Cost per success
The only number that matters. We fold in retries and failures, so a prompt that fails 30% of the time stops looking cheap.
FinOps for AI — at the prompt layer
Meter your prompt spend.
Private, verifiable observability for prompt efficiency. See cost-per-success, attribute waste across teams, and get cited fixes — without a vendor ever reading your data.
Cost-per-success, not cost-per-call. No card required.
Cost / success-83%
$0.07
Verbosity-38%
412 tok
Cache miss-71%
6%
Retry rate-64%
1.2%
p95 latency-22%
840 ms
Reasoning burnoff
0 tok
$37Benterprise GenAI spend in 2025
98%of FinOps teams now manage AI spend
5–30×more tokens per agentic task
83%cost-per-answer cut with routing + caching
One engine
From diagnosis to prescription.
Most tools stop at visibility. We close the loop — capture, compute, attribute, then tell you exactly how to fix it.
- 01
Capture
A drop-in proxy or SDK records context, prompts, token counts, and timings from real production traffic.
- 02
Compute
Cost-per-success, verbosity, cache-miss, retries, and latency percentiles — pure arithmetic, no LLM judging.
- 03
Attribute
Roll waste up by prompt, engineer, team, app, and workflow — the breakdown the monthly bill can never give you.
- 04
Remediate
For each wasteful pattern, get a specific cited fix with a dollar impact. Diagnosis becomes prescription.
- 05
Govern
Enforce efficiency budgets in CI, gate cost before release, and rank improvement over time.
What you measure
The small things that compound into dollars.
Mechanical, exact, reproducible, ungameable — computed from captured data with no LLM judging.
Verbosity
Output tokens cost 3–5× input. We flag answers paying for text nobody reads.
Cache misses
Cached input is ~10% of the price. A miss on a reused prompt is a 10× overpay.
Reasoning burn
Hidden thinking tokens, billed on tasks that never needed them — surfaced and switched off.
Latency shape
TTFT plus p95 / p99. Tail latency is what users feel; the mean lies.
Prompt-vs-prompt deltas
Pin the model, vary only the prompt. “B does the same job for 40% fewer tokens,” with confidence intervals.
The differentiator
Don’t just spot waste. Prescribe the fix.
Each wasteful pattern is matched to a public, cited technique with a dollar impact — grounded in your own data, not a black box.
Pattern in your dataTechnique prescribedTypical effect
- Reused system prompt, no cachingCache-friendly ordering + prompt cachingCached input ≈ 10% of price
- 8–10 few-shot examplesTrim to the examples that still move accuracyFewer input tokens, no quality loss
- Reasoning model on a trivial taskRoute or disable reasoning for simple callsDrops hidden reasoning-token burn
- No output cap, verbose answersOutput schema + max-tokens + terse instructionOutput tokens cost 3–5× input
- High parse-failure / retry rateStructured-output, format-adherence promptingFewer 2–3× retry multipliers
- Frontier model on routine trafficRight-size to a cheaper tierUp to ~80% of routine traffic divertible
Two products, one engine
Bottom-up adoption. Top-down governance.
Engineers discover the Playground and bring it to work. The org buys the Enterprise audit. The same capture-and-scoring engine powers both.
Bottom-up
Playground
A free, competitive arena where engineers prove and sharpen prompt skills, ranked on cost, speed, and quality against shared challenges.
- Objective, ungameable efficiency scoring
- Hidden, rotating test variants
- Cost-golf, speed, and frontier modes
- Public skill signal for recruiting
Top-down
Enterprise
Per-prompt, per-team cost observability plus remediation. Surfaces waste, attributes it, and tells you how to fix it — sold on provable bill reduction.
- Cross-engineer & team attribution
- Cited, dollar-quantified fixes
- Efficiency budgets enforced in CI
- TEE: we can’t see your data
We can’t see your data.
Tokens, cost, latency, cache-hits, and retries are pure arithmetic — computed without anyone reading a byte of your content. Structural fixes live inside the privacy boundary; content-aware rewrites stay opt-in or run in-enclave.
- no black box
- no model benchmarking
- no gameable scores
- no vendor reading your prompts
- no cost-per-call vanity metrics
- no guesswork remediation
Pricing
Start free. Pay when it pays for itself.
The Playground is free forever. Land with a proof-of-value audit, then expand to seats and governance.
Playground
$0forever
Compete, learn, and rank your prompts on efficiency.
- Unlimited public challenges
- Cost & latency leaderboard
- Pinned-model fair scoring
- Shareable skill profile
Team
$49per seat / mo
Per-prompt observability and remediation for your team.
- Drop-in proxy or SDK capture
- Cost-per-success + waste attribution
- Cited fixes with $ impact
- CI efficiency budgets
Enterprise
Customtalk to us
Governance and privacy for org-wide AI spend.
- TEE — we can’t see your data
- SSO, roles, audit logs
- Org-wide attribution & governance
- Proof-of-value audit
Stop discovering cost on the bill.
Diagnose it per prompt, prescribe the fix, and prove the savings — privately.
Start free