OpenClaw Agent Cost Analysis: From $340 to $112 Monthly with Five Optimizations

Cost Breakdown and Optimization Results

A developer running a SaaS with ~2k users deployed four OpenClaw agents in production: customer support, code review on PRs, daily analytics summaries, and content generation for blog and social media. After receiving a $340 bill that seemed excessive, they logged every API call, model, and token for 30 days to identify optimization opportunities.

Initial Setup and Problem Analysis

All four agents were configured with GPT-4.1 at $2 per 1M input tokens and $8 per 1M output tokens. Over 30 days, there were approximately 18,000 total calls across all agents. When categorized by task complexity:

• 70% were dead simple tasks: FAQ answers, basic formatting, one-line summaries, summarizing minor PR changes
• 19% were standard tasks: longer email drafts, moderate code reviews, multi-paragraph summaries
• 8% were complex tasks: deep code analysis, long-form content, multi-file context
• 3% needed real reasoning: architecture decisions, complex debugging, multi-step logic

The analysis revealed premium pricing was being paid for 70% of tasks that cheaper models could handle without quality loss.

Five Optimization Strategies Implemented

• Prompt caching: Enabled prompt caching, cutting input token costs for support by around 40%
• Shorter system prompts: Rewrote system prompts from 800+ tokens to half the length
• Batching analytics: Changed analytics agent from real-time processing to batching events every 30 minutes, reducing calls from ~3,000/month to ~1,400
• Model selection: Stopped using GPT-4.1 for everything, testing and implementing cheaper models for simple and standard tasks
• Max token limits: Added output token limits (e.g., capping support agent at 300 output tokens per response)

Results and Agent-Specific Savings

Monthly costs dropped from $340 to $112. Agent-specific breakdown:

• Support: $38/month (was $145) - biggest win from prompt caching and not using GPT-4.1 for simple questions
• Code review: $31/month (was $89) - most PRs are small and don't need top-tier models
• Content: $28/month (was $72) - still uses GPT-4.1 for longer pieces but shorter prompts helped
• Analytics: $15/month (was $34) - batching made the difference

Key Insights

The developer noted that most savings came from basic optimizations: prompt caching and not using GPT-4.1 for simple queries accounted for about 80% of the reduction. The biggest surprise was discovering they had no visibility into cost distribution before tracking - they couldn't identify which agent was most expensive or what task types consumed the budget.