Manage cost without sacrificing accuracy

With Primer’s BabyBear framework you can deploy deep learning models up to 10x cheaper at enterprise scale.

Primer BabyBear

Cheap inference triage for expensive language models

Read the research paper(Opens in a new window)

Why it matters

Deploying large language models at scale can incur significant cost, especially when running on-premises. Primer’s BabyBear optimization framework dramatically reduces expenses, selecting the best AI model for the job at the lowest cost without sacrificing performance or accuracy.

How it works

The principle is simple: minimize deep learning costs for specific tasks by cutting unnecessary usage. Primer’s BabyBear optimization framework automatically identifies such cases.


Hoverable Cards

  • Maximize Value, Minimize Spending

    As documents flow in for processing, the faster and more economical deep learning BabyBear model provides a confidence score for completing the task. BabyBear takes care of high-confidence, simpler tasks while more complex tasks get passed to the larger model, MamaBear. For the majority of AI tasks, Primer's BabyBear model can lead to significant cost savings at enterprise scale.
  • Customize Accuracy Targets

    Set your desired accuracy threshold and tell BabyBear how much to optimize for cost savings. Whether your F1 score target is 10%, 5%, or 0%, BabyBear optimizes compute usage by dynamically adjusting its confidence threshold to match.


AI-enabled knowledge discovery

Learn more about Primer Delta