Passio or Continual? A Comprehensive Review

Customizable SDKs

Compatible with iOS and Android

Integrated mobile data collection

Tailored vertical-specific vision modules

User-defined data taxonomy

Supports data annotation

Complete end-to-end development

Module for Mindseye

Accommodates brand-specific user interfaces

Easy deployment on mobile

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 to mobile platforms

Specific industry focused modules

Might need specific technical knowledge

No clear pricing information

Dependent on user-collected data

Lack of tool integration

Limited customization options

App-dependent performance evaluation

SDK testing required

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