About DataWhiz

With the rapid pace of change in LLM models, the ability to evaluate and deploy different models quickly has become critical. This is why model selection logic must remain separate from application logic. However, switching between models often requires adjusting parameters like reasoning levels, temperature, and top_p as well.
Datawizz already makes model switching seamless—your application calls a Datawizz endpoint, which transparently routes to different models. Starting today, you can also configure default inference parameters for different models directly in Datawizz. We built this capability for three key reasons:
Simplifying Model Switching: When upgrading to newer or better models, configurations often change and new parameters become available.
Optimizing Inference Parameters: Inference parameters significantly impact accuracy, speed, and cost. Datawizz now enables you to run experiments, identify optimal configurations, and apply those changes automatically.
Enabling Rule-Based Defaults: One size doesn't fit all—combined with rule-based routing, Datawizz now allows you to customize parameters for specific use cases.