OpenClaw Cost Optimization: Reduce Your Monthly API Bill

OpenClaw's API costs can creep up quickly — especially once you have multiple skills running, automation chains triggering frequently, and a growing agent memory that extends every context window. This guide covers practical, immediately applicable strategies to cut your monthly API spend without sacrificing agent quality.

Understanding Where Costs Come From

OpenClaw API costs break down into:

1. Context window: Every message includes your conversation history, agent memory, and active skill descriptions. Long histories mean expensive calls.
2. Model choice: Claude 3.5 Sonnet costs 10–20× more than DeepSeek V3 for equivalent tasks.
3. Automation frequency: Scheduled tasks running every hour cost 24× more than daily tasks.
4. Skill overhead: Skills that add extensive tool descriptions to every context window increase costs.

Strategy 1: Right-Size Your Model

Not every task needs Claude 3.5 Sonnet. Configure different models for different task types:

Task Type	Recommended Model	Cost Tier
Research and complex reasoning	Claude 3.5 Sonnet / GPT-4o	High
Email drafting and summarisation	Claude 3 Haiku / GPT-4o Mini	Low
Simple lookups, reminders	DeepSeek V3	Very Low
Code generation	Claude 3.5 Sonnet	High
Data processing	DeepSeek V3 or Haiku	Low

Configure model routing in openclaw.json:

{
  "model_routing": {
    "default": "claude-haiku",
    "complex_reasoning": "claude-sonnet",
    "code": "claude-sonnet"
  }
}

Strategy 2: Manage Context Window Size

Each OpenClaw conversation sends the entire history to the LLM. As conversations grow, costs grow proportionally.

{
  "memory": {
    "context_window_messages": 20,
    "auto_summarise_threshold": 50
  }
}

With auto_summarise_threshold: 50, OpenClaw automatically summarises older conversations into compact memory entries after 50 exchanges, replacing verbose history with concise summaries.

Strategy 3: Optimise Skill Loading

Active skills add their tool descriptions to every context. Only keep skills enabled that you use regularly.

# Disable rarely-used skills
/skills disable mixpost-connector
/skills disable image-generation

# Re-enable when needed
/skills enable image-generation

Disabling 5 verbose skills can reduce context size by 2,000–5,000 tokens per message — saving $0.01–$0.02 per conversation, which adds up fast.

Strategy 4: Batch Your Automations

Instead of running 10 separate hourly checks, combine them into a single daily briefing. One complex daily call costs far less than ten simple hourly calls.

Before optimisation (10 hourly tasks): ~10 × 2,000 tokens × 24 hours = 480,000 tokens/day After optimisation (1 daily briefing): ~1 × 8,000 tokens = 8,000 tokens/day

At Claude pricing: $1.44/day → $0.02/day. 98% cost reduction.

Strategy 5: Use Caching for Repeated Queries

For automation tasks that query the same data repeatedly, use the Markdown Notes skill as a cache layer:

"Check if you've already fetched today's news summary from the notes. If yes, use the cached version. If no, fetch it now and save it to notes."

Strategy 6: Switch to Local LLMs for Some Tasks

For simple, repetitive tasks (formatting, classification, summarisation), local models via Ollama can replace expensive API calls entirely.

Run a small local model (Qwen 2.5 3B, Llama 3.2 3B) for routine tasks and reserve the API model for tasks that genuinely require high reasoning capability.

Monitoring Your Costs

Track your spending across models:

• Anthropic Console shows token usage by day and model
• OpenAI Dashboard provides cost breakdowns
• Set billing alerts in your LLM provider dashboard at $10, $25, and $50 thresholds

Frequently Asked Questions

What's the cheapest configuration that still works well?

DeepSeek V3 as the default model on a Hetzner CX22 ($4.51/month server) with minimal skills enabled. For moderate usage (20 conversations/day), total cost is approximately $6–$8/month.