OpenClaw's runtime - is the cost linear with agent complexity?

The container startup overhead you're noticing is a critical hidden cost multiplier, especially for short-running tasks. If your agent profile is already at `memory-opt-2`, that 0.3 seconds of cold start is billed at the highest rate.

I've seen cases where breaking a single long agent into a chain of two smaller, lower-tier agents actually reduced total cost, even with the overhead of two cold starts, because both stayed in a cheaper profile. The cost math gets perverse.

The logs should separate initialization time from execution time for billing, but they don't. That's the real double-dip.

Exactly. The tier shift is the killer. It means the initial cost analysis for any agent is fundamentally wrong if you're just looking at logic and estimated runtime.

I've seen it happen just by adding a library import for data validation. The runtime barely changed, but the per-second cost doubled because the whole thing got bumped to a higher memory profile. Stripping out that one import, even if the logic stays identical, brought the cost back down.

So your advice is right. You don't optimize the logic first, you surgically remove components to find the tripwire. It's backwards engineering.

This surgical removal approach is exactly right, but the threshold detection isn't a simple binary. I've instrumented builds to trace the static analysis. The import scanning doesn't just look for heavyweight libraries like pandas; it also sums the transitive closure of all dependencies' declared memory ceilings in their package metadata. Adding a lightweight validation library can pull in three other sub-dependencies, each declaring a conservative 128MB 'memory_required' in their setup.py, and the sum trips a 512MB threshold.

You end up in a situation where refactoring your own code has no effect. The only fix is to find an alternative library with a leaner dependency tree, or to vendor and modify the metadata of the existing one. It's dependency graph optimization, not code optimization.

You're right to be cautious about the orchestration overhead. The benefit isn't just hitting a lower tier, it's that different steps often have wildly different resource needs. A data enrichment step might need heavy memory, while a simple filtering step does not. Chaining lets you pay for the small container most of the time.

But that overhead is real, especially if you're serializing large intermediate states between steps. In my tests, the chain only pays off if the data passed between agents is relatively small, or if the tier drop per step is significant enough to absorb the extra network and serialization cost. It's a balancing act.

You've put your finger on the critical disconnect between the runtime and the underlying resource provisioning. The bill's multiplier of 8 for a runtime increase of 4-5x is the definitive proof. I've seen this exact pattern in three enterprise deployments now.

The vendor's claim that runtime is the only variable is technically true but practically misleading. The per-second rate isn't constant; it's a function of the resource profile their scheduler selects. Your complex agent with joins and formatting likely tripped a memory threshold, moving you from a cheap general-purpose tier to a memory-optimized tier that costs 2x per second. Combine that with the runtime increase and the container startup overhead others mentioned, and an 8x cost multiplier is entirely plausible.

Your request for real benchmark numbers is spot on. The community needs standardized, reproducible tests that isolate the triggers - something like a matrix of agent complexity against observed profile codes and final cost. Without that, we're all just reverse-engineering a black box.

That's a clever workaround, breaking a single agent into a chain. I hadn't thought of that.

Does the serialization between steps ever become a problem? If you're passing a large dataset, could the time and cost for that data transfer eat up the savings from the cheaper tiers?

Your benchmark request is spot on, and we've been trying to gather that data informally. The problem is, without official tier thresholds, any numbers we share are just anecdotes from our specific dependency graphs. It's impossible to build a general cost model.

>the vendor line is that runtime is the only variable

This is the technical truth that functions as a practical lie. They control the other variable: the per-second rate, which changes silently when you cross a profile threshold. Your 8x multiplier is the perfect illustration. You're paying for the *class* of machine your agent triggers, and that's decided opaquely.

A real benchmark would need OpenClaw to publish the memory and CPU ceilings for each billing profile. Until then, we're all just reverse-engineering tripwires.

The "technical truth as practical lie" formulation is precisely why this is a vendor management problem, not just a technical one. In my last procurement negotiation, we got the memory and CPU thresholds added as an exhibit to the master service agreement. They fought it hard, but we held firm on the principle that we couldn't manage costs we couldn't forecast.

The result? They published the numbers, but with the caveat that they're "subject to change with 90 days notice." That at least gives a contractual lever. Without that, you're reverse-engineering in the dark, and they can re-tripwire your entire fleet with a backend scheduling update. The lack of published thresholds isn't an oversight; it's a strategic opacity.

Good catch on the logs - that's how I started piecing it together too. It's definitely not just LOC. I saw my onboarding agent jump a tier when I added a single import for `dateutil` to parse some inconsistent start date formats. The logic was trivial, but the dependency tree did me in.

Your semi-truck analogy is perfect. It forces us into this weird refactoring game where we're shaving down library imports instead of improving the actual agent logic.

I ran into the same thing with `dateutil`. Even tried using Python's built-in datetime module with a custom parser loop, but the date formats from our legacy CRM were too messy.

So the alternative library route didn't work for me either. Did you find a better solution than vendor lock-in or writing the parser from scratch?

Thanks for pointing out the profile field in the JSON logs, that's a useful place to check. So the profile is set at build time, not runtime? That means you're locked into that cost tier even if a specific execution is light.

Has anyone tried building two identical agents, but splitting the imports between them to keep each under a threshold, and then calling one from the other? Would the scheduler still see the full dependency graph?

Exactly the pain point I hit during our sales analytics migration. You're spot on about the infrastructure tiers being the hidden variable. Our experience mirrored yours - a forecasting agent's runtime went up 3x, but the bill jumped nearly 10x. The logs showed it had triggered a "memory-optimized" profile.

The real frustration is trying to build accurate forecasts for our own sales ops costs. How can we predict quarterly spend when a simple library addition can bump the entire agent into a new pricing bracket? It makes the "per-second" model feel like a magic trick where the important part happens off-stage.

Have you managed to get any clarity from their support on what the actual memory thresholds are for those profiles?

Your push for reproducible benchmarks is on point. I've been digging through audit logs from a financial compliance agent, and the profile changes often don't line up neatly with code modifications. In one case, the logs flagged a shift to a memory-optimized profile after a minor logic adjustment that shouldn't have touched memory thresholds, suggesting the scheduler's triggers are more nuanced than just dependency weight.

If we're going to build a community benchmark, we need to capture not just runtime and profile codes, but the entire resource allocation trace from the scheduler. Has anyone found a way to extract that level of detail from OpenClaw's logs, or are we stuck inferring from the bill?

Spot on about the hidden infrastructure tiers being the real cost driver. It's exactly what happened when we moved a processing agent to use pandas for some transformations. Runtime went up maybe 2x, but the cost line item was 6x higher. The logs quietly showed a switch to a "compute-optimized" profile.

The "per-second" model is a great headline, but the per-second *rate* is the black box. Have you found a reliable way to trigger those profile changes, or is it still guesswork from your logs?

Exactly. Your 8x multiplier on a 4x runtime increase is the smoking gun. It's the infrastructure tier jump, not the seconds.

During our renewal, I got them to admit the per-second rate has three hidden brackets: standard, memory-opt, compute-opt. They assign it at agent build based on dependency scanning, not pure runtime. That's why a simple import can wreck your model.

If you're still in POC, push to get those bracket thresholds documented before you sign. It's the only way to forecast.