Continual is an operational AI platform designed to help build predictive models on modern data stacks. Users can easily create predictive models on data stored on popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks. With Continual, there is no need for complex engineering or infrastructures such as MLOPS platforms, as it simplifies the process of building and maintaining predictive models through simple SQL or dbt declarations. Features can also be shared across teams, which accelerates model development. Models built on Continual improve continually, so predictions are always up-to-date. Data and models are stored directly on the warehouse, making it easily accessible to operational and BI AI Tools. Continual is versatile and can be useful in a business to predict customer churn, inventory demand, and customer lifetime value. Continual is designed for modern data teams who love SQL and dbt, but it is also equally accessible to data scientists who can extend the platform by integrating Python. Continual’s declarative approach to AI makes it easy to work with for analytics and data teams.
Pros
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 AI Tools
Suitable for customer churn, inventory demand, and customer lifetime value predictions
Equally accessible to data scientists
Facilitates Python integration
Shared features accelerate model development
Simplifies process of building and maintaining predictive models
Models are up-to-date
Centralized feature store
Extensible with Python
CI/CD friendly
GitOps workflow support
Zero infrastructure requirement
Works natively with modern cloud data platforms
Declarative model and feature definition
dbt integration
Cons
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
