Dify or Continual? A Comprehensive Review

Supports GPT family models

Easy-to-use graphical interface

Text-based tools provided

Converts natural language to SQL

Creative scripts generation

Develops chatbots

Specified tones chat feature

Provides answers to tricky questions

Backend as a service API

Single API interface provided

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 model support

No custom model training

Limited language support

Focused on text-based tools

Limited dialogue types open-sourced

No apparent security features

Complex for non-technical users

Backend-as-a-service might limit flexibility

Dependent on third party APIs

No specified autoscaling features

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