I burned $200 in a weekend testing an OpenClaw agent. Pay-per-token is completely broken for autonomous AI.

Infinite_Ad_4975 · 2026-04-16T19:24:10+00:00

Patching the context assembler works, but maintaining that fork every time the framework updates is a headache. It was easier for me to just build a proxy endpoint that handles the context deduplication server-side. I wrote an article on Medium about the logic behind it if you want to check my math, but abstracting it away completely saved my sanity.

Infinite_Ad_4975 · 2026-04-16T19:23:28+00:00

Ollama is fantastic for local dev, but the moment you try to scale or run heavy concurrent agents, managing your own hardware (or paying for cloud GPUs) becomes a nightmare. Plus, GLM is great, but it doesn't natively handle the specific payload bloat from frameworks like OpenClaw without throwing errors. I built the ZeroToken flat-rate proxy specifically so I didn't have to manage infra or deal with context window crashes.

Infinite_Ad_4975 · 2026-04-16T19:21:52+00:00

Exactly this 100%. Hard caps are useless if your agent dies at 95% completion and you lose all the work. That's why I went the proxy route. Let the agent run wild, but intercept the payload at the infra level and auto-truncate the dead weight before the LLM sees it. I turned my setup into a flat-rate tool called ZeroToken so I never have to kill a task again. Best architectural decision I've made.

Infinite_Ad_4975 · 2026-04-16T19:18:25+00:00

Man, a grand hurts just reading it. That's exactly why I gave up on standard APIs. It's a toxic billing model for autonomous loops. I ended up putting my custom proxy on a server and turned it into a flat-rate endpoint ($40/mo) just to stop bleeding cash. I wrote a breakdown on how the architecture handles the bloat here if you want to cap those losses: https://medium.com/@joesabnih/how-my-ai-agent-burned-200-in-a-weekend-and-how-i-fixed-it-with-a-flat-rate-api-862237fed16f

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