Since using OpenClaw, our token usage dropped from 34 billion tokens per month to 1.5 billion tokens per month through the Sistem Berasingan approach.
Now, with Refactor and Resegment, we reduced it even further to around 750 million tokens per month.
The biggest lesson here is simple: optimization is not always about buying bigger GPUs or adding more compute power. Sometimes, the real breakthrough comes from redesigning how the system thinks, reads, and executes.
Before this, AI had to read too much context repeatedly just to make small changes. That created token waste, higher cost, slower execution, and unnecessary load on the system.
With Sistem Berasingan, the workload became more focused. With Refactor and Resegment, each process became even more structured. The AI no longer needs to scan the whole system every time. It only works on the exact part that matters.
That is how we moved from:
34B → 1.5B → 750M tokens/bulan
Less context.
Less repetition.
Less waste.
Lower cost.
Lebih Pantas execution.
For me, this proves one thing clearly: the future of AI efficiency is not only about stronger hardware. It is about smarter architecture.
Kecekapan starts with system design.
#OpenClaw #AI #LLM #AIAgents #SystemSeni Bina #TokenOptimization #SoftwareEngineering #AIEngineering #Kecekapan
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Lebih jelas daripada dek vendor yang saya terima pasal 34.
Angka tentang 34 lebih masuk akal daripada kebanyakan pos.
Worth reading for kecekapan starts with system design alone.
The direction on usage dropped from 34 billion is exactly right.
Good write-up. usage dropped from 34 billion alone was worth the read.
usage dropped from 34 billion is the part I would forward to my boss.