Is Perplexity Down? How to Check Perplexity AI Status in Real-Time

Quick Answer: To check if Perplexity is down, visit apistatuscheck.com/api/perplexity for real-time monitoring of both the web platform and API. Common signs include search timeouts, API authentication failures, citation loading errors, rate limiting issues, and Pro subscription access problems.

When your AI-powered research workflow suddenly stops working, every moment of uncertainty impacts productivity. Perplexity AI has become the go-to conversational search engine for researchers, developers, and knowledge workers worldwide. Whether you're seeing API timeouts, citation errors, or Pro features not loading, knowing how to quickly verify Perplexity's status can save you valuable troubleshooting time and help you decide whether to wait or switch to alternative AI search solutions.

How to Check Perplexity Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify Perplexity's operational status is through apistatuscheck.com/api/perplexity. This real-time monitoring service:

• Tests actual API endpoints every 60 seconds
• Shows response times and latency trends for search queries
• Tracks historical uptime over 30/60/90 days
• Provides instant alerts when issues are detected
• Monitors multiple endpoints (web search, chat, citations)

Unlike status pages that rely on manual updates, API Status Check performs active health checks against Perplexity's production API endpoints, giving you the most accurate real-time picture of service availability. This is especially critical for businesses integrating Perplexity into production workflows.

2. Official Perplexity Status Page

While Perplexity doesn't currently maintain a public status.perplexity.ai page like some competitors, you can check their official communication channels:

• X (Twitter) @perplexity_ai - Official announcements about outages
• Discord community - Real-time user reports and staff responses
• Help Center - Service notices and maintenance windows

Pro tip: Follow @perplexity_ai on X and enable notifications to receive immediate updates when incidents are announced. The team is typically responsive during outages and posts ETAs for resolution.

3. Test the Web Interface

Visit perplexity.ai and try a simple search query:

• If the page loads but searches timeout → API backend issues
• If searches work but citations fail to load → Citation engine problems
• If you get login errors → Authentication service down
• If Pro features don't work → Subscription service issues

Pay attention to error messages like "Something went wrong" or "Unable to process your request" which indicate backend problems rather than query-specific issues.

4. Test API Endpoints Directly

For developers using Perplexity API, making a test API call can quickly confirm connectivity:

import requests

def check_perplexity_status():
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "llama-3.1-sonar-small-128k-online",
        "messages": [
            {"role": "user", "content": "Test query"}
        ]
    }
    
    try:
        response = requests.post(url, json=payload, headers=headers, timeout=30)
        if response.status_code == 200:
            print(f"✓ API operational - Response time: {response.elapsed.total_seconds():.2f}s")
            return True
        else:
            print(f"✗ API error: {response.status_code} - {response.text}")
            return False
    except requests.exceptions.Timeout:
        print("✗ API timeout - Service may be down")
        return False
    except requests.exceptions.RequestException as e:
        print(f"✗ Connection error: {str(e)}")
        return False

check_perplexity_status()

Look for HTTP response codes outside the 2xx range, timeout errors, or connection failures.

5. Check Community Reports

Monitor real-time user reports on:

• Reddit r/perplexity_ai - User community discussions
• Hacker News - Tech community often reports AI service issues quickly
• DownDetector - Crowdsourced outage reports (though less reliable for API services)

If multiple users are reporting similar issues simultaneously, it's likely a Perplexity-side problem rather than your local configuration.

Common Perplexity Issues and How to Identify Them

Search API Timeouts

Symptoms:

• Queries hanging for 30+ seconds before timing out
• 504 Gateway Timeout HTTP errors
• Searches work intermittently but not consistently
• Response times 5-10x slower than normal baseline (3-5 seconds)

What it means: When Perplexity's search infrastructure is under heavy load or experiencing issues, the most common manifestation is increased latency and eventual timeouts. This differs from rate limiting—you'll see timeouts even on your first request of the day.

Debugging code:

import requests
import time

def measure_perplexity_latency(api_key, test_queries=3):
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    latencies = []
    for i in range(test_queries):
        payload = {
            "model": "llama-3.1-sonar-small-128k-online",
            "messages": [{"role": "user", "content": f"What is {i+1}+{i+1}?"}]
        }
        
        start_time = time.time()
        try:
            response = requests.post(url, json=payload, headers=headers, timeout=30)
            latency = time.time() - start_time
            latencies.append(latency)
            print(f"Query {i+1}: {latency:.2f}s - Status: {response.status_code}")
        except requests.exceptions.Timeout:
            print(f"Query {i+1}: TIMEOUT (>30s)")
            latencies.append(30.0)
    
    avg_latency = sum(latencies) / len(latencies)
    print(f"\nAverage latency: {avg_latency:.2f}s")
    
    if avg_latency > 10:
        print("⚠️ DEGRADED PERFORMANCE - Perplexity may be experiencing issues")
    elif avg_latency < 5:
        print("✓ Normal performance")
    
    return avg_latency

# Run the check
measure_perplexity_latency("YOUR_API_KEY")

Rate Limiting Issues

Common error codes during rate limit problems:

• 429 Too Many Requests - Standard rate limit exceeded
• {error: {message: "Rate limit exceeded"}} - API quota exhausted
• Unexpected rate limits well below your plan's quota

What's normal vs. what's not:

Plan	Expected Limit	Issue Indicator
Free	5 requests/hour	Getting 429s at 1 request/hour
Standard	50 requests/minute	Rate limited at 10 requests/minute
Pro	600 requests/minute	Limits triggering under normal usage

If you're seeing rate limits significantly below your plan's quota, this may indicate backend issues rather than actual overuse. During incidents, Perplexity sometimes implements emergency rate limiting to protect infrastructure.

Check your rate limit status:

def check_rate_limit_headers(api_key):
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "llama-3.1-sonar-small-128k-online",
        "messages": [{"role": "user", "content": "Test"}]
    }
    
    response = requests.post(url, json=payload, headers=headers)
    
    # Check rate limit headers
    print("Rate Limit Info:")
    print(f"  Limit: {response.headers.get('x-ratelimit-limit', 'N/A')}")
    print(f"  Remaining: {response.headers.get('x-ratelimit-remaining', 'N/A')}")
    print(f"  Reset: {response.headers.get('x-ratelimit-reset', 'N/A')}")
    
    return response.headers

Citation and Source Errors

Indicators:

• Search completes but "Sources" section shows errors
• Citations return empty or null values
• Specific domains consistently fail to load
• Failed to fetch sources error messages

What causes this: Perplexity's citation system relies on web scraping infrastructure separate from the core search API. When this subsystem fails:

• You'll still get AI-generated answers
• But source attribution will be missing or incomplete
• This particularly affects Pro features that promise transparency

For developers using the API:

def validate_citations(api_response):
    """Check if Perplexity response includes valid citations"""
    try:
        message = api_response['choices'][0]['message']
        citations = message.get('citations', [])
        
        if not citations:
            print("⚠️ No citations provided")
            return False
        
        # Validate citation structure
        for idx, citation in enumerate(citations):
            if not citation.get('url'):
                print(f"⚠️ Citation {idx+1} missing URL")
                return False
        
        print(f"✓ {len(citations)} valid citations found")
        return True
    except KeyError:
        print("✗ Invalid response structure")
        return False

Pro Subscription Issues

Signs your Pro subscription isn't working:

• Getting free-tier rate limits despite active subscription
• Pro-only models (like Claude) returning access denied
• Billing shows active but features unavailable
• "Upgrade to Pro" messages despite being subscribed

Common causes during outages:

• Authentication service disconnection from billing system
• Database sync issues between payment processor and API
• Session token validation failures

Verify your Pro status via API:

def check_pro_access(api_key):
    """Test access to Pro-only features"""
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    # Test Pro-only model
    payload = {
        "model": "llama-3.1-sonar-large-128k-online",  # Pro model
        "messages": [{"role": "user", "content": "Test Pro access"}]
    }
    
    response = requests.post(url, json=payload, headers=headers)
    
    if response.status_code == 200:
        print("✓ Pro access confirmed")
        return True
    elif response.status_code == 403:
        print("✗ Pro access denied - subscription may not be active")
        return False
    else:
        print(f"? Unexpected status: {response.status_code}")
        return None

API Authentication Failures

Error patterns:

• 401 Unauthorized despite correct API key
• Invalid authentication credentials
• Intermittent auth failures (works sometimes, fails others)
• JWT token expired errors

What this indicates: When Perplexity's authentication service experiences issues:

• Valid API keys get rejected
• Token validation fails randomly
• Even successful authentications may be delayed

This differs from incorrect API keys (which always fail) by the intermittent nature and affecting multiple users simultaneously.

The Real Impact When Perplexity Goes Down

Disrupted Research Workflows

For knowledge workers and researchers who've integrated Perplexity into daily workflows:

• Academic researchers: Literature reviews halt mid-progress
• Content creators: Research for articles and videos stops
• Analysts: Market research and competitive intelligence delayed
• Students: Assignment research interrupted during critical deadlines

Time cost example: A researcher using Perplexity for 20-30 queries per day might lose 2-4 hours of productivity during a 3-hour outage, especially if they've optimized their workflow around Perplexity's specific capabilities like academic source prioritization.

Broken AI-Assisted Search Applications

Developers building on Perplexity API face immediate user impact:

• Customer support bots: Unable to search knowledge bases
• Research assistants: Can't fetch current information
• Content recommendation engines: Fail to discover related content
• Competitive intelligence tools: Data gathering halts

Cascading failures: Since Perplexity is often the "search brain" in larger AI systems, its downtime can make entire applications appear broken even when other components work fine.

Content Generation Pipeline Failures

Content creators using Perplexity for AI-assisted writing face:

• Blog writers: Can't verify facts or find current statistics
• Video creators: Research for scripts stalls
• Newsletter publishers: Unable to curate or validate trending topics
• SEO teams: Keyword research and competitor analysis blocked

For teams with tight publishing schedules, a 4-hour outage during final fact-checking can mean missed deadlines and rescheduled launches.

Alternative API Costs

When Perplexity API goes down, switching to fallback services incurs costs:

• OpenAI GPT-4 with browsing: 10-20x more expensive per query
• Anthropic Claude with search: Requires custom implementation
• Google Search API: Different data structure, integration refactoring needed
• Developer time: 2-4 hours adapting code to fallback APIs

For a product doing 10,000 Perplexity queries/day at $0.001-0.005 per query ($10-50/day), switching to GPT-4 could cost $100-1000/day during the outage.

Loss of Competitive Advantage

Businesses that differentiate on Perplexity's unique capabilities:

• Real-time web search accuracy: Competitors using cached data gain advantage
• Citation quality: Users may prefer cited answers over generic AI responses
• Source diversity: Perplexity's multi-source synthesis is hard to replicate

During outages, your product's key differentiator disappears, potentially driving users to competitors who use different underlying technologies.

Incident Response Playbook for Perplexity Outages

1. Implement Robust Retry Logic with Exponential Backoff

Smart retry pattern for Perplexity API:

import time
import random
from functools import wraps

def retry_with_backoff(max_retries=3, base_delay=1, max_delay=60):
    """Decorator for retrying Perplexity API calls with exponential backoff"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except requests.exceptions.Timeout:
                    if attempt == max_retries - 1:
                        raise
                    delay = min(base_delay * (2 ** attempt) + random.uniform(0, 1), max_delay)
                    print(f"Timeout on attempt {attempt + 1}, retrying in {delay:.2f}s...")
                    time.sleep(delay)
                except requests.exceptions.RequestException as e:
                    if attempt == max_retries - 1:
                        raise
                    # Don't retry on 4xx errors except 429
                    if hasattr(e, 'response') and e.response is not None:
                        if 400 <= e.response.status_code < 500 and e.response.status_code != 429:
                            raise
                    delay = min(base_delay * (2 ** attempt), max_delay)
                    print(f"Error on attempt {attempt + 1}, retrying in {delay:.2f}s...")
                    time.sleep(delay)
        return wrapper
    return decorator

@retry_with_backoff(max_retries=3, base_delay=2, max_delay=30)
def query_perplexity(api_key, question):
    """Query Perplexity with automatic retry logic"""
    url = "https://api.perplexity.ai/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "llama-3.1-sonar-small-128k-online",
        "messages": [{"role": "user", "content": question}]
    }
    
    response = requests.post(url, json=payload, headers=headers, timeout=30)
    response.raise_for_status()
    return response.json()

2. Implement Fallback to Alternative AI Search

Multi-provider search strategy:

class AISearchRouter:
    """Route searches across multiple AI providers with fallback"""
    
    def __init__(self, perplexity_key, openai_key, anthropic_key):
        self.perplexity_key = perplexity_key
        self.openai_key = openai_key
        self.anthropic_key = anthropic_key
        self.perplexity_healthy = True
        self.last_health_check = 0
    
    def search(self, query):
        """Try Perplexity first, fallback to alternatives"""
        # Check health status every 5 minutes
        if time.time() - self.last_health_check > 300:
            self.perplexity_healthy = self._check_perplexity_health()
            self.last_health_check = time.time()
        
        if self.perplexity_healthy:
            try:
                return self._search_perplexity(query), "perplexity"
            except Exception as e:
                print(f"Perplexity failed: {e}, falling back...")
                self.perplexity_healthy = False
        
        # Fallback chain
        try:
            return self._search_openai_with_browsing(query), "openai"
        except Exception as e:
            print(f"OpenAI failed: {e}, trying Anthropic...")
            return self._search_anthropic(query), "anthropic"
    
    def _check_perplexity_health(self):
        """Quick health check"""
        try:
            query_perplexity(self.perplexity_key, "test")
            return True
        except:
            return False
    
    def _search_perplexity(self, query):
        return query_perplexity(self.perplexity_key, query)
    
    def _search_openai_with_browsing(self, query):
        # Implementation using OpenAI with browsing
        # See: https://apistatuscheck.com/api/openai
        pass
    
    def _search_anthropic(self, query):
        # Implementation using Anthropic Claude
        # See: https://apistatuscheck.com/api/anthropic
        pass

# Usage
router = AISearchRouter(
    perplexity_key="YOUR_PERPLEXITY_KEY",
    openai_key="YOUR_OPENAI_KEY",
    anthropic_key="YOUR_ANTHROPIC_KEY"
)

result, provider = router.search("What are the latest developments in quantum computing?")
print(f"Answer from {provider}: {result}")

3. Cache Frequently Used Queries

Implement intelligent caching to reduce API dependency:

import hashlib
import json
from datetime import datetime, timedelta

class PerplexityCacheWrapper:
    """Cache Perplexity responses to reduce API dependency during outages"""
    
    def __init__(self, api_key, cache_ttl_hours=24):
        self.api_key = api_key
        self.cache_ttl = timedelta(hours=cache_ttl_hours)
        self.cache = {}
    
    def _cache_key(self, query):
        """Generate cache key from query"""
        return hashlib.md5(query.lower().encode()).hexdigest()
    
    def search(self, query, force_refresh=False):
        """Search with caching"""
        key = self._cache_key(query)
        
        # Check cache first
        if not force_refresh and key in self.cache:
            cached_data, timestamp = self.cache[key]
            age = datetime.now() - timestamp
            
            if age < self.cache_ttl:
                print(f"✓ Cache hit (age: {age.total_seconds()/3600:.1f}h)")
                return cached_data, True  # True = from cache
        
        # Fetch from API
        try:
            result = query_perplexity(self.api_key, query)
            self.cache[key] = (result, datetime.now())
            return result, False  # False = fresh from API
        except Exception as e:
            # If API fails and we have stale cache, use it
            if key in self.cache:
                cached_data, timestamp = self.cache[key]
                age = datetime.now() - timestamp
                print(f"⚠️ API failed, using stale cache (age: {age.total_seconds()/3600:.1f}h)")
                return cached_data, True
            raise

4. Queue Non-Critical Searches

Defer non-urgent queries during outages:

import queue
import threading

class PerplexityQueueManager:
    """Queue searches during outages for later processing"""
    
    def __init__(self, api_key):
        self.api_key = api_key
        self.queue = queue.Queue()
        self.results = {}
        self.processing = False
    
    def search_or_queue(self, query_id, query, priority="normal"):
        """Try search, queue if service down"""
        try:
            result = query_perplexity(self.api_key, query)
            return result, "immediate"
        except Exception as e:
            print(f"Perplexity unavailable, queueing query {query_id}")
            self.queue.put({
                "id": query_id,
                "query": query,
                "priority": priority,
                "timestamp": time.time()
            })
            return None, "queued"
    
    def process_queue(self):
        """Background worker to process queued searches"""
        while not self.queue.empty():
            item = self.queue.get()
            try:
                result = query_perplexity(self.api_key, item["query"])
                self.results[item["id"]] = result
                print(f"✓ Processed queued query {item['id']}")
                time.sleep(1)  # Rate limit protection
            except Exception as e:
                print(f"Failed to process {item['id']}, requeueing...")
                self.queue.put(item)
                time.sleep(60)  # Wait before retry
    
    def start_background_processing(self):
        """Start background thread to process queue"""
        if not self.processing:
            self.processing = True
            thread = threading.Thread(target=self.process_queue, daemon=True)
            thread.start()

5. Monitor and Alert Proactively

Set up comprehensive health monitoring:

import requests
from datetime import datetime

class PerplexityHealthMonitor:
    """Continuous health monitoring with alerting"""
    
    def __init__(self, api_key, alert_webhook_url=None):
        self.api_key = api_key
        self.alert_webhook_url = alert_webhook_url
        self.failure_count = 0
        self.alert_threshold = 3
        self.last_alert_time = 0
    
    def health_check(self):
        """Perform health check and alert if needed"""
        try:
            start = time.time()
            query_perplexity(self.api_key, "test")
            latency = time.time() - start
            
            # Reset failure count on success
            if self.failure_count > 0:
                self._send_alert("✅ Perplexity service restored", "recovery")
                self.failure_count = 0
            
            # Warn on high latency
            if latency > 10:
                self._send_alert(f"⚠️ High latency detected: {latency:.2f}s", "degradation")
            
            return True, latency
            
        except Exception as e:
            self.failure_count += 1
            
            if self.failure_count >= self.alert_threshold:
                # Avoid alert spam
                if time.time() - self.last_alert_time > 1800:  # 30 min
                    self._send_alert(
                        f"🚨 Perplexity outage detected - {self.failure_count} consecutive failures",
                        "outage"
                    )
                    self.last_alert_time = time.time()
            
            return False, None
    
    def _send_alert(self, message, alert_type):
        """Send alert via webhook (Slack, Discord, etc.)"""
        if not self.alert_webhook_url:
            print(message)
            return
        
        payload = {
            "text": message,
            "timestamp": datetime.now().isoformat(),
            "type": alert_type,
            "service": "perplexity"
        }
        
        try:
            requests.post(self.alert_webhook_url, json=payload, timeout=5)
        except Exception as e:
            print(f"Failed to send alert: {e}")
    
    def continuous_monitoring(self, interval_seconds=60):
        """Run continuous health checks"""
        while True:
            healthy, latency = self.health_check()
            status = "✓" if healthy else "✗"
            latency_str = f"{latency:.2f}s" if latency else "N/A"
            print(f"{status} [{datetime.now().strftime('%H:%M:%S')}] Latency: {latency_str}")
            time.sleep(interval_seconds)

# Usage
monitor = PerplexityHealthMonitor(
    api_key="YOUR_API_KEY",
    alert_webhook_url="https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
)

# Run in background thread
threading.Thread(target=monitor.continuous_monitoring, daemon=True).start()

6. Communicate Transparently with Users

Status page component example:

def get_perplexity_status_message():
    """Generate user-facing status message"""
    # Check via API Status Check
    try:
        response = requests.get("https://apistatuscheck.com/api/perplexity", timeout=5)
        data = response.json()
        
        if data.get("status") == "operational":
            return {
                "status": "operational",
                "message": "All systems operational",
                "color": "green"
            }
        elif data.get("status") == "degraded":
            return {
                "status": "degraded",
                "message": "Experiencing slower than normal response times. We're monitoring the situation.",
                "color": "yellow"
            }
        else:
            return {
                "status": "outage",
                "message": "Perplexity AI is currently unavailable. Using fallback search providers. Estimated recovery: checking...",
                "color": "red",
                "alternatives": [
                    {"name": "OpenAI", "url": "https://apistatuscheck.com/api/openai"},
                    {"name": "Anthropic", "url": "https://apistatuscheck.com/api/anthropic"}
                ]
            }
    except:
        return {
            "status": "unknown",
            "message": "Unable to verify service status",
            "color": "gray"
        }

Frequently Asked Questions

How often does Perplexity go down?

Perplexity maintains strong uptime but as a rapidly growing AI service, occasional outages occur. Major incidents affecting all users are rare (2-4 times per year), though brief API timeouts or rate limiting issues may happen more frequently during peak usage. Most users experience minimal disruption, but monitoring is essential for production applications. Check apistatuscheck.com/api/perplexity for historical uptime data.

What's the difference between Perplexity web app and API outages?

The web interface (perplexity.ai) and API (api.perplexity.ai) run on shared infrastructure but can experience independent issues. Web app problems often relate to frontend CDN or authentication, while API issues typically involve backend search infrastructure. During outages, one may work while the other doesn't. API Status Check monitors both endpoints separately to distinguish between these scenarios.

Can I get refunded or credits for Perplexity API downtime?

Perplexity's API Terms of Service include uptime commitments, but specific SLA guarantees and refund policies vary by plan tier. Pro and Enterprise customers may have different guarantees than free tier users. Contact support@perplexity.ai with documentation of downtime impact to discuss credits for extended outages affecting your subscription.

Should I use Perplexity or alternative AI search APIs for production?

Perplexity excels at real-time web search with citations, making it ideal for research tools, fact-checking applications, and current event queries. For production systems, implement fallbacks to services like OpenAI with browsing or Anthropic Claude. The best approach uses Perplexity as primary for its strengths, with automatic failover during outages. Cost-benefit depends on your specific use case—Perplexity is often 5-10x cheaper than GPT-4 for search queries.

How do I prevent exceeding rate limits during recovery from outages?

When Perplexity recovers from an outage, resist the urge to immediately process your entire backlog. Implement gradual ramp-up: process queued queries at 50% of your normal rate for the first 30 minutes, monitoring for 429 errors. Use exponential backoff and respect rate limit headers. If processing urgent queries, implement priority queuing to handle critical searches first while deferring non-urgent ones.

What models does Perplexity offer and which are most reliable?

Perplexity offers several models with different tradeoffs:

• llama-3.1-sonar-small-128k-online - Fastest, most reliable, good for high-volume production
• llama-3.1-sonar-large-128k-online - Better quality, slightly slower, requires Pro
• llama-3.1-sonar-huge-128k-online - Highest quality, longest latency, Pro only

During degraded performance, smaller models typically remain more stable. Consider implementing automatic model downgrade during incidents (large → small) to maintain service availability.

Is there a status notification service specifically for Perplexity?

Yes, several options exist:

• Subscribe to real-time monitoring at apistatuscheck.com/api/perplexity with instant alerts via email, Slack, Discord, or webhook
• Follow @perplexity_ai on X (Twitter) for official announcements
• Join Perplexity's Discord community for real-time user reports
• Set up custom monitoring using the health check code examples in this guide

How does Perplexity compare to ChatGPT search for reliability?

Both services have excellent uptime, but different failure modes. ChatGPT search (via OpenAI) benefits from OpenAI's mature infrastructure and typically sees fewer outages. Perplexity's focused mission on search means faster iterations but occasionally higher instability. For mission-critical applications, monitor both services using API Status Check for OpenAI and Perplexity, and implement fallbacks between them. OpenAI costs more per query but may offer better SLAs for enterprise customers.

What should I do if Perplexity API works but citations are missing?

Missing citations while searches complete indicates the citation subsystem is degraded independently from core search. This happens because Perplexity's architecture separates answer generation from source attribution. Short-term solutions: (1) Continue using responses for low-stakes queries, (2) Switch to alternative providers for citation-critical use cases, (3) Implement citation validation in your code to detect and flag missing sources. Check if other users report similar issues on Discord or Reddit before filing a bug report.

Stay Ahead of Perplexity Outages

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Last updated: February 4, 2026. Perplexity status information is provided in real-time based on active monitoring. For official updates, follow @perplexity_ai on X (Twitter).