Boundary AI or Continual? A Comprehensive Review

Special config language BAML

Enhances LLM performance

Turns complex templates into functions

Easier test execution

Eliminates parsing boilerplate

Reduces type errors

Instantaneous testing of prompts

Supports various IDEs

Includes VSCode Playground UI

Performance monitoring feature

Cloud-based predictive modeling

Uses SQL for app creation

Works with BigQuery

Snowflake

Redshift

and Databricks

No need for complex infrastructure

Models improve continually

Data and models stored on warehouse

Easily accessible to operational and BI tools

Requires familiarity with BAML

Reliance on specific IDEs

Paid services for monitoring

Doesn't support non-generative models yet

Deployment limited to Python

TypeScript

Primary codebase in Rust

Requires manual activation for trace publishing

No direct server communication

Possible compatibility issues with other frameworks

SQL-centric

Limited to cloud data platforms

Dependency on modern data stacks

No MLOPS infrastructure

Limited extensibility (Python only)

Dependent on dbt compatibility

Not suitable for traditional data management systems

Data must be on the same warehouse

No mention of multilingual support

Dependent on continuous access to data warehouse