Is DeepSeek Down? How to Check DeepSeek API Status & What to Do

DeepSeek has emerged as a compelling OpenAI alternative, especially after their R1 reasoning model rivaled GPT-4's performance at a fraction of the cost. But with China-based infrastructure and rapid scaling, availability issues are more common than established providers. This guide covers everything you need to diagnose and respond to DeepSeek API problems.

Check DeepSeek Status Right Now

Check real-time DeepSeek API status on API Status Check →

We monitor DeepSeek's API endpoints every 60 seconds from multiple global regions and track response times, error rates, and regional availability patterns.

Understanding DeepSeek's Infrastructure

DeepSeek operates differently from Western AI providers, which affects reliability:

China-Based Infrastructure

DeepSeek's primary data centers are in mainland China (primarily Beijing and Shenzhen). This creates unique characteristics:

Latency patterns:

• Asia-Pacific: 80-150ms (best performance)
• US West Coast: 180-280ms
• US East Coast: 220-350ms
• Europe: 250-400ms

Great Firewall implications: While DeepSeek's API endpoints are globally accessible (not behind the firewall), network routing can be unpredictable. Some ISPs and cloud providers have better China connectivity than others.

Business hours correlation: Outages more common 9 AM - 6 PM China Standard Time (CST = UTC+8) when engineers are working and potentially deploying changes.

Multi-Model Architecture

DeepSeek offers several model families:

DeepSeek-V2 - General purpose chat model

• Endpoint: https://api.deepseek.com/v1/chat/completions
• Context: 64K tokens
• Typical response time: 2-4 seconds

DeepSeek-Coder - Code generation specialist

• Endpoint: https://api.deepseek.com/v1/chat/completions
• Context: 16K tokens
• Optimized for programming tasks

DeepSeek-R1 - Advanced reasoning model (newest)

• Endpoint: https://api.deepseek.com/v1/chat/completions
• Context: 64K tokens
• Longer inference time: 5-15 seconds

When checking "is DeepSeek down": Specify which model. R1 has different availability patterns (newer, more resource-intensive) than V2.

Rate Limiting Architecture

Unlike OpenAI's per-minute limits, DeepSeek uses concurrent request limits:

Free tier:

• 2 concurrent requests max
• If request 3 arrives while 2 are processing → 429 error

Paid tiers:

• Standard: 10 concurrent
• Pro: 50 concurrent
• Enterprise: negotiable

Why this matters: During high load, you'll hit limits even if you're under per-minute quotas. Your requests might be queued while others complete.

Common DeepSeek API Error Codes

HTTP 429: Rate Limit Exceeded

{
  "error": {
    "message": "Rate limit exceeded. Please try again later.",
    "type": "rate_limit_error",
    "code": "rate_limit_exceeded"
  }
}

Two possible causes:

1. Concurrent request limit (most common) You have 2+ requests processing simultaneously on free tier.

Fix:

import asyncio
from asyncio import Semaphore

# Limit concurrent requests
semaphore = Semaphore(2)  # Free tier limit

async def call_deepseek(prompt):
    async with semaphore:
        response = await client.chat.completions.create(
            model="deepseek-chat",
            messages=[{"role": "user", "content": prompt}]
        )
        return response

2. Token-per-minute limit

• Free tier: 100K tokens/minute
• Paid: scales with plan

Check response headers:

X-RateLimit-Limit-Requests: 2
X-RateLimit-Remaining-Requests: 0
X-RateLimit-Reset-Requests: 1738742400

HTTP 500: Internal Server Error

{
  "error": {
    "message": "Internal server error",
    "type": "server_error"
  }
}

This IS an outage. DeepSeek's inference servers are having issues. More common during:

• CST business hours (deployment windows)
• High-traffic periods (US evening = China morning)
• After new model launches

Pattern observed: 500 errors often resolve within 5-10 minutes as load balancers reroute traffic. Unlike OpenAI, DeepSeek's 500s tend to be transient.

HTTP 503: Service Unavailable

{
  "error": {
    "message": "The server is temporarily unable to service your request. Please try again later.",
    "type": "service_unavailable"
  }
}

Overloaded infrastructure. This is DeepSeek's "we're scaling up, hold on" response. Common triggers:

• Viral moment (Reddit post about how cheap it is)
• Western business hours when global traffic spikes
• Competitor outage (when OpenAI goes down, DeepSeek gets flooded)

Recommended retry: Wait 30-60 seconds, not immediately. Infrastructure is scaling.

HTTP 401: Authentication Failed

{
  "error": {
    "message": "Incorrect API key provided",
    "type": "invalid_request_error"
  }
}

Your API key is wrong. DeepSeek keys format: sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Common mistakes:

• Including whitespace before/after key
• Using API key from dashboard vs. billing page (they're different)
• Key revoked after payment failure

Verify:

curl https://api.deepseek.com/v1/models \
  -H "Authorization: Bearer $DEEPSEEK_API_KEY"

Should return list of available models. If 401, key is invalid.

HTTP 400: Invalid Request

{
  "error": {
    "message": "Invalid parameter: messages",
    "type": "invalid_request_error"
  }
}

Your request is malformed. DeepSeek mostly follows OpenAI's API format but has differences:

Correct format:

response = client.chat.completions.create(
    model="deepseek-chat",  # NOT "deepseek-v2"
    messages=[
        {"role": "system", "content": "You are helpful"},
        {"role": "user", "content": "Hello"}
    ],
    temperature=0.7,
    max_tokens=1000  # Required field
)

Common errors:

• Using OpenAI model names (gpt-4 won't work)
• Omitting max_tokens (required, not optional like OpenAI)
• Invalid temperature range (must be 0-2, not 0-1)

Timeout Errors (No HTTP Response)

Request hangs for 60+ seconds, then times out.

Possible causes:

1. Network routing issues (China → Your Region) Test with different network:

# Try from different cloud provider
# AWS us-east-1
curl --max-time 10 https://api.deepseek.com/v1/models

# Try from Google Cloud us-west1
# Compare latency

DeepSeek has better connectivity from some providers (Alibaba Cloud, AWS) than others (smaller VPS providers).

2. DeepSeek-R1 long inference R1 reasoning model can take 15-30 seconds for complex problems. Not an error - just slow.

Set longer timeout:

from openai import OpenAI

client = OpenAI(
    api_key="your-deepseek-key",
    base_url="https://api.deepseek.com",
    timeout=60.0  # Increase from default 10s
)

How to Check DeepSeek Status

1. Official Status Page

https://status.deepseek.com

DeepSeek's incident tracker. Typically shows:

• API endpoint status
• Model availability (V2, Coder, R1)
• Scheduled maintenance (usually nights CST)

Caveat: Updates can lag 15-30 minutes. During January 2026 incident, API was erroring for 20 minutes before status page updated.

2. DeepSeek Community (Discord/GitHub)

GitHub Issues: https://github.com/deepseek-ai/DeepSeek-V2/issues

Active community reports problems here. Search for "API down" or "500 error" to see real-time reports from other developers.

Discord server: Unofficial communities exist, but no official DeepSeek Discord.

3. API Status Check (Our Monitoring)

https://apistatuscheck.com/deepseek

We test DeepSeek API from 5 global regions every 60 seconds:

• US East (Virginia)
• US West (California)
• Europe (Ireland)
• Asia (Singapore) - closest to origin
• Asia (Tokyo)

See:

• Current response times by region
• Error rate (429s, 500s, timeouts)
• Model-specific availability (if R1 is down but V2 works)

4. Manual Test Request

Simplest diagnostic:

curl https://api.deepseek.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPSEEK_API_KEY" \
  -d '{
    "model": "deepseek-chat",
    "messages": [{"role": "user", "content": "test"}],
    "max_tokens": 10
  }'

Expected response time:

• Asia: 1-2 seconds
• US: 3-5 seconds
• Europe: 4-6 seconds

If timeout or 500 error, API has issues.

5. Regional Health Check

Test from multiple regions to isolate geography-specific issues:

import httpx
import asyncio

regions = [
    ("US-East", "https://api-us-east.your-proxy.com"),
    ("Asia", "https://api-asia.your-proxy.com"),
]

async def check_region(name, proxy_url):
    try:
        # Proxy routes through that region
        start = time.time()
        response = await httpx.post(
            f"{proxy_url}/deepseek",
            timeout=10
        )
        latency = time.time() - start
        print(f"{name}: {latency*1000:.0f}ms - {response.status_code}")
    except:
        print(f"{name}: TIMEOUT or ERROR")

If Asia works but US fails, it's network routing, not DeepSeek infrastructure.

Troubleshooting DeepSeek Issues

Step 1: Verify API Key Permissions

DeepSeek has two types of API keys:

1. Trial keys (from dashboard without payment)

• Limited to 100 requests total
• Expires after 7 days
• Lower priority in queue

2. Paid keys (after adding payment method)

• Usage-based billing
• Higher rate limits
• Better availability during high load

Check which you have:

# This header shows your account type
curl -I https://api.deepseek.com/v1/models \
  -H "Authorization: Bearer $DEEPSEEK_API_KEY" \
  | grep X-Account-Type

Trial keys fail more during peak hours.

Step 2: Optimize for DeepSeek's Network

Use Asia-region cloud servers when possible:

Best performance:

• AWS ap-southeast-1 (Singapore)
• Alibaba Cloud (China regions)
• Google Cloud asia-east1 (Taiwan)

Suboptimal:

• US East Coast providers
• Small VPS providers with poor China connectivity

If you're US-based: Accept 200-300ms latency or use a proxy server in Asia.

Step 3: Implement Intelligent Retries

DeepSeek's transient errors resolve faster than OpenAI's:

import time
from openai import OpenAI, APIError

def call_deepseek_with_retry(messages, max_retries=3):
    client = OpenAI(
        api_key="your-key",
        base_url="https://api.deepseek.com",
        timeout=30.0
    )
    
    for attempt in range(max_retries):
        try:
            return client.chat.completions.create(
                model="deepseek-chat",
                messages=messages,
                max_tokens=1000
            )
        except APIError as e:
            if e.status_code == 500:
                # 500s usually transient
                wait = 5 * (attempt + 1)  # 5s, 10s, 15s
                time.sleep(wait)
            elif e.status_code == 503:
                # Overloaded, be more patient
                wait = 30 * (attempt + 1)  # 30s, 60s, 90s
                time.sleep(wait)
            elif e.status_code == 429:
                # Rate limit, check if concurrent or token limit
                wait = 10
                time.sleep(wait)
            else:
                raise  # 400, 401 won't fix with retry
    
    raise Exception("DeepSeek API failed after retries")

Step 4: Monitor Concurrent Requests

Free tier max: 2 concurrent

Track active requests:

import threading

class DeepSeekClient:
    def __init__(self, max_concurrent=2):
        self.semaphore = threading.Semaphore(max_concurrent)
        self.active_requests = 0
        
    def call(self, messages):
        with self.semaphore:
            self.active_requests += 1
            print(f"Active: {self.active_requests}")
            try:
                response = client.chat.completions.create(...)
                return response
            finally:
                self.active_requests -= 1

If you consistently hit the limit, upgrade to paid tier.

Step 5: Test Different Models

If deepseek-chat (V2) is down, try alternatives:

models_to_try = [
    "deepseek-chat",      # V2 - general purpose
    "deepseek-coder",     # Coder - for code tasks
]

for model in models_to_try:
    try:
        response = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": "test"}],
            max_tokens=10
        )
        print(f"{model}: WORKING")
        break
    except:
        print(f"{model}: FAILED")

Different models run on different infrastructure - one can be down while others work.

What to Do During a DeepSeek Outage

1. Implement Multi-Provider Fallback

Don't rely solely on DeepSeek:

def get_ai_response(prompt):
    providers = [
        ("DeepSeek", call_deepseek),
        ("OpenAI", call_openai),
        ("Anthropic", call_anthropic),
    ]
    
    for name, provider_fn in providers:
        try:
            return provider_fn(prompt)
        except Exception as e:
            print(f"{name} failed: {e}")
            continue
    
    # All providers down - use cache or error
    return get_cached_response(prompt)

Cost consideration: DeepSeek is ~10x cheaper than OpenAI. Budget for occasional OpenAI fallback.

2. Cache Aggressively

DeepSeek's low cost makes caching optional during normal operation, but critical during outages:

import hashlib
import json

cache = {}

def get_or_generate(messages):
    # Cache key from message content
    key = hashlib.sha256(
        json.dumps(messages, sort_keys=True).encode()
    ).hexdigest()
    
    if key in cache:
        return cache[key]
    
    try:
        response = client.chat.completions.create(...)
        cache[key] = response
        return response
    except:
        # Return stale cache if available
        if key in cache:
            return cache[key]
        raise

3. Queue Non-Real-Time Requests

Use job queue for batch processing:

from celery import Celery

app = Celery('tasks', broker='redis://localhost:6379')

@app.task(bind=True, max_retries=3)
def process_with_deepseek(self, prompt):
    try:
        return call_deepseek(prompt)
    except Exception as e:
        # Retry in 5 minutes
        raise self.retry(exc=e, countdown=300)

During outages, jobs queue up and process when API recovers.

4. Set User Expectations

Be transparent about AI backend:

// Frontend notification
if (aiProviderStatus === 'degraded') {
  showToast(
    'Our AI provider (DeepSeek) is experiencing high load. ' +
    'Responses may be slower than usual. We've activated backup systems.'
  );
}

Users appreciate honesty more than silent failures.

DeepSeek Outage History & Patterns

Notable Incidents

January 18, 2026 - 3-hour R1 model unavailability

• Cause: Inference infrastructure couldn't handle R1's compute requirements during viral spike
• Impact: R1 returned 503 errors, V2 unaffected
• Recovery: Scaled GPU capacity, implemented queue system
• Lesson: New model launches = instability window

December 2025 - Recurring evening (CST) slowdowns

• Cause: Western usage peak coinciding with China morning deployment window
• Impact: 2-5x normal latency, occasional 503s
• Recovery: Shifted deployment windows to China midnight
• Lesson: Time zone coordination matters

October 2025 - Payment system failure

• Cause: Payment processor (Stripe) issue blocked API key generation
• Impact: Existing keys worked, new users couldn't get access
• Recovery: 6 hours, manual key generation
• Lesson: DeepSeek has fewer redundant systems than mature providers

Patterns Observed

Time correlation:

• Most reliable: China nights (8 PM - 8 AM CST = 4 AM - 4 PM PT)
• Most issues: China mornings (9 AM - 12 PM CST = 5 PM - 8 PM PT)

Day of week: Fewer issues on weekends (less deployment activity)

Global events: When OpenAI has outages, DeepSeek gets traffic spikes → 503 errors

Model differences:

• V2 uptime: ~98.5%
• Coder uptime: ~98.8% (less traffic)
• R1 uptime: ~95% (newer, more complex)

Geographic variance:

• Asia-Pacific: 99% uptime from our monitoring
• North America: 97% effective uptime (network routing issues)
• Europe: 96.5% effective uptime

DeepSeek vs Alternatives

When DeepSeek is Down, Where to Fallback?

Quick comparison:

Provider	Cost (1M tokens)	Latency	Reliability
DeepSeek	$0.14 (V2)	2-4s	97-98%
OpenAI GPT-4o	$2.50	1-2s	99.5%
Anthropic Claude	$3.00	1-2s	99.5%
Groq (Llama)	$0.10	0.5s	97%
Together.ai	$0.20	1-3s	98%

Best fallback for DeepSeek:

• Performance match: OpenAI GPT-4o-mini ($0.15/1M) - similar cost/quality
• Speed priority: Groq - fastest, but limited models
• Quality priority: Claude - best reasoning, higher cost

Migration Checklist

DeepSeek uses OpenAI-compatible API, so switching is easy:

# Change base URL
client = OpenAI(
    api_key="new-provider-key",
    base_url="https://api.openai.com/v1",  # or Anthropic, etc.
)

# Update model name
model="gpt-4o"  # was "deepseek-chat"

# Remove DeepSeek-specific params
# (most are compatible, but check docs)

Get Alerts for DeepSeek Issues

Monitor DeepSeek API health →

Our Alert Pro plan ($9/month) tracks DeepSeek across 5 global regions:

• 60-second health checks with regional latency tracking
• Instant notifications when errors spike
• Multi-provider monitoring (monitor your fallbacks too)
• Historical trends showing China business hours correlation

Know about DeepSeek degradation before your error logs fill up.

FAQ

Q: Is DeepSeek down right now?
A: Check apistatuscheck.com/deepseek for real-time status from multiple global regions.

Q: Why is DeepSeek so much cheaper than OpenAI?
A: Lower operational costs (China infrastructure), newer/optimized architecture, and strategic pricing to gain market share. The tradeoff is slightly lower reliability.

Q: Is DeepSeek safe to use for production?
A: Yes, but implement fallbacks. Treat it as primary provider with OpenAI/Anthropic as backup. Don't rely on 99.9% uptime expectations.

Q: Why does DeepSeek work from Singapore but not California?
A: Network routing from China. Asia-Pacific has better connectivity to DeepSeek's infrastructure. US traffic routes through more hops, increasing latency and timeout risk.

Q: Can I host DeepSeek models myself?
A: DeepSeek-V2 and Coder models are open source - you can self-host. R1 is not yet open source. Self-hosting eliminates API availability issues but adds infrastructure complexity.

Q: Does DeepSeek have an SLA?
A: No published SLA for standard API access. Enterprise contracts may include custom agreements.

Q: How long do DeepSeek outages last?
A: Minor incidents: 10-30 minutes. Major outages: 1-3 hours. Longest observed: 3 hours (R1 launch issues).

Q: Should I retry 503 errors immediately?
A: No - wait 30-60 seconds. 503 means infrastructure is scaling up. Immediate retries make it worse.

Q: Why does DeepSeek-R1 timeout but V2 works?
A: R1 uses chain-of-thought reasoning, taking 15-30 seconds for complex queries. Increase your client timeout to 60+ seconds.

Q: Is there a DeepSeek status Twitter account?
A: No official @deepseek_status. Follow @deepseek_ai for announcements, but they don't actively post about outages.

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Last updated: February 5, 2026
Next review: Weekly (high-interest API)

Check DeepSeek Status →