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Understanding how to optimize token cost requires looking at the equation for calculating cost per million tokens. In this equation, many enterprises evaluating AI infrastructure focus on the numerator: the cost per GPU per hour. For cloud deployments, this is the hourly rate paid to a cloud provider; for on-premises deployments, it’s the effective hourly cost derived from amortizing owned infrastructure. The real key to reducing token cost, however, lies in the denominator: maximizing the delivered token output. That denominator carries two business implications. Minimize token cost: When thi

In healthcare, tedious, time-consuming tasks like medical coding, documentation and managing insurance forms cut into the time doctors can spend with patients. Sully.ai helps solve this problem by developing “AI employees” that can handle routine tasks like medical coding and note-taking. As the company’s platform scaled, its proprietary, closed source models created three bottlenecks: unpredictable latency in real-time clinical workflows, inference costs that scaled faster than revenue and insufficient control over model quality and updates. To overcome these bottlenecks, Sully.ai uses Basete

Sentient Labs is focused on bringing AI developers together to build powerful reasoning AI systems that are all open source. The goal is to accelerate AI toward solving harder reasoning problems through research in secure autonomy, agentic architecture and continual learning. Its first app, Sentient Chat, orchestrates complex multi-agent workflows and integrates more than a dozen specialized AI agents from the community. Due to this, Sentient Chat has massive compute demands because a single user query could trigger a cascade of autonomous interactions that typically lead to costly infrastruct

Customer service calls with voice AI often end in frustration because even a slight delay can lead users to talk over the agent, hang up or lose trust. Decagon builds AI agents for enterprise customer support, with AI-powered voice being its most demanding channel. Decagon needed infrastructure that could deliver sub-second responses under unpredictable traffic loads with tokenomics that supported 24/7 voice deployments. Together AI runs production inference for Decagon’s multimodel voice stack on NVIDIA Blackwell GPUs. The companies collaborated on several key optimizations: speculative decod