Buyer guide · Decision framework
A scorecard for choosing AI tools without buying the hype.
Feature lists reward products for having more buttons. A buying scorecard should reward the tool that performs your actual job with acceptable quality, low correction cost and manageable risk.
The 100-point scorecard
| Dimension | Weight | Question |
|---|---|---|
| Task success / output quality | 25 | Does it produce an accepted result on representative work? |
| Human correction time | 15 | How much repair, prompting and babysitting remains? |
| Total cost | 15 | Subscription/API/compute plus the value of operator time |
| Reliability | 10 | Does it keep working across repeated tasks and realistic variation? |
| Integration / automation fit | 10 | API, export, webhooks, CLI, connectors and workflow compatibility |
| Privacy / data control | 10 | Is the data path acceptable for the workload? |
| Commercial/licensing fit | 5 | Does the intended use fit current terms and plan? |
| Portability / switching risk | 5 | Can you leave without rebuilding the workflow? |
| Support / ecosystem | 5 | Can problems be solved quickly? |
Weights should change with the job. A hobby image generator may give privacy 5 points; an internal document-processing workflow may give it 25.
How to score each dimension
Use a 0–5 evidence score, then multiply by the weight:
- 0: unusable / requirement not met.
- 1: major gaps; only works with heavy compromise.
- 2: below acceptable; substantial manual workaround.
- 3: acceptable for the target job.
- 4: strong; clearly above minimum requirements.
- 5: excellent; repeated evidence of strong fit.
weighted points = (score ÷ 5) × dimension weight
Evidence grades
Add a confidence grade beside the numeric score:
| Grade | Evidence |
|---|---|
| A | Repeated direct test on representative tasks |
| B | Direct test, but small sample or limited edge cases |
| C | Official documentation plus indirect evidence |
| D | Marketing claim / anecdote not independently verified |
A 90/100 score made mostly of D-grade evidence should not beat an 82/100 score grounded in repeated tests.
Example: managed TTS vs local TTS
A creator might weight output quality and correction time heavily because every pronunciation failure requires manual intervention. A privacy-sensitive internal system might shift points toward data control and portability. The scorecard forces those priorities into the open instead of arguing that one tool is universally “better.”
Add hard gates before scoring
Some requirements should be pass/fail rather than weighted:
- available in your country;
- commercial use allowed for intended workflow;
- required API/export exists;
- budget ceiling not exceeded;
- required data policy or deployment location is supported;
- minimum output quality threshold is met.
A product that fails a hard gate should be rejected even if its weighted score is high.
Include switching cost
AI products change quickly. Prefer architectures that keep your data and process portable. Before committing, ask: Can prompts/templates be exported? Is there an API? Is the data in a standard format? Can another provider be placed behind the same interface? How painful would cancellation be?
Re-score after 30 days
The buying score is a hypothesis. After real use, replace assumptions with measured data: acceptance rate, median correction time, monthly usage, support incidents, actual cost and number of tasks completed. Many subscriptions look valuable in the first week and unused by week four.
A strong recommendation needs both fit and evidence
AI Tools Lab uses decision criteria like these to separate “interesting” from “worth paying for.” When a page has only documentation-level evidence, it should say so. When repeated direct testing exists, the test design and limitations should be visible.