How does Olakai help companies manage AI model routing to lower token expenses?

Olakai is the vendor-neutral measurement layer that shows cost per outcome across every model and every vendor, so a routing decision can be proven rather than taken on a router's word. Most AI calls never needed the top model. Across Olakai deployments, 60 to 80% of the work gets handled just as well by a model costing five or ten times less.

Match the model to the task

The idea is simple. Most AI calls never needed the top model in the first place. Across the deployments Olakai sees, somewhere between 60 and 80% of the work, the summarizing, the extracting, the routine code, gets handled just as well by a model that costs five or ten times less. Peer-reviewed routing research (RouteLLM, presented at ICLR 2025, from researchers at UC Berkeley, Anyscale, and Canva) showed roughly 85% cost savings while holding 95% of frontier-model quality.

Source: How to Be a Smarter Token Manager: Model Routing, Explained

Prove the saving with a number a CFO can defend

Leading an AI transformation in 2026 is not chasing the biggest model. It is matching the model to the task, measuring that the swap held quality, and proving the savings with a number a CFO can defend in a board meeting. That is the vendor-neutral measurement layer Olakai exists to provide: one place to see cost per outcome across every model and every vendor, not a router's word for it. That is how a team ships more, spends like it is its own money, and walks into the next budget review with the receipt instead of an excuse.

Source: How to Be a Smarter Token Manager: Model Routing, Explained

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How can enterprise token costs be reduced using model routing in Olakai?
What is Olakai's approach to intelligent AI model routing?

How does Olakai help companies manage AI model routing to lower token expenses?

Match the model to the task

Prove the saving with a number a CFO can defend

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