Real numbers on running OpenClaw agents: Claude API pricing, OpenAI costs, Twilio voice, typical monthly bills, and actionable tips to cut costs by 40-60%.
Most guides gloss over costs with vague disclaimers. This isn't that guide.
I've been running OpenClaw agents for the past year and I track every dollar. Here's exactly what I spend, what drives those costs, and how I cut my bill by 50% without sacrificing quality.
Running an OpenClaw agent has three main cost categories:
Let's break each down.
Claude is OpenClaw's default model and the one most users stick with. Current pricing:
| Model | Input (per 1M tokens) | Output (per 1M tokens) | |-------|----------------------|------------------------| | Claude Opus 4.5 | $15.00 | $75.00 | | Claude Sonnet 4-5 | $3.00 | $15.00 | | Claude Haiku 4.5 | $0.80 | $4.00 |
What this means in practice: A typical back-and-forth conversation message uses about 1,500-3,000 tokens total (including system prompt, context, and response). At Sonnet pricing, that's roughly $0.004-$0.008 per exchange. Sounds cheap until you're sending 500 messages a day.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | |-------|----------------------|------------------------| | GPT-4o | $2.50 | $10.00 | | GPT-4o mini | $0.15 | $0.60 | | o3-mini | $1.10 | $4.40 |
GPT-4o mini is remarkably capable for simple routing tasks and costs almost nothing.
Running models locally via Ollama has zero API cost. Relevant models:
If you have an M2/M3 Mac with 32GB+ unified memory, running a 30B model locally is genuinely viable for many tasks.
Setup: One Claude Sonnet agent, Discord + iMessage, ~20 messages/day
Messages per month: 600
Avg tokens per msg: 2,000
Total tokens: 1,200,000
Cost breakdown:
- Input tokens (70%): 840,000 × $3.00/1M = $2.52
- Output tokens (30%): 360,000 × $15.00/1M = $5.40
Monthly API cost: $7.92
Twilio: $0 (iMessage is free, Discord is free)
Compute: $0 (running on existing Mac)
Total monthly: ~$8
This is the "entry level" — a useful AI assistant for under $10/month.
Setup: Claude Sonnet as main, one coder sub-agent on Sonnet, Telegram + WhatsApp, ~80 messages/day
Messages per month: 2,400
Avg tokens per msg: 3,500 (larger context with code)
Total tokens: 8,400,000
Cost breakdown:
- Input (70%): 5,880,000 × $3.00/1M = $17.64
- Output (30%): 2,520,000 × $15.00/1M = $37.80
Monthly API cost: $55.44
Twilio WhatsApp: $0.005/message × 700 msgs = $3.50
WhatsApp number rental: $1.00
Total monthly: ~$60
This is where most serious OpenClaw users land. $60/month for an always-on AI team.
Setup: 4 agents (main, coder, researcher, writer), voice calls via Twilio, 200+ messages/day
Messages per month: 6,000
Avg tokens per msg: 4,500 (large context, documents)
Total tokens: 27,000,000
Cost breakdown:
- Input (65%): 17,550,000 × $3.00/1M = $52.65
- Output (35%): 9,450,000 × $15.00/1M = $141.75
Monthly API cost: $194.40
Voice (Twilio):
- 10 calls/day × 30 days × 2 min avg × $0.013/min = $7.80
- Twilio Voice number: $1.00
Total monthly: ~$203
At $200/month, you're essentially employing a 24/7 AI team for less than a part-time human assistant charges for a single day.
Here's what trips up new users: your system prompt + memory file gets sent with every single message. If your memory.md grows to 5,000 tokens and your system prompt is 1,000 tokens, that's 6,000 tokens of overhead per request — before the user even says anything.
Over 3,000 messages a month, that's:
6,000 tokens × 3,000 messages = 18,000,000 tokens
18M input tokens × $3.00/1M = $54/month
Just from context overhead!
Fix: Keep your system prompt tight (under 500 tokens) and prune memory.md regularly. Every 500 tokens you remove from your system prompt saves ~$4.50/month at moderate usage.
Twilio has confusing pricing. Here's the simple version:
If cost is a concern, avoid Twilio channels. An OpenClaw agent on Discord + Telegram + iMessage has zero messaging costs.
Not every message needs Claude Sonnet. A quick lookup or yes/no question works fine with Haiku or GPT-4o mini.
Set up a routing rule in your agent config:
{
"routing": {
"default": "claude-sonnet-4-5",
"quick": {
"model": "claude-haiku-4-5",
"maxTokens": 200,
"triggers": ["what time", "remind me", "set a timer", "quick question"]
}
}
}
Routing 30% of messages to Haiku can cut that 30% of costs by ~75%.
Savings potential: 15-25% reduction in total bill.
Use a weekly "memory compression" step where you ask your agent to summarize and compress memory.md:
You: Compress memory.md — summarize all facts into the most token-efficient
format possible, removing any outdated information. Keep all current context
but use bullet points and abbreviations where meaning is preserved.
This typically reduces a 3,000-token memory file to 1,200 tokens while preserving all relevant context.
Savings potential: 10-20% reduction (more with large memory files).
Most system prompts are verbose because we write them like essays. Rewrite yours in compressed instruction format:
Instead of:
You are a helpful AI assistant. Your goal is to help the user
accomplish their tasks efficiently. Always be polite and professional...
Use:
Role: Personal AI assistant for Alex
Tone: Direct, no fluff, markdown when helpful
Memory: Read memory.md at start, update when asked
Format: Under 150 words unless detail requested
The second version is ~40 tokens vs ~120 tokens. At scale, that matters.
Savings potential: 5-15% depending on current prompt length.
Add output token limits to prevent your agent from writing essays when you asked a simple question:
{
"defaultMaxTokens": 500,
"channelOverrides": {
"voice": { "maxTokens": 150 },
"discord": { "maxTokens": 800 }
}
}
Voice especially benefits — a conversational voice response should be under 3 sentences. Limiting to 150 output tokens saves money and makes voice responses actually listenable.
Savings potential: 10-30% on output costs.
If you send the same large document to your agent repeatedly (a product spec, a code file), use Anthropic's prompt caching:
{
"caching": {
"enabled": true,
"cacheSystemPrompt": true,
"cacheLongDocuments": true,
"minTokensToCache": 2000
}
}
Cached tokens cost 90% less on re-reads. If your system prompt is 1,500 tokens and you cache it, subsequent reads cost $0.30/1M instead of $3.00/1M.
Savings potential: 20-40% if you have static large context.
Let's put the numbers in perspective.
At Profile 2 usage ($60/month), you're getting:
Compare to alternatives:
OpenClaw's value isn't just the AI quality — it's the persistence, the channels, and the fact that you own the whole setup. When you want to add a new capability, you add it. You're not waiting for a feature roadmap.
For most users at $8-60/month, it's an easy yes.
At $200/month for a power user setup, you need to be clear that it's saving you more time than that — but if you're running a business and it's replacing even one hour of human work per week, it's a bargain.
| Use Case | Recommended Config | Expected Monthly Cost | |----------|-------------------|----------------------| | Personal assistant | Sonnet, Discord + iMessage | $8-15 | | Developer productivity | Sonnet + Haiku routing | $25-45 | | Small team assistant | Sonnet + coder sub-agent | $50-80 | | Business operations | 4+ agents, voice, WhatsApp | $150-250 |
Start at the bottom and scale up as you see value. The cost grows linearly with use, and so does the benefit.
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