- Updated: March 18, 2026
- 1 min read
Benchmarking OpenClaw Edge Rating API: Latency, Throughput, and Resource Usage
Benchmarking OpenClaw Edge Rating API: Latency, Throughput, and Resource Usage
This article presents a data‑driven benchmark of the OpenClaw edge rating API CRDT‑based token‑bucket rate limiter. We cover the test methodology, real‑world results, and include visual charts for latency vs. request rate, throughput vs. concurrency, and CPU & memory usage.
Test Methodology
We exercised the API using a controlled load generator, varying request rates from 100 rps to 10 k rps and concurrency levels from 1 to 200 threads. Each test run lasted 5 minutes, measuring average latency, 95th‑percentile latency, successful request count, and system resource consumption (CPU, memory).
Results
The following charts illustrate the observed performance:
Overall, the OpenClaw rate limiter maintains sub‑100 ms latency up to ~5 k rps and scales linearly in throughput with concurrency until the underlying hardware limits are reached.
Conclusion
The benchmark confirms that the OpenClaw edge rating API provides robust performance for high‑traffic edge scenarios. For deployment details, see the related article Hosting OpenClaw on UBOS.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.