Token economics is the discipline of understanding what a piece of AI work actually costs, in the tokens a model reads and writes, and deciding deliberately which tasks should run on rented models and which should run on infrastructure you control. It connects a technical unit (the token) to a financial one (your AI budget).
Turn your AI spend into a number you can plan on.
AI is the only line in your budget that grows when the business succeeds, and nobody can tell you by how much. Thoughtworks helps you match workloads to the right models, move the mission-critical workloads onto infrastructure you control, and a variable, vendor-set bill becomes a cost you can forecast and justify with ROI.
The challenge
Token spend doesn't grow in a straight line
Once teams start relying on AI day to day, usage compounds month over month, and left unmanaged, that curve grows expotentially. Do nothing, and the run rate can double again within a year.
The spend itself isn't the problem. Spend without a plan is. Once you know which tasks need your most expensive models and which don't, you can route work deliberately instead of paying premium rates for everything by default. The bill still grows as you do more with AI. It just grows on your terms.
If you're reading this, you're probably living one of three moments right now:
How we help clients
See the spend
Where the tokens go, by team, product, and task. You get a real map of consumption before you touch anything.
How we deliver it:
Instrumented against your AI gateway; a real consumption picture inside the first weeks, not a survey.
Diversify your models
Introduce open weight models to your portfolio and move high volume workloads. Save 50-90% compared to using closed frontier models.
How we deliver it:
Each workload is benchmarked against your own quality bar, then rolled out gradually and monitored before scaled adoption.
Get the hardware
Spend discipline doesn't end at go-live. We keep the routing tuned as usage, models, and prices shift.
How we deliver it:
Routing rules and cost thresholds get revisited on a set cadence as prices and usage shift. Never a set-and-forget system.
Read our latest research
By Thoughtworks AI LabsChoosing and changing models requires evidence of how they behave under real conditions. Thoughtworks AI Labs investigates model evaluation, interpretability, robustness and control. Its researchers publish findings and, where available, code and datasets so others can examine the methods and build on the results.
Our work includes more than 1,000 experiments on how changes in a query affect retrieval-augmented generation, including publications across NeurIPS, ICLR, AAAI, ACL and EMNLP, and in IEEE publications.
FAQ
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Once a workload is high-volume and stable, the closed-API premium you're paying for often stops being justified. A misallocation audit, profiling spend by workload, is the fastest way to find out.
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There is no single number — it varies widely by workload mix and how far along the AI journey a company is. We’ve seen the tipping point is typically between a quarter and a half a million dollars US for a single workload per year. For many organizations, they crossed that line on dozens of workloads over a year ago.
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Our engagements typically target 30%+ reduction in coding AI spend, measured as cost per completed task with quality safeguards held against a baseline.
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Token cost optimization is the fastest, most contained entry point, it fixes what a workload runs on today. AI Factory is the fuller operating model: gateway, governed multi-model serving, and the infrastructure to own more of your intelligence as volume grows.