Quaneuron App
Control AI cost and reliability
in production.
The Planner helps you model AI unit economics before you ship. The Quaneuron App helps you keep costs sane after you ship, by showing spend and latency by feature, surfacing waste patterns like duplicates and retries, and giving your team guardrails.
Quaneuron never stores prompts or completions.
Only cost, latency, errors, and call patterns.
Built for teams shipping AI features into real products. Designed to reduce “invisible spend” that hides in
retries, duplicated calls, and oversized models.
Cost by feature
Latency p95 by route
Duplicate call detection
Retry loop alerts
Budgets & guardrails
Privacy-first telemetry
If your AI bill is “fine” until it isn’t, this is the layer that keeps you from learning about it too late.
Attribute spend to product reality
See cost and latency by feature, workflow, route, model, and environment, so your team can fix the biggest leak first.
Find waste patterns automatically
Detect duplicates, retries, and inefficient routing patterns that quietly inflate cost and degrade UX.
Put guardrails around growth
Budgets, thresholds, and “no surprises” visibility so scaling users does not accidentally scale spend 10×.
Ship faster with fewer arguments
Replace spreadsheet debates with a shared view of what production is doing. When cost spikes,
you can answer “what changed” without guesswork.
Privacy-first by design
No prompts, no completions. Quaneuron focuses on telemetry that matters for cost and reliability:
tokens, latency, errors, call patterns, and routing outcomes.
Your bill grows faster than usage
MAU is up 20% but spend is up 80%. You need attribution and pattern detection, not more guessing.
Latency is “fine” until it spikes
p95 is what users feel. If p95 jumps, you need to know which routes, models, and retries caused it.
You suspect retries and duplicates
Retries can turn one request into three calls. Duplicate calls can hide inside UI, polling, and backfills.
Model choices are made blind
“Let’s use the better model” is expensive when the impact is multiplied across your highest-volume paths.
FinOps can’t see inside AI flows
Cloud cost tools do not understand LLM routing, token spikes, or which feature created the spend.
You want to scale without fear
Guardrails let you grow usage while staying inside margins, instead of learning the hard way in month-end invoices.
Planner vs App
Use the right tool at the right time
The Planner is a free “before you build” tool. The Quaneuron App is the “after you ship” system for cost and reliability.
| Capability | Free Planner | Quaneuron App |
|---|---|---|
| When it helps most | Before shipping, planning pricing and margin | After shipping, controlling real production spend |
| Cost by feature / route | Not applicable | Yes, break down spend by workflow and feature |
| Latency and reliability visibility | Model assumptions only | Actual p50/p95 latency, errors, retries |
| Detect duplicates and retry loops | No | Yes, identify silent burners |
| Guardrails | No | Budgets, thresholds, alerting patterns |
| Access | Open, no login required | Requested access (approved in cohorts) |
Start with the Planner. When you are shipping AI into production and you need ongoing visibility and guardrails,
request access to the Quaneuron App.
Examples
The kinds of problems this surfaces
These are the “quiet failures” that inflate cost and degrade UX. Quaneuron is built to make them visible.
Duplicate calls hiding in UI
A chat screen triggers multiple background fetches and replays the same prompt, doubling spend without obvious errors.
Quaneuron flags duplicates by fingerprint and shows the route that emitted them.
Retries turning 1 request into 3 calls
Timeout handling retries too aggressively. The user sees “slow”, the bill sees “triple”.
Quaneuron surfaces retry loops and correlates them with latency spikes.
Oversized model on the hottest path
A high-quality model is used everywhere by default. On your highest-volume workflow, that becomes the dominant cost driver.
Quaneuron makes the tradeoff visible by feature and model.
“Why did costs spike yesterday?”
A deployment changed a prompt template and token count jumped. Quaneuron shows the time window, the workflow,
and the token delta driving spend.
Routing drift over time
Your router starts favoring a more expensive model as usage shifts. Without visibility, you only notice at invoice time.
Quaneuron highlights changes in model mix and cost per request.
Does Quaneuron store prompts?
No. Quaneuron focuses on cost, latency, errors, and call patterns. Prompt and completion content is not stored.
Who is the App for?
Teams shipping LLM features into production who need visibility and guardrails. If your AI usage is growing,
you want this before the bill becomes a surprise.
Is the Planner enough?
The Planner is perfect for forecasting and pricing decisions. The App is for real production behavior:
attribution, waste detection, and operational guardrails.
How do I get access?
Request access below. Access is approved in cohorts so onboarding stays tight and feedback stays useful.
Request access to the Quaneuron App
If you are shipping AI features in production, tell us your stack and what you are seeing.
Access is approved in small cohorts so we can onboard teams carefully.
If you only need planning and pricing, the free Planner is ready now.
If you only need planning and pricing, the free Planner is ready now.
No drip spam. If we reach out, it will be personal.