Opportunities › Cost-efficient AI inference for developers › Proposed app
Penny Gauge
Replay your real prompts on any model and see what each one costs per task
- Score
- 63
- confidence 51
- Evidence
- 12
- items on the page
- Rivals
- 2
- named in sources
- First version
- 30 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2020
- Established market · AI estimate
ScreensConcept
Desktop app: Comparing LLM models and analyzing cost reports involves detailed analytical work with dense data and dashboards best done at a computer. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Replay results
Best value: Model A$0.84
About this appAI proposal
Penny Gauge helps developers building on large language model APIs see what a task really costs before they switch models. Import a sample of your own prompts, run them on the models you are considering, and get cost per finished task, not just price per token.
It also watches live usage. Set a budget per task type, and Penny Gauge flags runs that cost far more than your baseline, such as a newer model using many more tokens than the one it replaced. Optional routing sends easy requests to cheaper models.
It differs from a price table because it measures your own tasks: tokens used, retries, and a quality check you define. You decide the trade-off with numbers from your work.
- Tagline
- Know what a task costs before you commit to the model.
- Voice
- PlainPreciseFrugal
- Why this name
- A penny stands for small costs adding up, and a gauge measures. Two plain words that say what the product does.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Cost per task, not per token
Replay your own prompts on several models and compare total spend per completed task, including reasoning tokens and retries.
No member yet
Quality checks you define
Add pass/fail rules, test commands or a judge prompt, so a cheaper model only wins if it still passes.
No member yet
Surprise-cost alerts
Set a baseline per task type and get alerted when a model or setting costs several times more than expected.
No member yet
Effort setting sweeps
Run the same task at low, medium and high settings and see where extra spend stops improving results.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#FAF6ECLedger Cream · surface#1E2A27Ink · text#B8602BCopper · primary#14625CDeep Teal · accent#E6DCC4Sand · borders and panels#B3261ESignal Red · alert data
Typography
Aa
Penny Gauge
PricingAI proposal
The material gives no competitor prices for tools like this, only complaints about model token costs. These tiers are a starting point to test, not a market benchmark.
Free
Free
- 1 project
- 50 replay runs a month
- Cost comparison on 3 models
- Public report links
Solo
$19 a month
- Unlimited projects
- 1,000 replay runs a month
- Cost alerts
- Effort setting sweeps
Team
$79 a month
- 5 seats
- Routing and fallbacks
- Budget caps
- Shared baselines
- Priority support
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$190,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Users publish read-only comparison reports, showing cost per task across models, to teammates, in pull requests and in forum posts. Each report carries a link to run the same test on your own prompts.
- Do they become customers?
- Readers are developers with the same problem, so they could become paying users. To test: how many report viewers run their own replay, and how many of those start a paid plan.
- First customers
- Small teams of 2 to 10 engineers shipping an AI coding or agent feature on a frontier model API, where one person owns the monthly model bill.
- Where to reach them
- Hacker News and developer forums: Post real cost-per-task comparisons across new model releases, with links to replay them.
- Open-source agent communities: Offer free replays for popular agent projects and share the public reports.
- Direct outreach to AI startups: Message founders and tech leads who discuss model costs publicly, offering a free audit.
- First milestone
- Ten teams run replays on their own prompts and at least three pay for Solo or Team within 60 days.
Why people already pay for this
- Public claimDevelopers face unexpected high costs when using newer AI models, with token consumption significantly exceeding prior expectations based on older model pricing.
- Public claimHigh token costs are a barrier to adoption of newer AI models, with price being a decisive factor in whether developers use them.
- Public claimToken cost varies significantly with model capability settings, and even medium settings can incur unexpectedly high charges on newer models.
- EstimateOpenRouter: about 5% on top of inference spend
- Estimate
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Background jobsQueue with workers for long replay runsReplaying many prompts across models takes minutes and must survive page closes.
- This app · Secrets storageEncrypted storage for users' model API keysUsers bring their own provider keys, which must be encrypted and never logged.
RisksAI inference
- CommercialProviders and existing observability tools may add cost comparison and routing, and developers may judge a standalone tool not worth paying for.
- TechnicalA routing proxy sits in the request path, so outages or added latency would hurt customers' production apps.
- DataReplaying real prompts may expose private code or customer data; users need clear controls over storage and redaction.
- AIPass/fail and judge-based quality checks can be wrong, so a cheaper model may look good while quality quietly drops.
- PlatformProvider terms, rate limits and frequent pricing or model changes require constant upkeep of the model catalog.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.