Real production lessons from running OpenClaw for 30 days. Cost breakdowns, architecture decisions, and hard truths about self-hosted AI agents.

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A 30-day production deployment of OpenClaw, a self-hosted AI agent framework, reveals the operational reality behind running AI agents on bare-metal infrastructure. The team processed 47,200 tasks at $104 per 1,000 tasks, discovering that self-hosting only becomes cost-effective above 80,000 monthly tasks due to 8 hours weekly maintenance overhead. Key lessons include the necessity of task queues from day one, GPU memory over-provisioning by 30%, and quantization optimizations that improved throughput by 38%. The break-even analysis shows cloud APIs win below 30,000 tasks monthly, while self-hosting provides clear advantages for data sovereignty and high-volume workloads despite requiring rare ML ops expertise.

OpenClaw Production Guide: 4 Weeks of Lessons

The Starting Architecture: Day 0 Hardware and Software Decisions

Week 1: Baseline Performance and Early Surprises

Week 2: Scaling Challenges and the First Architecture Pivot

Week 4: Stabilization, Optimization, and What We'd Do Differently

30-Day Scorecard: Final Metrics and Recommendations

What's Next: OpenClaw Roadmap and Our Plans