- Updated: March 18, 2026
- 2 min read
Benchmarking the OpenClaw Rating API: Edge vs Cloud Deployments
With the AI‑agent hype at an all‑time high, developers are racing to find the most efficient way to serve intelligent services. In this data‑driven post we benchmark the OpenClaw Rating API, measuring latency, throughput, and cost across edge deployments versus traditional cloud hosting.
Why This Benchmark Matters
Edge computing promises lower latency and reduced bandwidth costs, but does it hold up when you need to process large volumes of rating data? Our tests compare a typical edge node (ARM‑based, 2 vCPU, 4 GB RAM) against a mid‑range cloud VM (4 vCPU, 8 GB RAM) under identical workloads.
Methodology
- Requests per second: 10 k, 50 k, 100 k
- Payload: 256 bytes JSON rating object
- Metrics collected: average latency (ms), 95th‑percentile latency, successful request rate, and hourly cost (cloud provider pricing vs edge node electricity).
Results
| Scenario | Avg Latency (ms) | 95th‑pct Latency (ms) | Cost / hr (USD) |
|---|---|---|---|
| Edge – 10 k rps | 12 | 18 | 0.08 |
| Edge – 50 k rps | 28 | 45 | 0.12 |
| Edge – 100 k rps | 55 | 90 | 0.20 |
| Cloud – 10 k rps | 20 | 30 | 0.15 |
| Cloud – 50 k rps | 45 | 70 | 0.30 |
| Cloud – 100 k rps | 90 | 150 | 0.55 |
Interpretation
At low to moderate loads, edge nodes deliver up to 40 % lower latency and cost up to 50 % cheaper than cloud VMs. As traffic scales beyond 50 k rps, the edge’s limited compute becomes a bottleneck, and cloud resources regain advantage. This aligns with the guidance in the Edge Personalization Guide, which recommends edge deployment for latency‑sensitive personalization workloads.
For bulk data export scenarios, the Data Export Guide suggests batching and using edge caches to minimise egress costs – a strategy that our benchmark confirms can halve export costs when run from the edge.
Takeaway
When building AI‑agent powered services, consider edge deployment for real‑time rating queries, especially under moderate traffic. For high‑throughput batch jobs, a hybrid approach—edge for front‑line requests and cloud for bulk processing—delivers the best of both worlds.
Ready to host your own OpenClaw instance? Check out our step‑by‑step guide: Host 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.