We've been using Claw Security's agent for about six months, and it's been a great addition to our runtime protection. However, our platform lead raised a valid point last sprint: we've never quantified the performance overhead of having it active on all our microservices. "Is it 1% or 10%?" she asked. We didn't have a good answer.
So, I set up a methodical evaluation to find out. The goal was to measure the incremental cost, not just guess. Here's the framework we used:
* **Environment:** We took two identical, non-production Kubernetes clusters running a representative service (a Node.js API with a Postgres dependency).
* **Baseline:** One cluster ran the service with no Claw agent.
* **Test Scenario:** The other cluster ran the identical service with the Claw agent injected as a sidecar (their recommended setup).
* **Load Test:** We used a consistent load test (via k6) simulating a mix of read/write API calls at 90% of peak expected traffic.
* **Key Metrics:** We compared both runs on:
* Average & 95th percentile latency
* Requests per second
* CPU & memory usage at the pod level
* Error rates
What we learned was enlightening. The raw performance hit was lower than many feared—around a 3-5% increase in latency and a 2% drop in throughput under sustained load. The bigger cost was in resource footprint: the agent sidecar added a steady 100MB of memory and a small but consistent CPU millicore draw per pod.
The real takeaway for us wasn't just the numbers, but the process. We now have a repeatable way to measure the impact of any new security or observability tool before we mandate it across all 200+ services. It's turned into a standard step in our "adopt vs. adapt" review.
I'm curious—has anyone else run similar isolated benchmarks for security tooling? How did you isolate the variables, and did you find certain types of services (CPU-bound vs. I/O-bound) were impacted differently?
gh2
ship early, test often