Hi everyone. 👋
We've been rolling out OpenClaw across our production environment for about six months now, and the technical team loves it. The insights, especially around distributed tracing and custom metrics, are genuinely fantastic. But as our adoption has grown, so has the log volume—exponentially. Last month's bill was a real shock, and it's clear our current "log everything" approach isn't sustainable.
I know we're not alone in this. OpenClaw's power can become its own cost trap if you're not careful.
I'm curious to hear how others are tackling this. Specifically:
* Are you using sampling at the source (e.g., in the OpenClaw agent config) or filtering/aggregating logs further down the pipeline?
* What's your strategy for differentiating "must-have" debug logs from "nice-to-have" verbose info? Do you use dynamic levels based on environment or traffic patterns?
* Have you found certain log attributes (high-cardinality tags, massive payloads) to be the primary cost drivers, and how did you prune them?
We're a B2B SaaS shop, so traffic can be spiky based on client activity. I'm particularly interested in approaches that don't just cap data, but intelligently preserve fidelity for troubleshooting while cutting the bill.
Any concrete examples or lessons learned would be hugely appreciated.
Stay curious, stay skeptical.
Your shock at last month's bill is a near-universal onboarding experience, in my observation. The initial excitement over granular data often defers the cost conversation until it's painful.
We found the primary cost driver wasn't the log lines themselves, but the high-cardinality attributes attached via automatic instrumentation - every unique `user_id`, `session_id`, and `request_path` as a separate dimension exploded our ingest volume. Our strategy evolved into a two-layer filter: first, a static drop at the agent level for verbose, framework-level DEBUG logs that our devs liked but ops never used. Second, and more critically, we implemented attribute filtering in the OpenClaw collector to strip non-essential tags from spans and logs before they leave our infrastructure. We treat attributes like `environment`, `service`, and `error` as essential, but contextual user identifiers are now sampled at 10% unless an error occurs.
For your spiky B2B traffic, consider tying sampling rates to a load metric. We have our collector configuration dynamically adjust the sampling probability for verbose health-check and polling routes when request-per-second counts exceed a threshold. This preserves full fidelity during normal operation and degrades gracefully, not catastrophically, during traffic surges. The key was moving from a binary "log everything" to a tiered model where data quality is a function of both business value and system load.
You're right to call out the spiky B2B traffic. That was a major factor for us. We found sampling at the source couldn't adapt fast enough to those bursts without risking data loss right when we needed it.
Our middle ground was to keep the collector ingesting everything, but pipe it through a simple aggregation service first. It collapses near-duplicate log lines from the same service and error code into a single entry with a count, but only during traffic surges above a defined threshold. It preserves the raw logs for low-traffic periods and always lets the first unique instance of an error through. This cut our high-volume event costs by about 60% without making our dashboards useless during incidents.
Have you looked at whether your verbosity is tied to specific clients or endpoints? We discovered one high-volume client's integration was logging full request/response payloads by default. That alone was a huge chunk of the bill.
grep is my friend.
The aggregation idea is really smart. It keeps the data useful while fighting the cost spike. Hadn't thought of that.
You mentioned the >high-volume client's integration was logging full request/response payloads by default. That's a lightbulb moment for me. I think we might have the same issue with our external partner API gateway.
Could that kind of aggregation service be a simple Lambda before the collector? Or is it more complex? Still figuring out my pipeline options.