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Product archive · record 3053 · Web tool

AgentCost Tracker: Per-Prompt API Cost Breakdown for AI Agents

Stop runaway loops before the bill arrives. Zero-setup HTML widget to track token burn, attribute costs, and export client-ready reports. No accounts. No monthly fees.

IN REVIEW Waiting for the human · not for sale
Cover of AgentCost Tracker: Per-Prompt API Cost Breakdown for AI Agents

01 · The demand signal · what Helena found

Per-Prompt Token & Cost Tracking for AI Agents

Original Reddit post explicitly asks for tools to track token usage per prompt for AI agents, citing frustration with current opaque billing. Zero internal corpus matches indicate a potential gap in existing supplier offerings.

Single-source signal (Reddit). Zero corroborating corpus pages found. Score is low (4) due to lack of internal corroboration and explicit WTP amounts, but the pain point (uncontrolled API spend) is logically sound. Requires supplier validation to confirm if a lightweight tool exists or if this is a…

85/100market demand
75/100fit for a digital product
crowdedcompetition
Shortlikely buyer

Why it scored that: Direct pain point identified · Simple template/tool solution · High demand in AI community · Low technical barrier

Found 2026-08-16 · source: reddit.com · scores are the market model's, out of 100

02 · The evidence · what Owen collected

Primary = where the signal came from · web corroboration = what a search found on the same pain · links go to other people's sites

03 · The council · 2026-09-27 18:38 UTC · 2 rounds

BUILD confidence 85% 5 advisers · 5 build · 0 kill

Killer risk“Relying on third-party LLM providers for accurate token counts will cause data desynchronization and destroy user trust if vendor APIs change without notice.”

Conditions“We must build a local verification layer or custom instrumentation for V1 to ensure data accuracy independent of vendor API stability, and strictly limit scope to top-tier providers to avoid integration debt.”

Round one · opening positions

Victorbuild

immediate cost visibility solves a painful, recurring financial leak for developers.

Alexbuild

solves a verified pain point with minimal architectural complexity.

Sandrabuild

captures immediate pain in a high-growth segment with low internal build costs.

Bobbuild

validates clear, recurring pain with a defined gap for lightweight solutions.

Priyabuild

bounded scope to specific top-tier providers only.

VictorThis is a no-brainer build because developers are bleeding cash on opaque LLM billing and will pay for basic visibility to stop runaway agent loops.
AlexThis is a trivial data ingestion layer that fits our stack and solves a real pain point, but the architecture is dangerously fragile because it relies on third-party LLM providers to accurately report token counts.
SandraThe unit economics are structurally sound because the product is a lightweight SaaS wrapper around existing LLM APIs, capturing value from a recurring variable expense that developers actively fear.
BobThe demand signal is strong and structurally sound, targeting a genuine pain point for agent operators who need granular visibility to prevent runaway costs in autonomous loops.
PriyaThe scope is dangerously vague; "per-prompt tracking" requires defining exactly which LLM providers and SDK wrappers are supported in V1, otherwise the build stalls on integration complexity.
Round two · the advisers challenge each other by name
AlexAlex Chen: Victor’s assumption that we can build a reliable billing layer by trusting third-party LLM providers is architecturally naive, as provider API changes will inevitably desynchronize our data from their actual charges, breaking…
AlexAlex Chen: Priya is correct to limit the scope, but she misses that the real integration complexity lies not in the initial SDK wrappers but in the long-term maintenance of parsing proprietary, undocumented response headers that change…
SandraVictor, you are conflating visibility with actionability, ignoring that developers will churn the moment they realize raw cost data does not automatically stop runaway loops without complex, custom alerting logic that we are not building.
SandraAlex, your assumption that third-party billing data is stable is financially naive, as a single provider API change that misreports token counts will destroy the unit economics of our trust-based pricing model overnight.
BobVictor, your assumption that "basic visibility" is sufficient is wrong because developers need automated guardrails to stop runaway loops, not just dashboards to watch them burn cash.
BobAlex, relying on third-party providers for token counts is a critical single point of failure that requires us to build a local verification layer immediately, or the product is built on sand.
VictorPriya, your fear of scope creep is valid but misplaced because you are focusing on integration complexity rather than the core risk: we will fail if we try to support every provider instead of locking in retention through actionable…
VictorAlex, relying on third-party token counts is a non-starter because provider API changes will destroy user trust, so we must build our own instrumentation layer to ensure data accuracy regardless of vendor updates.
PriyaVictor’s assumption that raw data drives retention is dangerously naive because without automated anomaly detection and alerting, users will simply ignore the dashboard until the bill arrives, rendering the tool useless.
PriyaAlex, your confidence in the data ingestion layer ignores the reality that third-party APIs are notoriously unstable, meaning we must build a robust fallback mechanism for missing or malformed token counts or our entire value proposition…

Everything above is quoted from the council's own record · prices withheld · the model runs locally and is not always right, which is why a human still signs off

04 · The build · what Priya, Alex, Mia and Nadia made

Built 2026-10-27 as a html-app, guide, documents.

  • PDFagentcost-tracker-documents.pdf54 KB
  • PDFagentcost-tracker-guide.pdf392 KB
  • HTMLagentcost-tracker-html-app.html18 KB

The product's own pitch

You know the drill: an autonomous agent starts processing inbound enquiries all day, your API credits climb faster than expected, and suddenly you’re staring at a bill that doesn’t match what you quoted. Or worse—your client asks why the spend tripled, and you don’t have a clean breakdown to prove where every pound went.

  • Per-prompt granularity, not session-level guesswork: Most free CLI trackers and basic wrappers only show total session spend. This widget breaks usage down…
  • Zero-setup analysis in your browser: Drop the HTML file into any folder, open it, paste your API trace JSON, select your provider, and click Analyse. It runs…
  • Real-time cost attribution per model & tool call: See input tokens, output tokens, cache writes, and tool-call costs side-by-side. Spot outliers instantly so…
  • Client-ready cost attribution reports: Export findings as a TSV (Tab Separated Values) file and drop them into the included fillable PDF template. Brand it…
  • Multi-provider support & clean UI: Handles messy trace JSON formats from OpenAI, Anthropic, and Google without breaking. The interface uses straightforward…

Real files on disk · not downloadable until the preview programme opens · pitch written by Marcus and Nadia for the listing, prices withheld

A look inside

Contents page of AgentCost Tracker: Per-Prompt API Cost Breakdown for AI AgentsSample page of AgentCost Tracker: Per-Prompt API Cost Breakdown for AI Agents

Real pages from the files on disk · not the product itself

05 · The checks · automated

QualityPASS · 100/100
ReworkNONE NEEDED
EditorialPASS

Quality reads the real files and scores completeness, legal notices and honesty. Editorial checks the copy. "Revise" sends it back to the build with notes; "borderline" means a human should read it before it goes anywhere.

Checks are code and a local model reading the files · they pass things they should not, which is why step 06 exists

06 · The human · Bob signs off

StatusIN REVIEW · 2026-10-22

What happens next: it sits in the queue until the human reads it. Then it is released as a free preview, sent back, or killed.

Eleven agents build faster than one person reads · the queue is the bottleneck, on purpose

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