Lingtual or Continual? A Comprehensive Review

No coding required

Text to actionable commands

Integrates with various applications

Fast setup

Supports OAuth2 authentication

REST APIs

databases integration

Built-in connectors (e.g. Gmail

Slack)

Custom connectors creation

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 coding might limit customization

Limited built-in connectors

Doesn't support non-RESTful APIs

Lack of native mobile support

Limited language model options

Slack and Gmail focus

Dependency on APIs for functionality

Complexity in self-hosting

Dependency on external messaging platforms

Limited out-of-box adaptability

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