---
title: LLM Cost Efficiency — AI Workflow Cost Optimization with Cromus
description: LLM cost efficiency starts before execution. Cromus simulates cost across 62+ verified models and 5 cost-quality modes, identifies tier mismatches, and quantifies preventable LLM spend with the Croms™ metric.
canonical: https://cromus.ai/llm-cost-efficiency
source_html: https://cromus.ai/llm-cost-efficiency
---

# LLM Cost Efficiency

> The highest-leverage point for LLM cost reduction is **before execution** — not after.

Most teams optimize LLM costs reactively: they observe a cost spike in their observability tool, find the expensive call, and try to reduce it. Cromus takes the opposite approach — model selection, tier assignment, and structural optimization happen before a single token is spent.

---

## Why pre-execution cost optimization is different

Observability tools show you what you *spent*. Cromus shows you what you *should* spend — and the gap between the two is your Croms™ score.

| Approach | When it works | What you learn |
|----------|--------------|---------------|
| Post-execution monitoring | After tokens are spent | What you spent |
| Pre-execution intelligence (Cromus) | Before any token is spent | What you should spend |

---

## The five cost-quality modes

Cromus simulates every workflow across five cost-quality modes, each targeting a different point on the price/performance curve:

| Mode | Description | Typical savings vs. Quality |
|------|-------------|----------------------------|
| **Eco** | Smallest capable models, maximizing cost reduction | 80–95% |
| **Cost** | Cost-optimized with a quality floor | 60–80% |
| **Balanced** | Balanced performance and price | 30–50% |
| **Quality** | Highest-accuracy commercial models | Baseline |
| **Open Source** | Self-hosted stack, zero per-token API cost | 90–100% API cost |

Every simulation uses the **verified model registry** (62+ models, 11 providers, weekly price checks) — not published list prices, which are frequently outdated.

---

## The four sources of LLM cost inefficiency

Cromus quantifies waste across four dimensions (the Croms™ framework):

### 1. Model tier mismatch (cost waste)
Using a Frontier or Quality model for a task a Lightweight model handles equally well. A GPT-4o call where GPT-4o Mini suffices costs 10–30x more per token.

### 2. Serialized steps (latency waste)
Steps that could run in parallel are queued serially, increasing both latency and total compute time. In per-second billing contexts, this directly inflates cost.

### 3. Retry and failure cost (failure risk)
Flaky prompts without schema validation or retry policies fail at higher rates. Expected failure cost = failure rate × correction cost × volume.

### 4. Context bloat (structural inefficiency)
Re-pasting system prompts, passing full conversation history to every sub-agent, and using dead branches all inflate token counts without improving output quality.

---

## Example savings

A 10-step customer support workflow analyzed at Balanced mode:
- **Before optimization:** $0.031/run, $93/month at 100 runs/day
- **After optimization (tier downgrades + parallelization):** $0.011/run, $33/month
- **Savings:** 65% reduction in LLM cost, identified before any execution

---

## Related pages

- [Croms™ — preventable AI workflow waste →](/croms)
- [Workflow Classification →](/workflow-classification)
- [Baseline Cost per Workflow →](/baseline-cost-per-workflow)
- [Interactive Demo →](/demo)