Hey everyone! Been knee-deep in evaluating OpenClaw for our sales team's lead qualification bots, and I've hit a question I can't quite square from their pricing page.
They bill based on "runtime hours," which seems straightforward. But in my sandbox tests, a simple rule-based agent that just checks lead source and company size uses way fewer compute cycles than my more complex one that's hitting our internal APIs, doing some light data transformation, and then making a multi-factor scoring decision. Both ran for the same wall-clock time, but the complex one bumped up my estimated costs significantly.
So my question for the room: **Is OpenClaw's runtime cost actually linear with agent complexity, or are there hidden gradients?**
Specifically:
* Does embedding more logic (like conditional loops or data parsing steps) within a single agent increase runtime consumption *disproportionately*, or is it truly 1:1 with execution time?
* Are there "resource intensity" ceilings? For example, if an agent calls three external APIs instead of one, does that impact cost beyond just the added milliseconds of wait time?
* Has anyone done a direct comparison between a "dumb" routing bot and a "smart" enrichment agent? Were your cost increments predictable?
I'm trying to forecast if we should build a fleet of simpler, single-task agents or invest in fewer, more sophisticated ones. The per-seat license is fixed, but the runtime feels like the wild card. 😅
Any data points or experiences from your own builds would be super helpful!
~Jen
Always testing the next best thing.