Openkoda or Continual? A Comprehensive Review

Open-source platform

Pre-built application templates

Efficient business application development

Streamlines development cycles

Customizable templates

Avoids vendor-lock

Full code ownership

Assists with MVP development

Supports application modernization

Legacy system migration

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

Focus on business applications only

Requires technical knowledge for customization

Limited range of application templates

No mention of real-time collaboration

No indicated support for mobile platforms

Legacy system support unclear

Customization might still require coding

No clear debugging or testing tools

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