Why Is Kimi AI Always Busy? Server Issues Explained and How to Fix Them

Mar 25, 2026

The Problem: “Server Busy” Errors

I was trying to use Kimi AI for a coding task when I hit this error:

Server is busy, please try again later

Then I refreshed. Same error. I tried again. Same error. Then I got prompted to upgrade to a paid plan.

This happened repeatedly over several days. Was the server actually down? Was it a capacity issue? Or was something else going on?

After digging into the official documentation and user reports, I found the real culprit: rate limiting on the free tier.

The Root Cause: Tier 0 Limitations

Kimi AI (developed by Moonshot AI) uses a tiered access system. The free tier (Tier 0) has severe restrictions:

Limit Type	Tier 0 (Free)	Tier 1 (¥50)
Concurrent Requests	1	50
RPM (Requests/Minute)	3	200
TPM (Tokens/Minute)	500,000	2,000,000
TPD (Tokens/Day)	1,500,000	Unlimited

1 concurrent request means you can only have one active conversation at a time. 3 RPM means you can only make 3 requests per minute.

This explains why “server busy” appears so often - it’s not the server being down, it’s the rate limit kicking in.

How I Fixed It

For Web/App Users

Step 1: Slow Down Your Requests

I was rapidly clicking refresh when stuck. That’s the worst thing to do - each refresh counts as a new request and consumes your RPM quota.

What I did wrong:
  Request 1 → Error → Refresh (Request 2) → Error → Refresh (Request 3) → Error
  → Now I've exhausted my 3 RPM limit!

What I should do:
  Request 1 → Error → Wait 20+ seconds → Request 2 → Likely succeeds

Step 2: Avoid Peak Hours

Kimi’s servers (hosted in China) experience peak traffic during:

Peak Hours (China Standard Time):
  9:00 AM - 10:00 PM (work hours + evening)

Better Times:
  5:00 AM - 7:00 AM (early morning)
  11:00 PM - 1:00 AM (late night)

Step 3: Use Fewer, Longer Prompts

Instead of:

User: "What is Python?"
[wait for response]
User: "Tell me about decorators"
[wait for response]
User: "Show me an example"

Do this:

User: "Explain Python decorators with a practical example and explain when to use them"

One comprehensive request uses fewer RPM than multiple short ones.

For API Developers

I initially had the wrong endpoint configuration:

# WRONG: Using wrong endpoint
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxx",
    base_url="https://api.moonshot.cn/v1"  # Wrong if key is from .ai domain
)

The correct configuration depends on where you got your API key:

from openai import OpenAI

# China platform (platform.moonshot.cn)
client_cn = OpenAI(
    api_key="sk-xxx",
    base_url="https://api.moonshot.cn/v1"
)

# International platform (platform.moonshot.ai)
client_intl = OpenAI(
    api_key="sk-xxx",
    base_url="https://api.moonshot.ai/v1"
)

Using the wrong endpoint causes authentication errors that look like server issues.

Implement Exponential Backoff

The OpenAI SDK has built-in retry logic, but default settings can exhaust your RPM quota:

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxx",
    base_url="https://api.moonshot.cn/v1",
    max_retries=2,  # Don't set too high on Tier 0
    timeout=60.0
)

# Better: Custom exponential backoff
import time
import random

def call_with_backoff(func, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            wait = (2 ** attempt) + random.random()
            time.sleep(wait)

Enable Streaming

The official docs recommend streaming to reduce connection errors:

stream = client.chat.completions.create(
    model="moonshot-v1-8k",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True  # Recommended by Moonshot
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")

Why This Happens: The Economics

Kimi offers a capable AI at no cost (or low cost). The trade-off is aggressive rate limiting on free tiers to:

Prevent abuse: Malicious actors might flood the API
Manage capacity: Server resources cost money
Encourage upgrades: Free tier friction drives paid conversions

The official documentation is clear:

“When cluster load reaches capacity limits, we may take temporary throttling measures”

Common Mistakes I Made

Mistake 1: Rapid Retry on Errors

I assumed “server busy” meant the server was down. I’d immediately retry multiple times. But each retry consumes an RPM from my quota of 3.

Mistake 2: Ignoring Regional Endpoints

My API key was from platform.moonshot.ai but I was calling api.moonshot.cn. This caused auth errors that looked like server issues.

Mistake 3: Not Tracking Token Usage

The 1.5M TPD limit sounds generous, but file uploads and long conversations add up quickly:

Token usage breakdown:
  - File upload (10 pages): ~15,000 tokens
  - Long conversation: ~50,000 tokens per session
  - Daily limit: 1,500,000 tokens

Result: ~30 long conversations before hitting the limit

Mistake 4: Confusing “Server Busy” with Server Down

The error is often rate limiting, not server unavailability. This is by design.

When to Consider Alternatives

If you need reliable free access, consider:

Service	Free Tier RPM	Notes
Kimi AI	3	Strong reasoning, strict limits
Gemini Flash	15	Generous free tier
Claude	Varies	Different strengths
ChatGPT	3 (GPT-4o mini)	More consistent

For production applications, the paid tiers are reasonably priced:

Tier 1: ¥50 (~$7 USD) - 200 RPM, 50 concurrent
Tier 2: ¥100 (~$14 USD) - 500 RPM, 100 concurrent

What Worked for Me

Slowed down: 20+ seconds between requests
Early morning usage: 5-7 AM China time
Batched prompts: Fewer, longer requests
Correct endpoints: Matched API key domain
Streaming enabled: Fewer connection issues

The “server busy” errors dropped significantly after these changes.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!