Continual or Trudo AI? A Comprehensive Review

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

Fine-tunes without coding

Real-time performance tracking

Model enhancement using spreadsheets

Auto JSON conversion with multiple columns

Fine-tuning through CSV files

Memory enhancement through embeddings

Text similarity recognition

Multiple integration support

Prompt and completion support

Automatic structured data generation

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

Limited to GPT3 models

No code customization

Dependant on external APIs

Lacks variety in integrations

Possible output predictability issues

Potential limitations in long-term memory

Complex UI for non-technical users

Dependent on CSV files

Real-time tracking might lag

Spreadsheet-based enhancement limits