OpenClaw's bill is dominated by background traffic: heartbeats, scheduled skills, and monitoring run around the clock even when you're not talking to it. Routing that background chatter to a cheap model (or a flat-rate endpoint) typically cuts the bill more than anything you do to the interactive sessions.
The background loop doesn't need frontier quality — it needs to notice when something requires attention. Configure the routine path to a budget model and let escalation call the big model only when a skill actually engages.
Every scheduled skill is a recurring API cost. List them, kill the ones you stopped caring about, and stretch intervals (a 5-minute check that could be hourly is a 12x cost difference on that skill).
System prompt, skill definitions, and memory preamble are nearly identical on every call — exactly what prompt caching discounts. On providers with cache-aware limits this also multiplies your effective throughput.
A 24/7 agent is the textbook case where per-token billing loses: the usage never stops. A flat monthly price converts OpenClaw's whole cost profile into a constant.
The provider-agnostic tactics (prompt caching, retry budgets, batch APIs) are in the general playbook.
Because OpenClaw is never idle: heartbeats, scheduled skills, and monitoring run continuously in the background. Checking your provider's usage log usually shows a steady baseline of small calls all night — that baseline, not your conversations, is most of the bill.
Only momentarily — the background schedule resumes. The durable fixes are pruning scheduled skills, routing background calls to a cheap model, and capping the whole thing with flat-rate pricing.
The structural version of all of this: run OpenClaw on a flat monthly price with unlimited tokens, and the bill stops being a variable to manage. 2-minute OpenClaw setup → · Best models for OpenClaw →