Backender or Continual? A Comprehensive Review

No-code back-end development

Auto-generates databases

Uses modern NoSQL technology

File storage capabilities

Processes small-medium files

Media storage

Easy API endpoint management

Simple function editor

Build complex logic with ease

Seamlessly integrates third-party services

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

No SQL technology only

Limited file storage size

Pricing based on usage

No explicit version control

Limited third-party integrations

Auto-generated database lacks customization

No support for microservices

Documentation auto-generation may be insufficient

Manual API endpoints connection

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