Agent & skill evaluation · private beta
Cut your LLM bill without
cutting quality — verified, not estimated.
For engineering teams of 5–50 who own the model bill. ReasonRank finds the cheapest model that holds quality on your own test cases, projects the savings against your real traffic, and verifies the dollars in production — with rollback if quality slips.
bring your own keys · no token markup · your data stays yours
The problem
Teams ship agents fast, then overpay to run them forever.
Model sprawl
Every agent and skill was pinned to whatever model felt right that week. Nobody re-checks whether a cheaper one now does the job just as well.
Silent overspend
Token bills climb as traffic grows, but you can't see which agent is expensive, or how much a switch would actually save at your real volume.
Quality you can’t vouch for
Swapping models is scary because you have no repeatable, scored proof that quality holds. So you overpay for headroom you may not need.
Candidates for this skill
quality vs. cost per call| Model | Quality | Cost / call | Value |
|---|---|---|---|
| GPT-4o minirecommended | 0.91 | $0.0006/call | |
| Gemini 2.0 Flash | 0.90 | $0.0005/call | |
| Claude 3.5 Haiku | 0.89 | $0.0021/call | |
| GPT-4o (in prod) | 0.94 | $0.0102/call |
Verified savings
$4,050/mo
support-triage-agent · GPT-4o → GPT-4o mini, live in production
95% CI on quality delta
−0.6% … +2.3% · clears −2% tolerance
paired over 24 test cases · production baseline: 1,204 pre-switch calls · switch detected in live traffic · prices verified Jul 2026
Illustrative figures. Your models, cases, and traffic produce your own numbers — that’s the point.
The closed loop: ingest →
recommend → apply → verify.
How it works
Connect your traffic
Point your LLM client at the ReasonRank gateway (zero app-code change) or stream traces to the ingest API — metadata-only by default — so every agent learns its real monthly volume and spend.
Evaluate candidates
Run your test cases across candidate models with deterministic + LLM-judge scoring. Every run shows a pre-flight cost estimate first.
Get a recommendation
ReasonRank finds the cheapest model that holds quality and projects the dollar savings against your actual traffic.
Apply, verify & govern
Switch the production model in one click, then verify quality on live traffic with automatic rollback if it regresses. Budget caps and alerts keep evaluation spend under control.
The metric
Other eval tools tell you which model is smartest. ReasonRank tells you which is smart enough for the job — at the lowest defensible cost.
Capabilities
Everything you need to right-size an agent.
Savings recommendations
01The lever, not just the chart: “move this skill to a cheaper model — quality holds, save ~$1,200/mo at your volume.” Apply or dismiss in one click.
Quality × cost benchmarking
02Score agents and skills on a single efficiency axis — quality per token, per dollar, per millisecond — across OpenAI, Anthropic, and Google.
Production trace ingestion
03Recommendations reflect your real monthly volume and spend, not a synthetic benchmark. Metadata-only by default; sampled payloads are opt-in.
Spend guardrails
04Pre-flight estimates, per-run and monthly budget caps, an output-token ceiling, live running cost, and a kill switch. Measuring waste never becomes it.
Repeatable, defensible scoring
05Deterministic scorers (exact match, regex, keywords, JSON validity) plus optional LLM-as-judge with strict, token-frugal rubrics — and stability sampling.
Skills roll up into workflows
06Group agents into an ordered workflow and see combined cost and quality, so you can optimize a multi-step flow, not just one call.
Verified savings loop
07Apply a recommendation, verify quality on post-switch traffic, and roll back automatically if it regresses — with linkable evidence cards your team can share.
Why our numbers hold up
Statistics a staff engineer can audit.
Every recommendation ships with its method, interval, and sample — and every claim below is visible on the evidence page of a real recommendation.
Paired cluster bootstrap
Two models scored on the same test case are paired observations, and repetitions within a case are correlated. We bootstrap over test cases — clusters — never over individual results, so correlated repeats can't launder themselves into fake sample size.
Non-inferiority, not vibes
A cheaper model is recommended only when the 95% confidence interval on the quality delta clears a −2% tolerance. Too few shared cases? The recommendation is flagged unproven and excluded from every headline dollar — we tell you exactly how many cases to add.
Production baselines
Realized savings compare production cost-per-call before the switch to production cost-per-call after it — never eval-suite numbers — and are withheld until the new model is actually observed in your traffic with enough calls to judge.
Built to be trusted
Ready for the way serious teams operate.
ReasonRank is built on a multi-tenant platform with encryption, isolation, and spend governance from day one — the foundations enterprises require before they trust a tool with production data.
Enterprise-ready today: SAML/OIDC SSO, AES-256-GCM with versioned ciphertexts, a self-hosted single-tenant gateway (we never proxy your provider traffic through shared infrastructure), downloadable security packet, DPA template, and exportable audit logs. Details on /trust.
Bring your own keys
Evaluations run against your own provider accounts. We never resell tokens or mark them up — your provider bill stays yours.
Encrypted & isolated
Provider keys are encrypted at rest with AES-256-GCM. Every record is scoped to your workspace with strict tenant isolation.
Spend governance
Org-level budgets, per-run caps, and token ceilings turn “hope it’s fine” into enforced limits — with alerts at 50/80/100%.
Data control
Trace payloads are redacted on a short window and records age out automatically. Owners can delete a workspace and all its data, self-serve.
Pay in proportion to the spend we manage.
Full pricing →Stop guessing what your agents
should cost.
We’re onboarding a small group of design partners in private beta. Bring your agents, your models, and your rubrics — we’ll help you find where the money is going.