Measure every AI dollar. Then cut the waste.
trAIce meters every LLM call and every AI tool your team uses, attributes each dollar to the feature, customer, that customer’s users, team, and agent behind it, then hands you the specific change that lowers the bill: cost-validated model swaps, cache savings, and waste alerts you can act on.
No credit card · open-source SDK · 5-minute install
Live demo data · last 30 days
- No gateway in your request path
- <1 ms local SDK overhead per tracked LLM call at p99 Learn more
- Open-source SDK
- Prompt capture is opt-in · 14-day TTL
- Product + employee spend in one workspace
AI spend is growing faster than the systems used to manage it.
Engineering sees model spend. Finance sees invoices. IT sees SaaS commitments. CTOs and VP R&D leaders still cannot point to the customer, feature, team, or employee that changed the budget.
Which customers are profitable on AI?
Your dashboard says “OpenAI: $4,012/mo.” That’s not an answer. You need cost per customer, joined to the revenue they pay.
Your bill jumps 40% and no one can explain why
A new feature? A chatty customer? A retry loop? A model you forgot to downgrade? Aggregate spend hides the leak.
Which team or employee pushed AI spend over budget?
Seat commitments, metered tools, and raw API usage land in different systems. Leaders need one team-level view and alerts before a burst becomes the monthly baseline.
What AI costs to make.
Start from your application’s context: feature, customer, and run. Do not start from a billing export. That’s the difference between “how much did the API cost” and “which feature is unprofitable.”
Cost vs revenue, per account.
Every call tagged to a customer and its users, joined to the revenue they pay.
Cost per run, step, and retry.
- Retry cascade $184
- Runaway agent loop $132
- Duplicate spend $96
What your team spends using AI.
Beyond what AI costs to build, see employee usage from Codex, Claude Code, and internal sources you send through the API. Compare teams day by day, combine metered usage with seat commitments, and alert when an employee crosses budget.
Three operating views in the real platform UI.
Each workspace opens with fresh fictional data, current alerts, and read-only controls.
Customer AI-margin leak
Northstar Helpdesk AI finds Initech driving more resolver cost than its customer revenue safely supports.
Internal AI spend abuse
The same Northstar workspace shows 131 people across 12 startup teams, with department, role, source, budget, and anomaly evidence.
Product + employee AI spend
Northstar gives leaders one Team workspace for customer-facing AI economics, verified savings, and internal employee usage.
From measurement to the actions that cut spend.
We surface each saving with evidence and you apply it. Some levers you can turn on to act automatically. Nothing in your stack changes without your say, and we stay out of your request path.
One platform for both sides of company AI spend.
Gateways route. Evals score. Observability traces. Spend tools count seats. None of them connect what you build to what you buy.
| What you want | trAIce | AI gatewayPortkey, LiteLLM | EvalsBraintrust, LangSmith | ObservabilityDatadog, Helicone |
|---|---|---|---|---|
| AI cost per customer / tenant | No | No | Partial | |
| AI gross margin (cost vs revenue) | No | No | No | |
| Cost per agent run / tool step | Partial | No | Partial | |
| Cost-validated model swaps | No | Partial | No | |
| Prompt-cache hit rate & savings | Partial | No | Partial | |
| Employee / team AI tool spend | No | No | No | |
| Both build-side and buy-side AI cost | No | No | No |
Why not my AI gateway?
A gateway routes traffic and can total your spend, then stops. We sit beside it as the meter and the optimizer: attribute every dollar to the feature, customer, that customer’s users, and agent, then turn it into savings. Routing is a different job, and these pair well with us. We are deliberately not in your request path.
Why not LLM observability?
Observability shows you the number and the trace, then leaves the “now what” to you. We pair the measurement with the levers that bring cost down: cache insights, cost-validated model swaps, and waste caught in loops and retries.
Why not an evals tool?
Eval tools tell you whether a change is good, not what it costs. When we surface a cheaper model you see the quality evidence and the cost delta side by side, so safe and cheaper are one decision.
Why not my provider’s dashboard?
One provider, one page, after the bill lands. We unify every provider, attribute spend to the feature, customer, user, and agent that drove it, and alert in real time as a cost starts climbing.
Why not tag spend in my cloud cost tool?
Cloud cost tools bucket by service or account. “OpenAI: $4,012/mo” is not an answer. We start from your app’s context (feature, customer, run) so you see which feature is unprofitable, not just how much the API cost.
Buy-side spend tools (Torii, Zylo) and AI-FinOps (Amnic, Finout) cover half of this. None tie it back to the product you ship, or turn it into a specific change that lowers the bill.
Built to measure your spend, not hold your prompts.
You get the full product on metadata alone. Prompt content is opt-in, and trAIce never sits in your request path.
Metadata by default
We run on tokens, cost, latency, and the tags you attach (feature, customer, user, run). Raw prompts and outputs are never ingested unless you explicitly turn capture on.
Prompts stay opt-in
Prompt capture is off by default. When you enable it, samples carry a 14-day TTL and are deleted immediately if you opt out.
Never in your request path
Events are sent after your LLM call, so trAIce cannot slow it down or take it offline. Under 1 ms local SDK overhead per tracked call at p99.
See the benchmarkKeys and certification
trAIce API keys are stored as SHA-256 hashes; provider keys you connect for replay are encrypted with AES-256-GCM. We are not yet SOC 2 certified: Type I is on our near-term roadmap, and we will walk your security team through our controls today.
Read the full detail on our security and data handling page.
Three people, three jobs, one platform.
Own the full AI operating budget.
See product economics and employee usage together, with accountable teams, active budget alerts, and a clear path from anomaly to action.
Know which team, workflow, or person changed spend.
Daily team breakdowns, employee budgets, and deep-linked logs make cost reviews concrete without turning managers into billing analysts.
Optimize with evidence, not blanket limits.
Trace cost to customer, feature, agent run, employee, and team. Validate model changes and apply targeted budgets where they matter.
“Provider invoices tell you how much. We built trAIce to tell you which customer, feature, and employee, so you can act before the bill arrives.”
The trAIce team
Good questions, straight answers.
See where your AI budget really goes.
Open the demo with no signup. See a customer margin leak, an employee budget spike, and a unified product + employee workspace.
50,000 events/month free, no credit card.