Most AI cost tools answer two questions: how much you spend and who used it. TokenomicsIQ answers the third: why it is happening. It uncovers the structural patterns driving waste across your API calls, prioritises them by estimated monthly savings versus fix effort, and shows how your costs scale as usage grows.
Who is this for
TokenomicsIQ is for organisations where AI API spend has outgrown informal oversight. That usually means more than one team is running its own workflows on frontier models, the monthly bill is climbing faster than the business case behind it, and leadership has moved from asking “what is this?” to “what are we actually getting for it?” Company size matters less than spend pattern. If AI is now embedded across multiple functions and no one has mapped token spend at workflow level, the diagnostic will find recoverable cost.
Case study
TQ Solutions, a Future of Work and Workforce Research consultancy, ran a TokenomicsIQ diagnostic on their live usage data. The results were clear.
Read the full case study →“TokenomicsIQ provided a snapshot diagnostic on the efficiency of our AI usage. The report clearly identified waste in our workflow and, importantly, prioritised the biggest cost savings and how to action them. As we continue to build out our AI capabilities, having a grip on the AI costs without impacting our output becomes business critical.”
GFGareth FlynnFounder and CEO, TQ Solutions
TokenomicsIQ Report Features
When AI agents loop unnecessarily, retry redundant calls, or orchestrate parallel sub-tasks that duplicate work, every call is billed in full. Standard spend dashboards cannot see this pattern. The TokenomicsIQ Diagnostic is designed specifically to surface it. Including;
Identifies rapid repeated API calls with near-identical content: retries, polling via full API calls, and parallel sub-tasks that duplicate work. Each redundant call is billed in full. In TQ Solutions' first diagnostic, the agent loop pattern accounted for 100% waste on the affected workflow.
Flags agentic sub-tasks routed to frontier models where a cheaper model produces equivalent output for that task type. Named monthly saving per workflow, not a generic recommendation. Agentic systems are the most common source of this pattern at scale.
Every finding includes a spend projection at 3x and 5x usage. Agentic costs compound faster than standard API usage because inefficient patterns multiply with every autonomous task cycle. The trajectory shows the board-level number before it arrives.
Sample report
The report is the product. The sample below is based on a mid-sized SaaS team running multiple AI workflows across engineering and operations. Companies at this stage typically find 30 to 50% recoverable spend in their first diagnostic. Your report follows the same format, generated from your own data.
View sample reportHow it works
No integration. No credentials. No access to your systems. You export the data, we analyse it, you get a report.
Export your usage data from OpenRouter, Anthropic Console, OpenAI, or TypingMind. A two-minute task from your provider dashboard or app. No API keys required, no access to your production systems.
Our engine classifies every API call by workflow type, model, token volume, and cost. Each call is matched against the cheapest adequate model for that task. Inefficiencies are ranked by monthly saving.
A structured PDF and HTML report personalised to your stack: spend by model and named workflow, a ranked list of savings recommendations with engineering implementation notes, and risk flags for vendor concentration and model lifecycle exposure.
Pricing
TokenomicsIQ is a fixed-fee diagnostic, not a subscription. Run it once, implement the recommendations, run it again when you want to measure progress.
Baseline diagnostic. One report. Fixed fee.
Partner access
TokenomicsIQ is currently available to a select group of valued partners as we refine the reporting pipeline. If you are actively managing AI API spend and want early access, tell us about your stack and we will be in touch.
We received your request and will be in touch soon.
Insights
Practical analysis for engineering and finance teams managing LLM infrastructure costs.
Most teams know their AI bill is growing. Few know what is driving it. This guide covers what makes AI token spend structurally different, the four waste patterns that account for most recoverable spend, a five-step framework for taking control, and what the numbers look like at 3x and 5x current usage.
Read the full guide →AI spending has moved from a discretionary engineering line to a boardroom conversation in under two years. Uber burned its annual AI budget in four months. A San Francisco fintech saw $81,267 in a single week. This guide covers the scale, the visibility gap, and what the companies managing it well are actually doing differently.
Read the full article →Model switching, prompt compression, caching. These are the right levers. But without knowing which workflows are driving waste, pulling them is guesswork. The teams that get AI cost optimization right diagnose before they configure.
Read the full article →Most FinOps teams now track AI spend. Very few can explain which workflow to fix first, or what fixing it is worth. Visibility and attribution answer the ownership question. Diagnosis answers the one that matters: what is structurally wrong, and what do you change first?
Read the full article →Mavvrik and Benchmarkit surveyed 396 companies for their 2026 State of AI Cost Governance Report. 40% escalated an unexpected AI cost event to the board. 33% froze spending. 25% cancelled a programme outright. Only 11% can predict AI costs within 10% accuracy, down from 15% last year. The forecasting is getting worse, not better.
Read the full article →Visibility is not the same as diagnosis. A dashboard that shows spend by model cannot tell you whether those model choices were right, whether your agent loops are redundant, or whether your system prompts are carrying 4x more tokens than they need to. Organisations with structured AI spend management typically carry 30-60% recoverable waste, invisible at the dashboard level.
Read the full article →Bank of America is calling it a shift from tokenmaxxing to token rationing. BCA Research, writing in the FT this week, calls it the end of the token subsidy. For three years, AI model makers priced their products well below cost to drive adoption. That era is ending, usage-based pricing is now the direction of travel, and companies that have not mapped their spend at workflow level are walking into the new model blind.
Read the full article →A new word is entering the enterprise vocabulary. Uber capped engineer AI spend after burning through its annual AI budget in four months. A San Francisco fintech confirmed one employee spent $81,267 in a single week. Tokenminimizing is what happens when leadership finally looks at the bill.
The companies navigating this well are not capping first and asking questions later. They are running the diagnostic first.
Read the full article →Ramp raised $750M and launched AI Spend Intelligence in June 2026. It is a monitoring layer built for Ramp customers. It surfaces the data. It does not produce the diagnostic. Here is exactly how the two compare, and where the gaps are.
Read the full article →System prompt bloat is one of the most common and least visible sources of wasted AI API spend. In our first live diagnostic, it was the single largest inefficiency identified: on 43% of API calls, the system prompt alone accounted for more than 30% of all tokens sent per request.
Read the full article →The token invoice captures one cost bucket. There are eight others. This guide explains what AI token cost actually is, how the per-million pricing model works across model tiers, and where the hidden spend sits across all nine buckets in a typical AI deployment.
Read the full article →Most engineering teams managing LLM infrastructure reach a point where the monthly API bill is significant enough to attract finance attention, but not well understood enough to defend or optimise. This guide covers the practical steps to audit your token spend without requiring new tooling or integrations.
Read the full article →GPT-4o mini is roughly 16 times cheaper than GPT-4o for the same token volume, and OpenAI's Batch API cuts pricing in half on both. This guide breaks down what OpenAI actually charges per million tokens, how the cost compounds at scale, and where the prompt caching opportunity sits.
Read the full article →The shift from subscription AI to consumption-based billing is accelerating. Cloudflare, Ceramic.ai, and You.com are already live on attribution-based models. For enterprise teams, pay-per-token removes the subsidy that made AI costs predictable, and makes token spend management non-optional.
Read the full article →Gareth Flynn, CEO of TQ Solutions, ran a TokenomicsIQ diagnostic on his practice's actual usage data four weeks into building an AI-first operation. The report answered the four governance questions every AI-investing business should be asking before spend scales.
Read the case study →Common questions
AI spending covers all costs a company incurs from AI: API costs from providers like OpenAI, Anthropic, and Google, AI SaaS subscriptions, and the infrastructure costs of running AI workloads. It grows faster than expected for three reasons: usage compounds non-linearly as products gain adoption; most teams lack workflow-level visibility so inefficiencies accumulate undetected; and the token subsidy that kept frontier model prices artificially low during 2022-2025 is ending. UBS research from June 2026 found that 60% of companies actively managing AI budgets have already moved to cheaper models in response to cost pressure. Full analysis here.
AI token cost, also called LLM token cost, is the per-token price charged by AI API providers for model usage. Providers bill separately for input tokens (the text you send, including your system prompt and conversation history) and output tokens (the text the model generates). The token invoice is one of nine cost buckets in a typical AI deployment. The other eight do not appear on any vendor bill, which is why teams that manage only what they see on the invoice are usually working with an incomplete picture. Full breakdown here.
Identified at FinOps X in June 2026: (1) Token Invoice, the only one on any vendor bill; (2) Infrastructure overhead; (3) Orchestration cost; (4) Prompt processing waste, including system prompt bloat; (5) Caching inefficiency; (6) Agent loop redundancy; (7) Model-task mismatch; (8) Sync-on-batch overhead; (9) Uncached context. TokenomicsIQ analyses all nine in every diagnostic and ranks recommendations by estimated monthly saving.
OpenRouter CSV exports, Anthropic Console usage exports, OpenAI dashboard exports, and Typing Mind JSON conversation exports. Analysis quality is highest with OpenRouter data, which includes per-request token counts, named application context, and caching data. Any limitations in the source data are noted transparently in the report methodology section.
Six sections: a personalised executive summary, spend breakdown by model and named workflow, inefficiency analysis ranked by monthly saving, specific recommendations with engineering implementation notes, risk flags covering vendor concentration and model lifecycle exposure, and a methodology section. Both PDF and HTML formats included.
Under 30 minutes from upload to download for most datasets. Larger exports (over 100,000 rows) may take up to 45 minutes.
No. Your usage export is processed entirely in memory and deleted once your report is downloaded. We do not store, index, or analyse client data beyond the duration of the report generation process.
The tool works with Anthropic Console and OpenAI dashboard exports. The analysis is slightly less granular for these sources because their standard exports do not include per-request prompt content or application-level attribution. We note this transparently in the report. Typing Mind exports provide rich conversational context and work well for teams using AI assistants across client workflows.
Yes. The delta report ($1,250) runs the same analysis on your new usage data alongside your original export and shows exactly what has changed: saving confirmed, remaining opportunities, and any new patterns since the baseline. You re-upload both CSVs when you are ready. We never hold your data between sessions.