Snowflake announced the general availability of the SHOW CORTEX BASE MODELS command. This SQL command allows users to list the Cortex Base Models available in their account, along with each model’s lifecycle status, regional availability, legacy date, and end-of-life date. It also helps determine which models the current role can use.
As Cortex continues to expand its catalog of large language models and related AI capabilities, transparency into model availability and lifecycle becomes essential for developers, platform teams, and governance stakeholders. This detailed post examines the command’s value, its role in AI observability, early use cases, competitive context, and implications for enterprise model management in 2026.
Practical Value for AI Developers
Before this command, understanding which models were available, their status, and regional support often required consulting documentation or external resources. SHOW CORTEX BASE MODELS brings that information directly into the SQL interface developers already use.
Key Benefits
- Instant visibility into usable models within the account.
- Clear lifecycle status (GA, Public Preview, Legacy, End of Life).
- Regional availability details.
- Legacy and end-of-life dates for planning migrations.
- Role-aware results showing models accessible to the current user.
This reduces friction and helps teams make informed choices about model selection and long-term support.
Why Model Discoverability and Governance Matter
As the number of available models grows and organizations deploy more Cortex-powered applications and agents, several challenges emerge:
- Developers may not know which models are production-ready.
- Teams risk building on models approaching end-of-life.
- Regional differences can affect multi-region deployments.
- Governance and compliance teams need clear inventories of AI assets.
SHOW CORTEX BASE MODELS addresses these issues by providing a programmatic, auditable view of the model catalog.
Fitting into Broader AI Observability
Model discoverability is one piece of a larger observability picture that includes:
- Usage tracking and cost monitoring.
- Performance and quality metrics.
- Governance and access controls.
- Lifecycle and deprecation management.
By making base model information queryable, Snowflake strengthens the foundation for more advanced AI observability and management practices.
Early Use Cases
Common applications of the command include:
- Development Planning — Selecting models with appropriate capability, cost, and support status.
- Migration Planning — Identifying models approaching legacy or end-of-life status.
- Compliance and Inventory — Generating reports of available AI models for audits.
- Role-Based Access Review — Understanding what models different roles can access.
- Multi-Region Architecture — Checking regional availability for global deployments.
These use cases help both individual developers and centralized platform teams.
Command Syntax and Output
Typical usage follows standard Snowflake SHOW command patterns:
SQL
SHOW CORTEX BASE MODELS
[ LIKE '<pattern>' ]
IN [ SCHEMA ] SNOWFLAKE.MODELS;The result set includes columns such as lifecycle status, available regions, legacy date, and end-of-life date, giving teams a comprehensive view of each model.
Comparisons to Prior Cortex Management Features
Snowflake previously provided model information through documentation and regional availability tables. SHOW CORTEX BASE MODELS elevates this to a first-class, queryable system command. It aligns with other SHOW commands that help users discover accounts, objects, and services, bringing AI model management into the same operational paradigm.
Competitive Context Versus Other AI Platforms
Many AI platforms offer model catalogs through web consoles or APIs. Snowflake’s approach of exposing this information via a native SQL command is particularly valuable for teams that live in SQL and want consistent, scriptable access.
Combined with Cortex’s governed execution model, this creates a more integrated experience for enterprise users who prioritize both capability and control.
Implications for ML Engineers and Platform Teams
For ML and AI Engineers
- Faster, more confident model selection.
- Better awareness of support timelines.
- Easier integration into automated workflows.
For Platform and Governance Teams
- Improved inventory and audit capabilities.
- Clearer communication of model lifecycle policies.
- Stronger foundation for internal AI catalogs and standards.
Actionable Insights
- Incorporate SHOW CORTEX BASE MODELS into regular development and review processes.
- Build monitoring or alerting around approaching end-of-life dates.
- Use the command in documentation and onboarding materials.
- Combine results with usage data for more complete AI asset management.
- Establish internal guidelines for preferred models based on lifecycle status.
What This Signals for Enterprise Model Management in 2026
The general availability of SHOW CORTEX BASE MODELS reflects a broader maturation of enterprise AI platforms. As organizations move from experimental use of LLMs to production systems, they need the same operational rigor applied to traditional software assets — discoverability, lifecycle management, and governance.
Snowflake’s investment in this area signals continued focus on making Cortex a production-grade environment for AI workloads.
Conclusion
SHOW CORTEX BASE MODELS GA delivers greater transparency into Snowflake’s AI model catalog at a time when discoverability and governance are increasingly important. By providing a simple, SQL-native way to list models along with their lifecycle and regional details, the command helps developers and platform teams make better decisions and operate with greater confidence.
As Cortex and the broader AI Data Cloud continue to evolve, features that improve visibility and control will remain essential. Organizations that adopt these capabilities early will be better prepared to manage growing AI estates responsibly and effectively.