AI Optimization Calculator

Describe the workload

Pick a use case or set your own numbers. Everything here is an aggregate workload profile. No prompts, no PHI, and nothing leaves this page.

Cases per month 50K

Model calls per case 20

Avg tokens per call 10K

Routine share of the work 80%

Blended price / 1M tokens $5

Low routine share — that changes the verdict. Most of this work is novel reasoning, which is exactly where deep models and long chains earn their keep. Expect a modest reduction here, and treat that as the correct answer, not a failure of the exercise.

Step 1 of 3

Apply optimizations

Optimizations vary by prompt and starting workflow. Toggle levers off or drag sliders to align the assumptions with your own experience. Treat this as a thought exercise.

Every call

Optimize prompts with RLMs

Recursive language models tune and compress prompts against a scored objective, so each call carries less context for the same output quality. Anchored to RLMOpt, Autonomize research.

Prompt compression 30%

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Routine cases

Ground knowledge in a Context Graph

Compile SOPs, coverage policies, and measure logic once into a structured representation agents navigate deterministically. Routine cases stop re-deriving what the enterprise already validated: open-ended model calls collapse to a residual few, and the execution path becomes inspectable and auditable.

Residual calls per routine case 2

Document versions compiled / mo 2

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Remaining calls

Route routine calls to expert SLMs

Fine-tuned, self-hosted small models take the well-understood calls that remain. A specialist model needs far less instruction scaffolding per call, and self-hosting reprices every token that stays, modeled here at 12% of your blended frontier rate.

Share of calls routed to SLMs 65%

Scaffolding shrink per routed call 40%

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Step 2 of 3

Autonomize AI Token Optimization Calculator

Your report

Based on the workload and levers you have selected, here's a directional read on the cost savings and where that freed capacity could go next.

Executive summary

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is the projected annual cost for this workload post-optimization, –

Tokens per month

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Annualized savings

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Model calls per routine case

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fewer steps, fewer places to drift

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Where the savings could go

Redeploy freed budget into more impact

Assuming the same blended token rate, here is what those monthly savings could fund instead:

Monthly Token Breakdown

Assumptions on the table

Step 3 of 3

What should your AI cost?

What Should Your AI Cost?