AI costs can escalate quickly when usage grows faster than the budgets, visibility, and controls needed to manage it. As generative AI moves from pilots into production, every interaction consumes tokens, and costs rise with usage, context, latency, and workflow complexity.
In the inference era, the goal is not simply to access capable models. It is to deliver useful intelligence repeatedly, economically, and under control.
That means looking beyond token price to useful output per dollar, delivered at the speed the business requires. Achieving this demands the right model, accelerator, and deployment path for every workload.
Core42 Compass makes this possible by routing, governing, and optimizing AI consumption across diverse silicon, turning inference economics from a source of risk into a managed capability.
WHAT YOU'LL LEARN:
Why lower token prices do not always lead to lower total AI spend.
Why useful output per dollar matters more than token price alone.
How model, accelerator, and deployment choices affect inference economics.
Why no single accelerator is right for every AI workload.
How Compass provides cost visibility, governance, and control.
How to assess your organization’s readiness for production-scale AI.