Free AI Evaluation Tools for Cost, ROI, and Reliability

Most AI evaluation fails in one of two ways: it accepts a launch claim at face value, or it tests a demo without pricing the retries, review, and failure modes that appear in production.

The ResearchAudio Evidence Lab is a free six-part decision library for engineers, product teams, and technical leaders. Each instrument runs in the browser, requires no login, and turns one vague AI claim into a concrete result you can inspect or share.

The AI Launch Evidence Scorecard checks seven things that should exist before a team treats an announcement as decision-ready: real access, a falsifiable claim, a named baseline, visible operating cost, documented failure cases, reproducible methods, and a dated evidence owner.

Use it before a proof of concept, procurement review, or launch write-up. A low score is not a verdict against the product. It is a precise list of evidence still missing.

Per-token pricing hides the number that matters in production. The AI Cost per Successful Task Calculator combines model cost, task success rate, retry limits, human review time, and labor cost.

The result separates cheap attempts from cheap outcomes. That distinction becomes important when a workflow succeeds only after multiple runs or requires a person to inspect every result.

An agent can look capable while its control loop remains unsafe. The AI Agent Loop Diagnostic checks ten production guardrails, including machine-checkable success, capped retries, validation, checkpoints, budgets, escalation, termination tests, and retained traces.

Run it against one real workflow. Planned controls do not count. The score shows whether the loop is blind, exposed, bounded, or controlled.

The AI Agent ROI Calculator estimates successful automated tasks, gross labor value, review cost, run cost, recurring platform cost, monthly net savings, and implementation payback.

It is designed to expose fragile automation plans. If the return disappears after realistic failure and review assumptions, the correct next step is a smaller pilot, not a larger forecast.

Published prices per million tokens do not include your traffic shape. The LLM API Cost Calculator combines monthly requests, input and output tokens, current provider rates, prompt-cache savings, retry overhead, and attached per-request charges.

Use it before choosing a model or approving a traffic forecast. The worked budgeting guide explains the formula and the production metrics to pull from logs.

The Prompt Caching Cost Calculator prices cache hits at the cache-read rate and misses at the cache-write rate, then compares the total with an uncached reusable-input baseline. It reports monthly savings and the cache hit rate required to break even.

Use current provider prices and an observed production hit rate where possible. The worked prompt caching guide explains the formula, an illustrative scenario, and the inputs to pull from traces and billing data.

Completing all six instruments takes about fifteen minutes. The guided starter kit covers the four core evaluation steps. Your inputs and progress stay in your browser.

AI evaluation is the process of testing a system against explicit evidence, cost, reliability, and outcome criteria. A useful evaluation connects model behavior to the real task, constraints, and failure conditions a team will face.

Yes. All six tools are free, require no account, and run in the browser.

No. The current Evidence Lab instruments keep calculator inputs and diagnostic progress in the browser.

Start with successful automated tasks, not total attempts. Subtract human review, run cost, recurring platform cost, and implementation cost before calculating payback.

At minimum, test success criteria, retry limits, output validation, failure classification, checkpointing, cost and latency budgets, human escalation, termination behavior, and trace retention.