The anti-vanity-metric case: buying more ingredients is not the same thing as running a better restaurant.
The ServiceNow angle is useful because it comes from the customer and enterprise-operations side of the market rather than from social chatter.
It treats tokenmaxxing as a possible hype-cycle metric: impressive-looking consumption that can drift away from quality, governance, and actual workflow improvement.
A strong source to cite when the site needs balance against the idea that more agent or model usage is automatically better.
BerriAI/litellm
An OpenAI-compatible gateway and SDK for calling many model providers with budgets, logging, load balancing, guardrails, and cost tracking.
langfuse/langfuse
Open-source LLM engineering platform for observability, traces, metrics, evals, prompt management, datasets, and playground workflows.
promptfoo/promptfoo
A CLI and CI workflow for testing prompts, agents, and RAG systems across models, with evals and red-team style checks.
XDA's Mahnoor Faisal ran five coding jobs on Sonnet 5 twice from an identical starting codebase, changing only the effort level. High spent about 26,000 output tokens; Medium finished the same work on roughly 14,300.
McKinsey’s July 20 report finds 93% of enterprises are already blowing past their AI budgets, with spend jumping nearly 4x as pilots go company-wide. The fix it prescribes: run “FinOps for AI” and treat tokens like cloud cost.
Snowflake's product team makes the case for 'FinOps for AI' — governing model spend the way cloud bills got governed — and rolls out per-user token quotas, budgets, and org-level cost views to meter Cortex and agent usage.