Manage cost without sacrificing accuracy
With Primer’s BabyBear framework you can deploy deep learning models more cost effectively at enterprise scale.
Primer BabyBear
Cost optimized inference triage for expensive language models
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
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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.
Why choose Primer
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Cost efficiency
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Minimize latency
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High performance
Resources
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