Table of Contents
Why Latency Matters for AI APIs
When building AI-powered applications, latency directly impacts user experience. Here's why:
- User Perception: Studies show users perceive delays over 200ms as "slow"
- Conversation Flow: In chat applications, high latency breaks the natural conversation rhythm
- Streaming Quality: High latency causes choppy, uneven token streaming
- Cost: Longer response times mean slower applications and lower throughput
- Competitive Advantage: Faster APIs create better user experiences
💡 The Latency Stack
Total response time = Network latency + Queue time + Model inference time + Output generation time. Network latency is the only component you can control through provider choice.
Testing Methodology
How We Tested
- Test Locations: Singapore, Tokyo, Hong Kong (AWS EC2 instances)
- Test Duration: 7 days (March 6-12, 2025)
- Requests per Provider: 1,000+ requests
- Payload: Standard 50-token prompt
- Metric: Time to first token (TTFT) - network latency + queue time
- Protocol: HTTPS with HTTP/2 where supported
What We Measured
- Network Latency: Round-trip time to establish connection
- Time to First Token (TTFT): Time until first response token arrives
- Inter-token Latency: Time between consecutive tokens during streaming
- P50/P95/P99: Percentile distributions for consistency analysis
Test Results: Latency by Provider
From Singapore
From Hong Kong
From Tokyo
Real-World Impact
Chat Application Example
For a typical chat application with 10 back-and-forth messages:
| Provider | Latency per Request | Total Wait Time (10 msgs) | User Perception |
|---|---|---|---|
| NovAI (HK) | 80ms | 0.8 seconds | Instant |
| AWS Bedrock | 120ms | 1.2 seconds | Fast |
| OpenRouter | 220ms | 2.2 seconds | Noticeable delay |
| Anthropic | 350ms | 3.5 seconds | Slow |
Streaming Quality
Latency also affects streaming quality:
- <100ms: Smooth, natural token streaming
- 100-200ms: Good streaming with occasional pauses
- 200-300ms: Choppy streaming, noticeable gaps
- >300ms: Poor streaming experience, users may think it's broken
Provider Deep Dive
NovAI (Hong Kong)
- Server Location: Hong Kong
- Best For: Asian users, Chinese models (DeepSeek, Qwen, GLM)
- Latency Advantage: 4x faster than US-based providers from Asia
- Models: DeepSeek, Qwen, GLM, Doubao, Moonshot
Google Vertex AI
- Server Locations: Singapore, Tokyo, Osaka
- Best For: Claude users in Asia
- Latency: Good regional presence
- Setup Complexity: Moderate (requires GCP account)
AWS Bedrock
- Server Locations: Singapore, Tokyo, Sydney
- Best For: AWS customers, enterprise users
- Latency: Good with regional endpoints
- Setup Complexity: Moderate (requires AWS account)
OpenRouter
- Server Location: United States
- Best For: Experimenting with many models
- Latency: Higher from Asia (~200-250ms)
- Advantage: Access to 100+ models
Anthropic Direct
- Server Location: United States
- Best For: Enterprise users needing official support
- Latency: Highest from Asia (~300-400ms)
- Advantage: Direct access, latest features
How to Optimize AI API Latency
1. Choose the Right Provider Location
Select providers with servers closest to your users:
- Asia users: NovAI, Google Vertex (Singapore/Tokyo), AWS Bedrock
- US users: Anthropic, OpenRouter, Any provider
- Europe users: OpenRouter, Azure (European regions)
2. Use Streaming
Always enable streaming to improve perceived performance:
// Enable streaming
const response = await fetch('/api/chat', {
method: 'POST',
body: JSON.stringify({
message: userInput,
stream: true // Enable streaming
})
});
// Read stream
const reader = response.body.getReader();
while (true) {
const { done, value } = await reader.read();
if (done) break;
// Display tokens as they arrive
}
3. Implement Connection Pooling
Reuse HTTP connections to avoid TLS handshake overhead:
import httpx
# Use a client with connection pooling
client = httpx.Client(http2=True, limits=httpx.Limits(max_connections=100))
# Reuse for multiple requests
response1 = client.post(...)
response2 = client.post(...)
4. Cache Common Responses
Cache responses for frequently asked questions:
from functools import lru_cache
import hashlib
@lru_cache(maxsize=1000)
def get_cached_response(query_hash):
# Return cached response
pass
def chat(query):
query_hash = hashlib.md5(query.encode()).hexdigest()
cached = get_cached_response(query_hash)
if cached:
return cached
# Otherwise, call API
5. Use Edge Functions
Deploy API calls at the edge (Vercel Edge, Cloudflare Workers):
// Vercel Edge Function
export const config = {
runtime: 'edge',
regions: ['hkg1', 'sin1', 'tok1'] // Deploy to Asia
};
export default async function handler(req) {
// Your API call runs at the edge, close to users
const response = await fetch('https://aiapi-pro.com/api/...');
return new Response(response.body);
}
Conclusion
Our testing clearly shows that server location is the most important factor for AI API latency in Asia:
- Hong Kong-based providers (NovAI) offer 4-5x lower latency than US-based providers
- Cloud providers with Asian regions (Google, AWS, Azure) offer good middle-ground performance
- Direct API access from US providers creates noticeable delays for Asian users
For production applications serving Asian users, we recommend:
- Use NovAI for Chinese models (DeepSeek, Qwen, GLM) - lowest latency
- Use Google Vertex or AWS Bedrock for Claude - good regional presence
- Always enable streaming to improve perceived performance
- Implement caching and connection pooling
💡 Test It Yourself
Try NovAI's free playground to experience the difference. No signup required - test Qwen, DeepSeek, and other models with ~80ms network latency from Hong Kong.
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