Baseplate or Continual? A Comprehensive Review

High-performance retrieval workflows

Unified hybrid database

Easy management of vectors

API for connecting data

UI for configuring data

Version control feature

Supports text

images

documents

Simplified backend wiring

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

Requires extensive backend knowledge

Possible over-complication for smaller projects

Limited customer support channels

Specifically designed for LLM apps

No offline mode

Limited UI customization options

Unified database may present data confusion

Potential for stale data issues

May not suit non-data-centric businesses

No explicit support for non-English languages

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