How much of your AI spend is waste?
Enter three numbers. We build your spend profile, run Revenium's AI Insights detectors against it, and show you the optimized footprint — the same analysis Revenium runs continuously against your actual metered transactions.
Where does your money go today?
Monthly figures, best estimates are fine. We derive your full spend profile from three numbers — on the pattern we see everywhere: ~80% of AI spend runs through engineering.
Your AI economy, assembled.
Three numbers in, a full footprint out. We attribute 80% of spend to engineering — coding assistants, product APIs, and agents — and the rest to AI use across the wider company.
11 detectors, running now.
Revenium's AI Insights engine reads metered AI traffic and surfaces ranked, dollar-quantified findings. Waste detectors carry a dollar floor; hygiene and risk detectors fire at any spend. Overlapping findings are de-duplicated.
Your spend, with Revenium.
The same workload, with the findings above applied: caching on, retries contained, models right-sized, dormant spend retired, and guardrails watching every key against its own pattern.
Where the savings come from
| Detector | What it found | Est. monthly |
|---|
How the math works · assumptions per detector
This simulation applies conservative, industry-grounded rates to a spend profile derived from your three inputs. Each rate is an assumption made visible — the production AI Insights engine computes these from your actual metered transactions instead of assuming them.
- Profile derivation — 80% of total spend is attributed to engineering use of AI, split 60% coding assistants, 25% product APIs, 15% agents. The remaining 20% splits evenly between knowledge-worker assistants and automation.
- Prompt Cache Miss — ~70% of API/agent cost is input tokens; ~55% of that is a cacheable prefix; roughly half of eligible traffic runs uncached today; cache reads are priced ~90% below input tokens at an assumed 75% hit-rate.
- Retry Waste — ~4.5% of API/agent spend goes to retries of errored calls; ~70% is avoidable with back-off and circuit-breaking.
- Truncation — ~3% of calls end on a max-token stop; each carries a 1.5× expected re-run cost.
- Model Task Fit — ~20% of API/agent calls are oversized for the task, routable to models ~45% cheaper. For assistants: ~70% of tokens run on flagship models, ~30% of that work routes to efficient models ~70% cheaper (flagship budgets).
- Outdated Model Versions — ~12% of spend typically sits on superseded model versions with a newer version ~30% cheaper, token-for-token.
- Dormant & Underused Spend — ~2.5% of API/agent spend shows zero successful outcomes; ~3% of assistant seats sit dormant.
- Agent Graph Amplification — circular calls, wide fan-out, and deep chains typically burn ~4% of agent spend.
- Error Concentration — root-caused and de-duplicated under Retry Waste (no double counting).
- Provider-Deprecated Model, Model Awareness, Subscriber Concentration — risk and advisory findings; they fire without a dollar floor and carry no savings estimate here.
Per-detector results carry a deterministic ±8% profile adjustment seeded from your inputs, and the total is calibrated to the 20–40% waste band observed across real assessments. Engineer-count benchmarks adjust assistant-side findings.
Directional estimates for illustration, not a quote. The production assessment is computed from your actual metered transactions — every finding is auditable, with confidence, severity, affected cost, and sample transaction IDs behind it. If you ask “how do you know?”, we pull the transactions and show you.
Stop estimating. Start measuring.
Connect your coding assistants and providers in minutes, and run this analysis against your real traffic. Free, 100K transactions/mo, no credit card.