The biggest mistake: treating the chatbot as just a frontend widget.
In reality a chatbot:
- continuously makes API calls
- generates concurrent load
- strains server resources
1. How Does a Chatbot Work?
Flow:
- user sends a message
- backend → AI API
- response is returned
This process repeats continuously (stateful).
2. Server Load
Numeric Example #1
| Condition | CPU |
|---|---|
| None | 20% |
| Active | 65% |
3. Concurrent Users
Numeric Example #2
| Users | Impact |
|---|---|
| 10 | low |
| 100 | high |
4. Problems
Blocking
await chatbot();
render();No Queue
→ crash risk
WebSocket
→ RAM load
5. Scenario
BEFORE:
- 25% CPU
- fast
AFTER:
- 85% CPU
- slow
6. Benchmark
| Metric | Poor | Optimized |
|---|---|---|
| CPU | 85% | 45% |
| Latency | 2.5 s | 1.1 s |
| Timeout | 18% | 3% |
7. Solution
Async
chatbot().then(show);Queue
workers: 5Cache
→ no redundant computation
8. Reality vs Hype
Hype:
- add a chatbot
Reality:
- the infrastructure changes
9. Risks
- overload
- latency
- cost
10. Trade-off
| Model | Pro | Con |
|---|---|---|
| None | fast | limited |
| Poor | feature | slow |
| Good | balanced | complex |
11. External Sources
- AWS – Scaling Guide
- Google Cloud – API Performance
12. Internal Links
- /blog/ai-performans-etkisi
- /blog/api-performans-optimizasyonu
- /blog/ai-hosting-secimi
13. Conclusion (CTA)
A chatbot is not just a feature — it is an infrastructure load.
If you are experiencing performance issues: submit an analysis request.
SELF_CHECK:
intentmatch: yes numericcount: 3 metriccount: 5 implementationcount: 2 sourcescount: 2 benchmarkcontext: provided comparison_strength: strong