Your AI feature is one bad month away from killing your gross margin.
The Token Economics Workbook is the planning-side discipline most teams discover three months too late. A forecasting calculator, a model routing matrix, cache-hit patterns, a gross margin protection playbook, and 15 production teardowns with real cost-per-invocation math. One afternoon to install, every month afterward to compound.
Illustrative figures from a representative teardown. The workbook ships with 15 real production examples and the forecasting model that produced these numbers.
The prototype costs $40 a month. The production feature costs $14,000.
Almost every team ships their first AI feature the same way: the most capable model on the menu, default prompt structure, no caching, no routing logic, no per-tenant ceilings. It works. It impresses the demo. It also produces a cost-per-invocation that looks innocuous until usage compounds, and then a single Tuesday afternoon spike turns into a panicked Slack thread from the CFO.
By that point the options are bad. Rip out the feature. Raise prices. Cap usage and watch customers churn. Or stand up an emergency optimization sprint that ships a week late and breaks something else along the way. The teams that avoid that fork aren’t smarter — they just installed cost discipline before shipping, not after.
Average gap between prototype and production cost-per-invocation across teardowns in the workbook.
The point at which most teams discover they have a margin problem — usually one usage spike too late.
Cost reduction range across the 15 teardowns with no measurable change in output quality.
Clear about the lane. No inflated promises.
- A planning-side discipline you install before (and after) shipping.
- A forecasting calculator that lets you model unit economics in an afternoon.
- A model routing matrix and a set of cache patterns that survive price changes.
- A 22-page gross margin playbook a CFO will actually read.
- 15 anonymized production teardowns with the actual math, not estimates.
- An observability platform. Helicone, Langfuse, Vellum, and OpenLLMetry already do that well.
- A code library you install. The forecasting sheet is yours; it runs offline.
- A vendor pitch. The routing matrix names the cases where Claude loses to GPT or Gemini.
- A theoretical paper. Every dollar figure in the teardowns is a real production number.
- A subscription. One-time $59 with 12 months of rate-card updates.
Five deliverables. One install afternoon.
Drop in your token volumes, output ratios, and per-model rates. Output: side-by-side unit economics across Claude, GPT, and Gemini at three scale points (10K / 100K / 1M invocations/month). Google Sheets + Excel.
Decision tree for when Haiku beats Sonnet beats Opus, when GPT-4o-mini wins, when Gemini Flash wins on context, and when a small open-weight model on your own GPU is the right answer. Notion clone link + printable PDF.
A code repository with copy-paste TypeScript and Python patterns for prompt caching, response caching, semantic cache layers, and per-tenant cache isolation. With cost math on each pattern.
22-page PDF written for a CFO to read in one sitting. Covers unit economics framing, margin protection levers, pricing-side responses, and the three KPIs to put on the dashboard week one.
Notion database. Each teardown: feature description, original architecture, cost-per-invocation before, specific changes applied, cost-per-invocation after, deployment date, team size, and the catch nobody saw coming.
When Anthropic drops Haiku pricing or OpenAI launches a new tier, you get the updated rate card. Update one cell in the forecasting sheet; every projection re-flows.
What one teardown actually looks like.
Below is an abridged version of Teardown #07 from the workbook — an AI customer support triage feature deployed at a B2B SaaS company handling roughly 50,000 tickets a month. Full teardown in the workbook includes the prompt diffs, the cache configuration, and the rollback plan.
- Claude Opus on every ticket, default temp.
- ~1,500 input tokens (full ticket + KB context) · ~200 output tokens.
- No prompt caching. No response caching.
- No routing — the easy 80% of tickets ran the same expensive path as the hard 20%.
- Two-stage: Haiku classifier triages 80% of tickets; Sonnet only on the hard 20%.
- Prompt caching on the system prompt and KB context (~1,200 of the 1,500 input tokens).
- Response cache on a 4-week semantic window for top-15 recurring issue patterns.
- Output-token ceiling enforced; structured response schema prevents runaway generation.
When each model is the right answer — and when it isn’t.
The full matrix in the workbook is a Notion decision tree with 23 branch points across three providers. Below is the top-level grid you walk first.
The full matrix expands each row into a sub-tree covering output token ceiling, latency budget, structured-output requirements, eval-quality threshold, and per-tenant cost ceiling. The workbook also includes the failure-mode notes — the spots where a cheaper model looks identical on quick spot-checks and silently degrades on production traffic.
Every teardown ships with the math.
Each row below is a production AI feature, anonymized but real. The delta column is the cost-per-invocation reduction after applying the routing, caching, and prompt-structure changes documented in the teardown.
The integrity moat.
Exactly what you get for $59, and what you don’t.
- Cost-per-invocation modeling and forecasting.
- Model selection, routing, and provider trade-off analysis.
- Prompt caching, response caching, and semantic cache patterns.
- Output-token discipline and structured-response patterns.
- Gross margin framing for non-engineering stakeholders.
- Latency engineering. Important, separate problem.
- Eval framework design. Covered in the Prompt Evaluation & Versioning System ($49).
- Agent orchestration patterns. Covered in the Agent Orchestration Cookbook ($79).
- Building observability infrastructure. Use Helicone, Langfuse, or OpenLLMetry.
- Tax, financial, or fundraising advice. Talk to your CPA and your board.
Model the cost per token, then watch the whole spend.
This models cost at the token level; the rest of the AI-spend stack zooms out. The AI Cost-Per-Task Calculator rolls tokens up to the price of a finished task, the AI Burn-Rate & Budget Blowout Forecaster projects that spend forward to the month you'd blow the budget, and the AI Spend Runaway & Billing-Safeguard Gate checks a leaked key or retry loop can't blow it overnight.
The questions teams actually ask before they trust the model.
Those are observability tools. They tell you what your AI feature spent AFTER you spent it — usually after a CFO has already noticed. The Token Economics Workbook is the planning-side discipline that lives upstream of observability: a forecasting calculator, a routing matrix, and cache-hit patterns you apply BEFORE you ship. The two layers are complementary. Many of our teardowns assume you have observability running; the workbook is what you do with the numbers once you have them.
All three. The routing matrix explicitly covers when Claude Haiku beats Sonnet, when Sonnet beats Opus, when GPT-4o-mini beats GPT-4o, when Gemini Flash wins on context length, and when paid hosted models lose to a small open-weight model running on a $400/month GPU. The forecasting calculator is provider-agnostic — drop in your token volumes and the per-model rates and it produces side-by-side cost projections across all three vendors.
Real production features. Each teardown documents the original architecture, the cost-per-invocation before optimization, the specific changes applied (model routing, prompt restructuring, cache patterns, output capping, etc.), and the cost-per-invocation after — usually with the production deployment date and the team size. Identifying details are anonymized where required; the math is not.
A spreadsheet. Google Sheets and Excel versions are both included. The decision was deliberate: a $59 product that becomes a SaaS dependency for a team's planning is a worse product, not a better one. The sheet is yours, it runs offline, and you can fork it into your own internal tooling without asking us for an export.
The pricing inputs are a single cell per model — when Anthropic drops Haiku pricing or OpenAI launches a new tier, you update one number and every projection in the workbook re-flows. The structural advice (when to route, what to cache, how to think about cost-per-invocation as a margin lever) outlasts any specific price point. You also get free updates to the rate cards for 12 months.
Three audiences. (1) Founders or engineering leads about to ship an AI feature, who want to model unit economics before launch instead of after. (2) Teams already in production whose AI line item just got CFO-flagged and need a 30-day plan to cut burn without breaking the feature. (3) Agencies and consultants building AI features for clients who need defensible cost models in proposals. If you're none of these, the workbook is probably overkill.
All four where each serves the deliverable best. The forecasting calculator is a spreadsheet. The routing matrix is a Notion page (clone link included) plus a printable PDF decision tree. The cache-hit patterns are a code repository with copy-paste TypeScript and Python snippets. The 15 teardowns are a structured Notion database. The gross margin playbook is a 22-page PDF written for a CFO to read in one sitting.
Yes. 30-day no-questions refund. If the workbook doesn't pay for itself in identified savings within 30 days of you actually opening it, email RedHub AI support and we refund. We can count on one hand the refunds requested across the catalog to date — the math tends to be self-evident once people open the forecasting sheet.
One afternoon to install.
Every month afterward to compound.
Open the forecasting sheet. Drop in your token volumes. Pick a teardown that matches your feature. You’ll have a 30-day plan to cut burn before dinner.
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