"It works on my machine" isn't a performance guarantee. Before you push your OpenClaw (Clawdbot) deployment into production — especially if it's handling customer-facing conversations or time-sensitive alerts — you need hard numbers on how it behaves under load. This guide covers practical performance testing and benchmarking strategies for OpenClaw servers running on Tencent Cloud Lighthouse.
AI chatbots have a deceptive performance profile. They feel fast when you're the only user testing them. But in production, multiple factors compound:
Without benchmarking, you're guessing at capacity. With benchmarking, you know exactly when to scale.
Start with a standard Tencent Cloud Lighthouse instance from the Special Offer page. For benchmarking, provision the same spec you plan to use in production — testing on a beefier machine and then deploying on a smaller one defeats the purpose.
Recommended baseline specs for testing:
| Config | Spec |
|---|---|
| CPU | 2 cores |
| RAM | 4 GB |
| Storage | 60 GB SSD |
| Bandwidth | Bundled package |
| OS | Ubuntu 22.04 LTS |
Deploy OpenClaw using the one-click deployment guide, then install any skills you plan to use in production via the skills guide. Benchmark the actual configuration you'll run, not a stripped-down version.
You'll need tools that can simulate realistic chatbot interactions, not just raw HTTP throughput:
# Install common tools
sudo apt install -y apache2-utils wrk
# For more sophisticated bot simulation
pip install locust
First, establish the baseline — how many requests per second can your OpenClaw instance handle for simple health checks and echo responses?
# Health endpoint throughput
wrk -t4 -c100 -d30s http://localhost:3000/health
# Simple echo/ping endpoint
wrk -t4 -c50 -d30s -s post.lua http://localhost:3000/api/chat
post.lua for wrk:
wrk.method = "POST"
wrk.headers["Content-Type"] = "application/json"
wrk.body = '{"message": "ping", "session_id": "bench-001"}'
| Metric | Health Check | Echo Response |
|---|---|---|
| Requests/sec | 2,000-5,000 | 500-1,200 |
| Avg Latency | <5ms | 10-30ms |
| P99 Latency | <20ms | 50-100ms |
These numbers represent the server's own processing capacity, excluding LLM API calls. They tell you how much overhead OpenClaw adds before the actual AI inference happens.
This is the metric users actually feel. End-to-end latency from message sent to response received, including the LLM API round-trip:
# locustfile.py — realistic chat simulation
from locust import HttpUser, task, between
import json
import uuid
class ChatUser(HttpUser):
wait_time = between(2, 5) # Simulate human typing speed
def on_start(self):
self.session_id = str(uuid.uuid4())
@task(3)
def simple_question(self):
self.client.post("/api/chat", json={
"message": "What time is it?",
"session_id": self.session_id
})
@task(2)
def medium_question(self):
self.client.post("/api/chat", json={
"message": "Explain the difference between TCP and UDP in 3 sentences",
"session_id": self.session_id
})
@task(1)
def complex_question(self):
self.client.post("/api/chat", json={
"message": "Analyze the pros and cons of microservices vs monolithic architecture for a startup with 5 developers",
"session_id": self.session_id
})
Run with:
locust -f locustfile.py --host=http://localhost:3000 --headless \
-u 50 -r 5 --run-time 5m --csv=results
| Concurrent Users | Median Latency | P95 Latency | Failure Rate |
|---|---|---|---|
| 10 | 2.1s | 4.5s | 0% |
| 25 | 2.8s | 6.2s | 0% |
| 50 | 3.5s | 8.1s | <1% |
| 100 | 5.2s | 12.4s | 3-5% |
Note: The bottleneck at higher concurrency is almost always the upstream LLM API rate limit, not the Lighthouse instance itself. OpenClaw's server-side processing adds minimal overhead.
Monitor system resources during your load tests to identify bottlenecks:
# Terminal 1: Run the load test
locust -f locustfile.py ...
# Terminal 2: Monitor resources
vmstat 1 | tee vmstat_results.txt
# Terminal 3: Monitor OpenClaw process specifically
pidstat -p $(pgrep -f openclaw) 1
What to watch for:
iowait indicates storage bottleneck — unlikely on SSD-backed Lighthouse instances but worth checking if you're logging aggressively.Skills add processing overhead. Benchmark them individually:
# Time a skill-heavy request
time curl -X POST http://localhost:3000/api/chat \
-H "Content-Type: application/json" \
-d '{"message": "Run the knowledge base search for cloud deployment best practices", "session_id": "skill-bench"}'
Compare the latency of:
This helps you understand the marginal cost of each skill and make informed decisions about which skills to enable in production.
Based on benchmarking results, apply these optimizations:
cores - 1)After running all benchmarks, document your baseline:
OpenClaw Performance Baseline — [Date]
Instance: Tencent Cloud Lighthouse 2C/4G
Region: [Your Region]
OpenClaw Version: [Version]
Skills Installed: [List]
Max Concurrent Users (< 5s P95): 40
Max Throughput: 12 req/s
CPU Headroom at Steady State: 35%
Memory Usage at Steady State: 1.2GB / 4GB
Re-run benchmarks after every major update — OpenClaw version upgrades, new skill installations, or LLM provider changes.
Your benchmarks tell you when to scale. Here's the decision tree:
Tencent Cloud Lighthouse makes vertical scaling painless — upgrade your instance tier through the console with minimal downtime. Check the Special Offer page for cost-effective upgrade options that give you more headroom without overspending.
Performance testing isn't glamorous, but it's the difference between a bot that "seems fine" and one you can confidently put in front of users with defined SLAs. Spend an afternoon running these benchmarks, document your baseline, and you'll have the data you need to make informed scaling decisions as your OpenClaw deployment grows.