# How I Built a Credit Optimizer That Saves 30-75% on AI Agent Costs (Open Architecture)

> Source: <https://dev.to/rafsilva85/how-i-built-a-credit-optimizer-that-saves-30-75-on-ai-agent-costs-open-architecture-7>
> Published: 2026-05-26 04:09:26+00:00

If you're using AI agents like Manus AI, Claude, or ChatGPT with API access, you've probably noticed something frustrating: **every task gets the same expensive model**, regardless of complexity.

A simple "rename this variable" task burns the same credits as "analyze this 50-page legal document." That's like hiring a senior architect to hang a picture frame.

After burning through my monthly Manus credits in just 2 weeks, I decided to build a solution.

The core idea is simple: **analyze task complexity BEFORE execution, then route to the appropriate model tier.**

Here's the decision tree:

```
Task Input → Complexity Analyzer → Score (1-10)
                                      ↓
Score >= 8  → Opus/GPT-4 (expensive, high quality)
Score 4-7   → Sonnet/GPT-4o (balanced)
Score <= 3  → Flash/GPT-4o-mini (cheap, fast)
```

The scoring considers multiple factors:

| Factor | Weight | Examples |
|---|---|---|
| Token count | 20% | Long prompts = higher complexity |
| Domain keywords | 25% | "analyze", "research", "compare" = high |
| Output requirements | 25% | Code generation, multi-step = high |
| Context dependency | 15% | References previous work = higher |
| Creativity demand | 15% | "brainstorm", "innovate" = high |

``` php
def route_task(task_description: str) -> str:
    score = 0

    # Token analysis
    tokens = count_tokens(task_description)
    if tokens > 2000: score += 2
    elif tokens > 500: score += 1

    # Domain complexity
    high_complexity_keywords = [
        "analyze", "research", "compare", "synthesize",
        "architect", "design system", "debug complex"
    ]
    low_complexity_keywords = [
        "rename", "format", "list", "simple", "quick"
    ]

    for kw in high_complexity_keywords:
        if kw in task_description.lower():
            score += 2

    for kw in low_complexity_keywords:
        if kw in task_description.lower():
            score -= 1

    # Route based on score
    score = max(1, min(10, score))

    if score >= 8:
        return "opus"  # Most expensive, highest quality
    elif score >= 4:
        return "sonnet"  # Balanced
    else:
        return "flash"  # Cheapest, fastest
```

After implementing this system on my Manus AI workflow:

| Metric | Before | After | Improvement |
|---|---|---|---|
| Monthly credit usage | 100% in 14 days | 100% in 30+ days | 2x+ duration |
| Simple task cost | Same as complex | 70% cheaper | -70% |
| Complex task quality | Baseline | Same or better | No degradation |
| Average response time | 8-12s | 3-8s (simple tasks faster) | -40% |

The key insight: **~60% of daily tasks are simple enough for the cheapest model tier**, but without routing, they all consume premium credits.

I packaged this into a skill called **Credit Optimizer** that works as a pre-processing layer:

The architecture is model-agnostic — it works with any AI service that offers multiple model tiers:

Because quality matters. Complex tasks genuinely need powerful models. The optimizer ensures you get the RIGHT model for each task — not always the cheapest, not always the most expensive.

The Credit Optimizer is available at [creditopt.ai](https://creditopt.ai) — it includes:

I'm working on:

*Have you built something similar? I'd love to hear about different approaches to AI cost optimization. Drop a comment below or find me on creditopt.ai.*