That has not been my experience at all.
For the past four months, I have been running OpenClaw in production on a deliberately constrained stack: a low-cost VPS, 2GB RAM, no GPU, and Ollama on a budget subscription. No 企业 hardware. No managed AI cluster.
从 that stack, I shipped roughly 20 detached 系统, built a full e-commerce environment managing over 80,000 SKUs, generated animated landing pages, automated product article generation, and wired up payment processing through API-driven 工作流 comparable to what modern payment platforms expose.
The architecture that makes this work is 智能路由 plus 分离式系统.
智能路由 sends each task to the right model, tool, or workflow instead of defaulting everything to one oversized LLM. Lightweight tasks stay cheap and fast. 复杂 tasks only escalate when the router decides they need more capacity.
分离式系统 matter even more. Rather than keeping the agent online and running everything 实时, the agent builds independent components that execute on their own. That is where the architecture starts delivering real business value.
What I learned is that the real bottleneck is rarely the platform. The harder part is designing for agent behaviour, 系统 architecture, workflow orchestration, and expectation management.
OpenClaw is not perfect. No platform is.
But after four months of shipping production 系统 on minimal hardware, I can say the business value is real - if you treat the platform as a serious development tool instead of a demo toy.
The underlying technology is the same for everyone.
The outcomes are not.
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我喜欢文章对智能路由 sends保持务实的态度。
关于复杂 tasks only escalate的实际落地部分最吸引我。
关于lightweight tasks stay cheap的说明很完整，基础部分尤其清楚。
总结部分让built a full e-commerce environment的重点更加清楚。
文章对what I learned提供了很好的起点，值得继续跟进。
我会把roughly 20 detach 20这一段分享给需要了解技术的同事。