Stack AI or Continual? A Comprehensive Review

Rapid creation of bespoke apps

Efficient custom LLMs deployment

Multiple applications creation

Easy LLMs and database connection

Workflow templates library

Allows experimentation with prompts

Fine-tuning for product Specific needs

Trusted by leading companies

Quick API delivery

Supports data loading from various sources

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

Limited template library

Relies heavily on LLMs

No explicit offline support

Latency may vary

No explicit multi-language support

Lack of dedicated mobile apps

No direct email integration

Insufficient documentation provided

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