import RealityCheck from "@/components/RealityCheck"

OpenClaw feels "free" until you run agents in a loop—then the real bill is compute, not software.

Why OpenClaw Agents Blow Up API Bills: The Loop Cost Model

TL;DR

• If you're debugging an agent, your cost scales with loop count × context tokens × run count.
• Use the snippet below to estimate cost quickly, then compare against fixed-cost compute.

# Example cost estimate (edit numbers to match your trace + provider pricing)
node -e "const loops=5,ctx=10000,out=2000,inP=0.14,outP=0.28,runs=50,days=30;
const inT=loops*ctx, outT=loops*out;
const perTask=(inT/1e6)*inP+(outT/1e6)*outP;
console.log('per task $'+perTask.toFixed(4));
console.log('per month $'+(perTask*runs*days).toFixed(2));"

The Log: what you're seeing

A common pattern: a simple prompt causes repeated "think → tool → think" loops, and the agent keeps re-reading growing context.

Why this happens (Resource Mismatch)

Agents are loop-based. Each loop reads context (input tokens) and emits output tokens. Even if each loop is "small," total usage accumulates quickly.

The physics (the math)

Assumptions (examples)

These are illustrative numbers to demonstrate the model. Replace them with your real trace and your provider's published prices.

• Loops per task: 5
• Context tokens read per loop: 10,000
• Output tokens per loop: 2,000
• API pricing (example): input $0.14/1M, output $0.28/1M
• Debug runs per day: 50
• Days per month: 30
• Cloud GPU hourly (example): $2.5/hour

Tokens per task

• Input tokens per task: 50,000
• Output tokens per task: 10,000

Cost per task (example)

Using the example assumptions above:

• Cost per task (example): $0.01
• Cost per month (example): $14.70

The Fix: measure your trace, then decide

1. Capture one real agent trace:

• average context tokens read per loop
• average output tokens per loop
• number of loops per task
• tasks per day during debugging

2. Plug those numbers into the model and compute monthly spend.

3. Compare to fixed-cost compute.

Breakpoint table (API vs Cloud GPU)

GPU hours/day	GPU monthly cost (assumption)	API monthly cost (assumption)	Cheaper
0.5	37.50	14.70	API
1	75.00	14.70	API
2	150.00	14.70	API
3	225.00	14.70	API
4	300.00	14.70	API
6	450.00	14.70	API
8	600.00	14.70	API

The "Survival" recommendation (correct architecture)

Local debugging is bounded by your time and your machine. Fixed-cost compute is bounded by runtime, not tokens.

If your measured monthly API spend exceeds your expected GPU monthly runtime spend, move agent debugging to a clean, isolated Linux environment.

Deploy on Vultr (Limited Time Offer)

---

Related Fixes

• Fix OpenClaw Slow Inference? Why 3.5s/token Is Normal (And How to Fix It) - Performance bottlenecks and realistic expectations
• Fix OpenClaw CUDA OOM: The Cloud Solution vs. The 4-Hour Debug - VRAM limits and practical mitigation
• How to fix OpenClaw JSON Mode parsing errors with DeepSeek R1 - Structured output failures and model formatting

---

Still Stuck? Check Your Hardware

Sometimes the code is fine, but the GPU is simply refusing to cooperate. Before you waste another hour debugging, compare your specs against the Hardware Reality Table to see if you are fighting impossible physics.