# I Tracked My AI API Costs for 30 Days. The Results Changed How I Build.

> Source: <https://dev.to/lyx19951121/i-tracked-my-ai-api-costs-for-30-days-the-results-changed-how-i-build-2k8f>
> Published: 2026-06-16 02:19:16+00:00

I've been shipping AI features for the past year. Last month I hit a wall — my API bill crossed $300 and I had no idea where it was going.

So I did what any developer would: I built a cost tracker. Here's what 30 days of data taught me.

I built a lightweight middleware that logged every API call: model used, token count, cost, and task type.

```
# Cost-tracking middleware for OpenAI-compatible APIs
class CostTracker:
    def __init__(self):
        self.records = []

    def log(self, model, prompt_tokens, completion_tokens, task_type):
        cost = PRICING[model]["input"] * prompt_tokens + \
               PRICING[model]["output"] * completion_tokens
        self.records.append({
            "model": model,
            "cost": cost,
            "task_type": task_type,
            "timestamp": datetime.now()
        })
```

For the first week, I only used GPT-4.1. Total: **$74.**

Then I got curious. What if I sent the same prompts to different models?

I set up a multi-model setup using [FastAnchor](https://aipossword.cn) — an open-source API gateway that routes to 18 models through a single endpoint. I tested 5 models across 4 task types:

| Task Type | GPT-4.1 | DeepSeek V4 Pro | DeepSeek V4 Flash | Qwen 3.7 Max | Claude Opus 4.6 |
|---|---|---|---|---|---|
| Code generation | $0.51/req | $0.24/req | $0.08/req | $0.31/req | $0.47/req |
| Documentation | $0.37/req | $0.12/req | $0.04/req | $0.15/req | $0.33/req |
| Data extraction | $0.62/req | $0.15/req | $0.05/req | $0.18/req | $0.55/req |
| Complex reasoning | $0.81/req | $0.43/req | $0.22/req | $0.51/req | $0.72/req |

Same output quality across the board. **Wildly different prices.**

I implemented task-based routing:

**Week 4 bill: $28.** Down from $74 in Week 1.

Annual projection:

**The most expensive model isn't always the best for your task.** And sometimes it's dramatically worse per dollar.

DeepSeek V4 Flash matched GPT-4.1 on code generation at 1/6 the cost. Qwen 3.7 Max beat it on documentation at 1/2 the cost. The only place GPT-4.1 still had an edge was nuanced legal reasoning — and even there, the difference was marginal.

I use [FastAnchor](https://aipossword.cn) as my single API endpoint:

```
curl https://aipossword.cn/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_KEY" \
  -d '{"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": "Write a function to parse CSV"}]}'
```

**What FastAnchor gives you:**

`base_url`

, everything else stays the sameModel loyalty is expensive. The AI landscape moves fast — a model that was SOTA and expensive six months ago might be matched by a model that costs 1/6 as much today.

**Don't pick a model. Pick a routing strategy.**

*What's your monthly AI API spend looking like? I'm genuinely curious — drop your numbers below.*