> ## Documentation Index
> Fetch the complete documentation index at: https://docs.platform.nora.my/llms.txt
> Use this file to discover all available pages before exploring further.

# Cost vs. quality

> The tradeoff frontier — cheaper isn't always worse; more expensive isn't always better.

Every change moves your Agent somewhere on a two-axis plot: **quality** (pass rate) and **cost per run**. The Pareto view shows you exactly where.

## What the Pareto view shows

X-axis: cost per run. Y-axis: pass rate.

Every simulation you've run is a dot. Baselines, published versions, drafts, Improvement candidates — all plotted.

The **Pareto frontier** connects the dots that aren't dominated by any other — for their cost, they're the best quality, or for their quality, they're the cheapest. Everything below the frontier is strictly worse than something on it.

## Making a decision

* **Cheaper + higher quality** — obviously ship.
* **Cheaper + same quality** — usually ship. Watch for edge case regressions.
* **Same cost + higher quality** — usually ship.
* **More expensive + higher quality** — the interesting case. Is the quality gain worth the cost gain? Depends on domain.
* **More expensive + same or lower quality** — don't ship.

The Pareto view is decision aid, not decision maker.

## Cost vs. quality vs. latency

There's a third axis you can toggle: latency. Some workflows are latency-critical (chat) and cheaper isn't worth the extra 500ms. Others are batch (nightly) and latency doesn't matter.

Toggle: click **Add axis** on the chart. View as 3D scatter or as multiple 2D projections.

## Filtering the frontier

The full plot can be cluttered. Filter:

* **By target type** — show only Draft variants, or only Improvement candidates, or only published versions.
* **By dataset** — show results against a specific dataset.
* **By dimension** — show performance on a specific tag ("hard cases only").

Different subsets can have different Pareto shapes. A change might dominate on average but lose on hard cases.

## Pareto by dataset tag

Because "quality" depends on which examples you test, the frontier changes per tag:

* On `easy` examples, cheap models often match expensive ones — frontier is flat.
* On `hard` examples, expensive models dominate — frontier is steep.

Choose your battles: use the cheap model for easy intents (via a Router), the expensive one for hard intents.

## Historical Pareto

The historical view shows the frontier over time. Each week's best point plotted. Watch:

* **Frontier moving up-and-left** — you're improving quality *and* reducing cost. Ideal.
* **Frontier moving up-and-right** — quality gains cost more. Common, sometimes acceptable.
* **Frontier moving down-and-right** — quality regressed while cost went up. Bad. Investigate.

## Model selection

Simulations that vary the model (same Flow, different model choices) build a natural model comparison Pareto. Useful when picking a default model — you see the tradeoff explicitly.

## Auto-suggest improvements from Pareto

If a Draft sits below the frontier (dominated), Nora suggests specific tweaks that could move it up:

* "Reducing top-K from 12 to 6 saves \$0.02 per run with no quality change in this dataset."
* "Enabling reranker adds +5% pass rate for +\$0.01 per run."

Suggestions are grounded in the simulation history — Nora knows what changes have worked in the past.