Cost Optimization Guides
Learn to reduce your OpenClaw API costs with token budgets, model routing, and compaction tuning.
How to Reduce Your OpenClaw API Costs
OpenClaw's default configuration sends every request to Claude Opus, the most expensive model, resulting in unnecessarily high API costs. This guide shows you how to implement model routing, configure token budgets, tune compaction settings, and add automation guardrails to significantly reduce your API costs without sacrificing quality.
How to Set Up Token Budgets in OpenClaw
OpenClaw ships with no default token limits, which means every conversation can use unlimited tokens and rack up unexpected API costs. This guide walks you through setting up global, per-conversation, and per-skill token budgets to control spending while maintaining a smooth user experience.
How to Configure OpenRouter Model Routing
OpenRouter acts as a unified gateway to multiple LLM providers and models, enabling intelligent routing based on task complexity. By configuring OpenRouter with OpenClaw, you can automatically send simple tasks to Claude Haiku, complex reasoning to Claude Opus, and everything else to Sonnet — cutting costs by 50-70% while maintaining quality.
How to Fix OpenClaw Compaction Cost Issues
Long conversations in OpenClaw trigger automatic compaction — a process that summarizes older messages to free up context window space. By default, OpenClaw uses your primary model (often Opus) for compaction, resulting in expensive summarization calls that can cost more than the actual conversation. This guide shows you how to switch compaction to Claude Haiku and tune settings to reduce compaction costs by 85%.
Claude Opus vs Sonnet vs Haiku: Cost Comparison for OpenClaw
Anthropic offers three Claude 3 models at vastly different price points: Opus (most powerful, most expensive), Sonnet (balanced), and Haiku (fastest, cheapest). Choosing the right model for each task can significantly reduce your OpenClaw API costs. This guide breaks down the pricing, performance, and ideal use cases for each model.
How to Prevent Runaway Automations in OpenClaw
OpenClaw automations are powerful but dangerous. A single misconfigured loop — like an automation that checks a condition that never becomes true — can generate thousands of API calls overnight, burning $500-2000 in tokens before you notice. This guide shows you how to set up guardrails that prevent runaway automations while keeping legitimate automation functional.
How to Monitor OpenClaw Token Usage
OpenClaw has no built-in token usage dashboard, making it difficult to understand where your API costs are coming from. Are certain skills using too many tokens? Which conversations are the most expensive? This guide shows you how to enable token logging, parse logs into actionable metrics, set up a simple dashboard, and integrate with your API provider's analytics.
How to Set Up Per-User Token Budgets
When multiple team members share an OpenClaw instance, one power user can burn through the entire API budget, leaving nothing for others. Per-user budgets let you set daily and monthly token limits for each user, track usage individually, and gracefully handle over-limit scenarios without disrupting the team.
How Much Does OpenClaw Cost to Run?
OpenClaw itself is free and open source, but running it has real costs: infrastructure to host it, and API fees every time it calls an LLM provider like Anthropic or OpenAI. Many teams are surprised when their first month's bill arrives — especially if they haven't tracked variable usage patterns or hidden API costs. This guide breaks down every component so you can budget accurately.
How to Choose the Right LLM for OpenClaw
OpenClaw works with dozens of LLM providers — Claude, GPT-4, Gemini, Llama, Mistral, and more. Each model has different strengths: some excel at coding, others at analysis, some are blazing fast, others are dirt cheap. Choosing the wrong model means you either overpay for capabilities you don't need, or under-deliver on quality for tasks that demand precision. This guide helps you match models to your actual needs.
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