Is Qdrant Down? How to Check Qdrant Status in Real-Time

Is Qdrant Down? How to Check Qdrant Status in Real-Time

Quick Answer: To check if Qdrant is down, visit apistatuscheck.com/api/qdrant for real-time monitoring of Qdrant Cloud, or check the official status.qdrant.io page. Common signs include collection creation failures, upsert timeouts, search latency spikes, cluster synchronization errors, and memory pressure warnings.

When your vector search suddenly stops responding, every second of downtime impacts your AI applications. Qdrant powers semantic search, recommendation engines, and RAG (Retrieval-Augmented Generation) systems for thousands of AI companies worldwide. Whether you're seeing failed upserts, slow similarity searches, or cluster connection errors, knowing how to quickly verify Qdrant's status can save you critical troubleshooting time and help you maintain your production AI workflows.

How to Check Qdrant Status in Real-Time

1. API Status Check (Fastest Method)

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

  • Tests actual API endpoints every 60 seconds
  • Measures vector search latency and response times
  • Tracks historical uptime over 30/60/90 days
  • Provides instant alerts when issues are detected
  • Monitors cluster health across multiple regions

Unlike status pages that rely on manual updates, API Status Check performs active health checks against Qdrant's production endpoints, giving you the most accurate real-time picture of service availability.

2. Official Qdrant Status Page

Qdrant maintains status.qdrant.io as their official communication channel for Qdrant Cloud service incidents. The page displays:

  • Current operational status for all services
  • Active incidents and investigations
  • Scheduled maintenance windows
  • Historical incident reports
  • Component-specific status (API, Dashboard, Cluster Management)

Pro tip: Subscribe to status updates via email or webhook on the status page to receive immediate notifications when incidents occur.

3. Check Your Qdrant Dashboard

If the Qdrant Cloud Dashboard at cloud.qdrant.io is loading slowly or showing errors, this often indicates broader infrastructure issues. Pay attention to:

  • Login failures or timeouts
  • Collection list loading errors
  • Cluster status showing unavailable
  • API key management access issues
  • Metrics not updating

4. Test API Endpoints Directly (Python)

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

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
import time

def check_qdrant_health(url, api_key):
    """Quick health check for Qdrant cluster"""
    try:
        client = QdrantClient(url=url, api_key=api_key)
        
        # Test basic connectivity
        start = time.time()
        collections = client.get_collections()
        latency = (time.time() - start) * 1000
        
        print(f"✓ Connected successfully ({latency:.0f}ms)")
        print(f"✓ {len(collections.collections)} collections accessible")
        return True
        
    except Exception as e:
        print(f"✗ Health check failed: {e}")
        return False

# Example usage
check_qdrant_health(
    url="https://your-cluster.qdrant.io",
    api_key="your-api-key"
)

Look for connection timeouts, SSL/TLS errors, or 502/503/504 HTTP response codes indicating service unavailability.

5. Monitor Self-Hosted Instances

For self-hosted Qdrant deployments, use the built-in health endpoint:

# Health check endpoint
curl http://localhost:6333/health

# Expected response when healthy:
# {"title":"healthz","version":"1.7.0"}

# Cluster info
curl http://localhost:6333/cluster

# Metrics endpoint
curl http://localhost:6333/metrics

If these endpoints are unresponsive or returning errors, your Qdrant instance has issues that need immediate attention.

Common Qdrant Issues and How to Identify Them

Collection Creation Failures

Symptoms:

  • create_collection() hanging or timing out
  • "Collection already exists" errors despite UI showing no collection
  • Schema validation errors during collection creation
  • Memory allocation failures for large vector dimensions

Example error patterns:

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams

client = QdrantClient(url="https://your-cluster.qdrant.io", api_key="key")

try:
    client.create_collection(
        collection_name="products",
        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
    )
except Exception as e:
    # During outages, you might see:
    # - ConnectionError: Connection timeout
    # - 503 Service Unavailable
    # - 500 Internal Server Error
    print(f"Collection creation failed: {e}")

What it means: When collection creation fails consistently, it often indicates cluster resource exhaustion, storage issues, or API gateway problems affecting write operations.

Upsert Timeouts

Common scenarios:

  • Batch upserts timing out on large payloads
  • Single point upserts succeeding but batches failing
  • Intermittent success followed by sudden timeouts
  • Queue backlog messages in logs

Diagnostic code:

from qdrant_client.models import PointStruct
import time

def upsert_with_monitoring(client, collection_name, points):
    """Monitor upsert performance and catch timeout issues"""
    start = time.time()
    
    try:
        operation_info = client.upsert(
            collection_name=collection_name,
            points=points
        )
        
        elapsed = time.time() - start
        
        if elapsed > 5.0:  # Flag slow upserts
            print(f"⚠️  Slow upsert: {elapsed:.2f}s for {len(points)} points")
        
        return operation_info
        
    except Exception as e:
        elapsed = time.time() - start
        print(f"✗ Upsert failed after {elapsed:.2f}s: {e}")
        raise

# Example: batch upsert
points = [
    PointStruct(
        id=i,
        vector=[0.1] * 1536,
        payload={"text": f"document_{i}"}
    )
    for i in range(1000)
]

upsert_with_monitoring(client, "products", points)

Root causes during outages:

  • Cluster under heavy load
  • Storage I/O bottleneck
  • Memory pressure causing swap usage
  • Network connectivity issues between cluster nodes

Search Latency Spikes

Indicators:

  • Search queries taking seconds instead of milliseconds
  • p99 latency jumping from <50ms to >1000ms
  • Inconsistent search performance across identical queries
  • Timeout errors during peak usage

Performance monitoring:

import time
import statistics

def benchmark_search_latency(client, collection_name, query_vector, iterations=10):
    """Measure search latency to detect degradation"""
    latencies = []
    
    for i in range(iterations):
        start = time.time()
        
        try:
            results = client.search(
                collection_name=collection_name,
                query_vector=query_vector,
                limit=10
            )
            
            latency = (time.time() - start) * 1000
            latencies.append(latency)
            
        except Exception as e:
            print(f"Search {i+1} failed: {e}")
            return None
    
    avg_latency = statistics.mean(latencies)
    p99_latency = sorted(latencies)[int(len(latencies) * 0.99)]
    
    print(f"Average latency: {avg_latency:.0f}ms")
    print(f"P99 latency: {p99_latency:.0f}ms")
    
    if avg_latency > 200:
        print("⚠️  WARNING: Latency degraded significantly!")
    
    return latencies

# Test with your embedding
query = [0.1] * 1536  # Replace with actual embedding
benchmark_search_latency(client, "products", query)

What slow searches indicate:

  • Index corruption or rebuilding in progress
  • Cluster node failures requiring rebalancing
  • Resource contention (CPU/memory exhausted)
  • Cold start after cluster restart

Cluster Synchronization Issues

Symptoms in distributed deployments:

  • "Peer not responding" errors in logs
  • Inconsistent search results across queries
  • Split-brain scenarios in multi-node clusters
  • Raft consensus failures

Checking cluster health:

def check_cluster_health(client):
    """Verify cluster synchronization status"""
    try:
        cluster_info = client.get_cluster_info()
        
        print(f"Cluster status: {cluster_info.status}")
        print(f"Peer count: {len(cluster_info.peers)}")
        
        for peer in cluster_info.peers:
            print(f"  - Peer {peer.id}: {peer.state}")
            
        # Check for unhealthy peers
        unhealthy = [p for p in cluster_info.peers if p.state != "Alive"]
        
        if unhealthy:
            print(f"⚠️  {len(unhealthy)} unhealthy peers detected!")
            return False
            
        return True
        
    except Exception as e:
        print(f"✗ Cannot retrieve cluster info: {e}")
        return False

check_cluster_health(client)

Cluster issues often manifest as:

  • Write operations succeeding but not replicated
  • Stale data returned from follower nodes
  • Leader election failures
  • Network partition between nodes

Memory Pressure

Signs of memory exhaustion:

  • Out of memory (OOM) errors during upserts
  • Slow performance followed by crashes
  • Automatic restarts/pod evictions in Kubernetes
  • "Cannot allocate memory" errors

Memory monitoring code:

def check_memory_usage(client):
    """Monitor Qdrant memory consumption"""
    try:
        collections = client.get_collections().collections
        
        total_points = 0
        for collection in collections:
            info = client.get_collection(collection.name)
            points_count = info.points_count
            vector_size = info.config.params.vectors.size
            
            # Rough memory estimate (bytes per point)
            memory_per_point = vector_size * 4 + 1024  # 4 bytes per float32 + payload overhead
            estimated_memory = points_count * memory_per_point / (1024**3)  # GB
            
            print(f"Collection: {collection.name}")
            print(f"  Points: {points_count:,}")
            print(f"  Estimated memory: {estimated_memory:.2f} GB")
            
            total_points += points_count
        
        print(f"\nTotal points across all collections: {total_points:,}")
        
    except Exception as e:
        print(f"Memory check failed: {e}")

check_memory_usage(client)

Memory pressure causes:

  • Too many vectors for available RAM
  • Large payload sizes per vector
  • Insufficient index optimization
  • Memory leaks in long-running instances

The Real Impact When Qdrant Goes Down

RAG System Failures

Retrieval-Augmented Generation systems depend critically on vector search:

  • Chatbots return generic responses without context retrieval
  • AI assistants lose access to knowledge bases
  • Question-answering systems fail to find relevant documents
  • Context window optimization becomes impossible

Example impact: A customer support chatbot using RAG with Qdrant suddenly loses access to your entire support documentation, product manuals, and historical ticket context. Support quality plummets from 95% accuracy to basic scripted responses.

Recommendation Engine Downtime

E-commerce and content platforms rely on vector similarity for personalization:

  • Product recommendations disappear or fall back to popularity-based rules
  • "Similar items" features break entirely
  • Personalized content feeds become generic
  • User engagement drops 30-50% during outages

Revenue impact: A large e-commerce platform processing $100K/hour may see 15-25% conversion rate drop when personalized recommendations fail, translating to $15-25K hourly revenue loss.

Semantic Search Degradation

Modern search experiences depend on vector-based semantic understanding:

  • Search falls back to keyword matching (significantly worse results)
  • Natural language queries fail completely
  • Cross-lingual search breaks
  • Image/audio search becomes unavailable

User experience: Users searching for "comfortable running shoes for marathon training" get keyword matches for "comfortable shoes" instead of semantically relevant endurance running products.

AI Application Pipeline Failures

Production AI workflows often chain multiple operations:

# Typical RAG pipeline that breaks during Qdrant outage
def rag_pipeline(user_query):
    # 1. Generate query embedding (works)
    embedding = openai_embed(user_query)
    
    # 2. Search Qdrant for relevant context (FAILS HERE)
    context = qdrant_search(embedding)  # ← Timeout/error
    
    # 3. Generate response with context (never reached)
    response = llm_generate(user_query, context)
    
    return response

Cascading failures:

  • Entire pipeline halts at vector search step
  • Request queues build up
  • Upstream services time out waiting for responses
  • User-facing applications show errors or hang

Model Training and Evaluation Delays

ML teams using Qdrant for similarity search during training:

  • Cannot retrieve negative examples for contrastive learning
  • Evaluation datasets become inaccessible
  • Hyperparameter tuning experiments fail mid-run
  • Model versioning and comparison breaks

Development impact: A 4-hour Qdrant outage can delay model training experiments by days if checkpoints are lost or experiments must be restarted from scratch.

Duplicate Detection Failures

Systems using vector embeddings for deduplication:

  • Duplicate content passes through undetected
  • Data quality degrades in databases
  • Compliance issues (duplicate user records, redundant documents)
  • Storage costs increase

Example: A document ingestion pipeline that normally deduplicates 30% of incoming content processes all documents during an outage, tripling storage costs and creating data quality issues.

Incident Response Playbook for Qdrant Outages

1. Implement Intelligent Retry Logic

Exponential backoff with circuit breaker:

import time
from functools import wraps
from datetime import datetime, timedelta

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.last_failure_time = None
        self.state = "closed"  # closed, open, half-open
    
    def call(self, func, *args, **kwargs):
        if self.state == "open":
            if datetime.now() - self.last_failure_time > timedelta(seconds=self.timeout):
                self.state = "half-open"
            else:
                raise Exception("Circuit breaker is OPEN - Qdrant likely down")
        
        try:
            result = func(*args, **kwargs)
            if self.state == "half-open":
                self.state = "closed"
                self.failure_count = 0
            return result
        
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = datetime.now()
            
            if self.failure_count >= self.failure_threshold:
                self.state = "open"
                print(f"⚠️  Circuit breaker opened after {self.failure_count} failures")
            
            raise

# Usage
breaker = CircuitBreaker(failure_threshold=3, timeout=60)

def search_with_circuit_breaker(query_vector):
    return breaker.call(
        client.search,
        collection_name="products",
        query_vector=query_vector,
        limit=10
    )

This prevents overwhelming a struggling Qdrant cluster with retry storms while gracefully degrading.

2. Implement Fallback Search Strategies

Multi-tier fallback approach:

from typing import List, Optional

class ResilientVectorSearch:
    def __init__(self, primary_client, fallback_cache):
        self.primary = primary_client
        self.cache = fallback_cache
        self.fallback_mode = False
    
    def search(self, collection: str, query: List[float], limit: int = 10):
        """Search with automatic fallback to cached results"""
        
        # Try primary Qdrant cluster
        try:
            results = self.primary.search(
                collection_name=collection,
                query_vector=query,
                limit=limit,
                timeout=5  # Fail fast
            )
            
            # Cache successful results
            cache_key = f"{collection}:{hash(tuple(query))}"
            self.cache.set(cache_key, results, ttl=3600)
            
            if self.fallback_mode:
                print("✓ Qdrant recovered - back to normal operation")
                self.fallback_mode = False
            
            return results
            
        except Exception as e:
            print(f"⚠️  Primary search failed: {e}")
            
            if not self.fallback_mode:
                print("⚠️  Entering fallback mode")
                self.fallback_mode = True
            
            # Try cache
            cache_key = f"{collection}:{hash(tuple(query))}"
            cached = self.cache.get(cache_key)
            
            if cached:
                print("✓ Serving cached results")
                return cached
            
            # Last resort: return empty with error
            print("✗ No cached results available")
            return []

# Implementation with Redis cache
import redis

cache = redis.Redis(host='localhost', port=6379, db=0)
resilient_search = ResilientVectorSearch(client, cache)

results = resilient_search.search("products", query_embedding, limit=10)

3. Queue Operations for Batch Processing

Defer non-critical upserts during outages:

import json
from datetime import datetime

class QdrantOperationQueue:
    def __init__(self, queue_file="qdrant_queue.jsonl"):
        self.queue_file = queue_file
    
    def queue_upsert(self, collection: str, points: List[dict]):
        """Queue upsert operations for later processing"""
        operation = {
            "timestamp": datetime.now().isoformat(),
            "operation": "upsert",
            "collection": collection,
            "points": points
        }
        
        with open(self.queue_file, 'a') as f:
            f.write(json.dumps(operation) + '\n')
        
        print(f"✓ Queued upsert of {len(points)} points to {collection}")
    
    def process_queue(self, client):
        """Process queued operations when Qdrant is back online"""
        if not os.path.exists(self.queue_file):
            return
        
        processed = 0
        failed = 0
        
        with open(self.queue_file, 'r') as f:
            for line in f:
                operation = json.loads(line)
                
                try:
                    if operation["operation"] == "upsert":
                        client.upsert(
                            collection_name=operation["collection"],
                            points=operation["points"]
                        )
                    processed += 1
                    
                except Exception as e:
                    print(f"Failed to process operation: {e}")
                    failed += 1
        
        if failed == 0:
            os.remove(self.queue_file)
            print(f"✓ Processed {processed} queued operations")
        else:
            print(f"⚠️  Processed {processed}, failed {failed}")

# Usage
queue = QdrantOperationQueue()

try:
    client.upsert(collection_name="products", points=new_points)
except Exception:
    print("⚠️  Qdrant unavailable, queuing for later")
    queue.queue_upsert("products", new_points)

# Later, when Qdrant recovers
queue.process_queue(client)

4. Monitor Proactively with Alerts

Comprehensive monitoring setup:

import requests
from datetime import datetime

def monitor_qdrant_health(client, alert_webhook):
    """Continuous health monitoring with alerting"""
    
    health_checks = {
        "connectivity": False,
        "latency": None,
        "cluster": False,
        "memory": None
    }
    
    # Check 1: Basic connectivity
    try:
        start = time.time()
        collections = client.get_collections()
        latency = (time.time() - start) * 1000
        
        health_checks["connectivity"] = True
        health_checks["latency"] = latency
        
        if latency > 1000:
            send_alert(alert_webhook, f"⚠️  Qdrant latency degraded: {latency:.0f}ms")
    
    except Exception as e:
        send_alert(alert_webhook, f"🚨 Qdrant DOWN: {e}")
        return health_checks
    
    # Check 2: Cluster health
    try:
        cluster = client.get_cluster_info()
        unhealthy_peers = [p for p in cluster.peers if p.state != "Alive"]
        
        if not unhealthy_peers:
            health_checks["cluster"] = True
        else:
            send_alert(alert_webhook, f"⚠️  {len(unhealthy_peers)} unhealthy cluster peers")
    
    except Exception as e:
        print(f"Cluster check failed: {e}")
    
    return health_checks

def send_alert(webhook_url, message):
    """Send alert to monitoring service"""
    payload = {
        "text": message,
        "timestamp": datetime.now().isoformat()
    }
    requests.post(webhook_url, json=payload)

# Run monitoring every 60 seconds
import schedule

schedule.every(60).seconds.do(
    monitor_qdrant_health,
    client=client,
    alert_webhook="https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
)

Subscribe to external monitoring:

  • API Status Check alerts - automated Qdrant Cloud monitoring
  • Qdrant status page notifications
  • Set up synthetic monitoring with your own health checks

5. Consider Multi-Cloud Vector Database Strategy

For mission-critical applications, implement redundancy:

from typing import List, Union
from qdrant_client import QdrantClient

class MultiVectorStore:
    """Manage multiple vector databases with automatic failover"""
    
    def __init__(self):
        self.stores = {
            "qdrant": QdrantClient(url="https://qdrant.example.com", api_key="key1"),
            "pinecone": initialize_pinecone(),  # Backup option
            "weaviate": initialize_weaviate(),  # Second backup
        }
        self.primary = "qdrant"
    
    def search(self, collection: str, query: List[float], limit: int = 10):
        """Try primary, failover to backups"""
        
        for store_name, store_client in self.stores.items():
            try:
                if store_name == "qdrant":
                    return store_client.search(
                        collection_name=collection,
                        query_vector=query,
                        limit=limit
                    )
                elif store_name == "pinecone":
                    # Pinecone-specific search
                    return pinecone_search(store_client, collection, query, limit)
                # ... other implementations
                
            except Exception as e:
                print(f"{store_name} failed: {e}, trying next...")
                continue
        
        raise Exception("All vector stores unavailable")

# Usage
multi_store = MultiVectorStore()
results = multi_store.search("products", embedding, limit=10)

Cross-posting strategy: Write to primary Qdrant + backup asynchronously. Read from primary only unless it fails. See also: Is Pinecone Down? and Is Weaviate Down? for alternative vector database monitoring.

6. Post-Outage Recovery Actions

Systematic recovery checklist:

  1. Verify cluster health fully restored:

    # Comprehensive health check
assert client.get_collections()  # Connectivity
assert check_cluster_health(client)  # All nodes healthy

  2. Process queued operations:

    queue.process_queue(client)  # Replay queued upserts

  3. Validate data integrity:

    # Check collection counts match expected
info = client.get_collection("products")
expected_count = get_expected_count_from_source()

if info.points_count != expected_count:
    print(f"⚠️  Data inconsistency: {info.points_count} vs {expected_count} expected")

  4. Benchmark performance:

    # Ensure latency is back to normal
latencies = benchmark_search_latency(client, "products", test_query)
assert statistics.mean(latencies) < 100, "Latency still degraded"

  5. Review incident timeline and update runbooks

  6. Optimize for future resilience based on lessons learned

Frequently Asked Questions

How often does Qdrant go down?

Qdrant Cloud maintains strong uptime, typically exceeding 99.9% availability. Major outages affecting all users are rare (1-2 times per year), though self-hosted deployments may experience issues based on infrastructure setup. Most production users experience minimal downtime from Qdrant Cloud services.

What's the difference between Qdrant Cloud and self-hosted reliability?

Qdrant Cloud is managed by the Qdrant team with professional SRE support, automated failover, and geographic redundancy. Self-hosted Qdrant reliability depends entirely on your infrastructure—factors like Kubernetes cluster health, storage reliability, network configuration, and monitoring setup. Cloud typically offers better uptime but less control; self-hosted offers full control but requires more operational expertise.

Should I use Qdrant Cloud or self-host for production?

Choose Qdrant Cloud if:

  • You want managed infrastructure and automatic scaling
  • Your team lacks deep vector database operations experience
  • You need geographic distribution without complex setup
  • Fast deployment is critical

Choose self-hosted if:

  • Data sovereignty/compliance requires on-premise hosting
  • You have specific performance tuning requirements
  • Cost optimization at massive scale matters
  • You have experienced DevOps/SRE team

Many companies start with Cloud and migrate to hybrid or self-hosted as they scale.

How does Qdrant compare to Pinecone and Weaviate for reliability?

All three are enterprise-grade vector databases with strong uptime:

  • Qdrant: Open-source with managed cloud option, excellent for self-hosted control. See Is Qdrant Down? for live monitoring.
  • Pinecone: Fully managed cloud-only, known for simplicity but less flexibility. Monitor at Is Pinecone Down?.
  • Weaviate: Open-source with strong GraphQL support, good for complex data models. Check Is Weaviate Down?.

Choice depends on your specific needs (cloud vs. self-hosted, query features, pricing, language support).

Can Qdrant handle RAG applications at scale?

Yes, Qdrant is specifically designed for production RAG systems and is used by major AI companies. It supports:

  • Billions of vectors with horizontal scaling
  • Sub-50ms search latency at scale
  • Filtered search for metadata-based retrieval
  • Payload storage for complete document context
  • Multi-vector configurations for hybrid search

For RAG reliability: Implement the circuit breaker and caching patterns shown above, monitor Qdrant status proactively, and consider fallback strategies for mission-critical systems. Also monitor your LLM provider: Is Cohere Down? if using Cohere embeddings.

What causes memory pressure in Qdrant?

Common causes of memory exhaustion:

  1. Too many vectors for available RAM: Qdrant keeps vectors in memory for fast search
  2. High-dimensional embeddings: 1536-dim vs 768-dim doubles memory per vector
  3. Large payloads: Storing full documents vs. IDs and fetching separately
  4. Insufficient segment optimization: Unoptimized indexes consume more memory
  5. Memory leaks: Rare but possible in long-running instances

Solutions:

  • Scale vertically (more RAM) or horizontally (more nodes)
  • Use quantization to reduce vector memory footprint
  • Store minimal payloads, fetch full content from primary database
  • Regularly optimize indexes: client.update_collection(..., optimizer_config=...)

How do I prevent duplicate vectors during Qdrant recovery?

Use idempotent upserts with consistent IDs:

from qdrant_client.models import PointStruct

# Good: deterministic IDs based on content
point = PointStruct(
    id=hash(document_id),  # Consistent ID
    vector=embedding,
    payload={"doc_id": document_id}
)

client.upsert(collection_name="docs", points=[point])

# During recovery, re-upserting the same point updates it rather than duplicating

Qdrant's upsert operation is idempotent—upserting the same ID multiple times updates the vector rather than creating duplicates. This makes retry logic safe.

What monitoring should I set up for production Qdrant?

Essential monitoring:

  1. Uptime monitoring: API Status Check for Qdrant - 60-second health checks
  2. Latency tracking: P50, P95, P99 search latency
  3. Error rate: Failed operations / total operations ratio
  4. Resource utilization: Memory, CPU, disk I/O
  5. Cluster health: Node status, replication lag
  6. Queue depth: Pending operations backlog

Alerting thresholds:

  • Search latency >200ms (p95)
  • Error rate >1%
  • Memory usage >85%
  • Any cluster node unhealthy
  • API endpoint unreachable for >60 seconds

Set up alerts via email, Slack, PagerDuty, or webhook from API Status Check.

Does Qdrant support multi-region deployments?

Yes, Qdrant Cloud offers multi-region clusters for geographic distribution. For self-hosted deployments, you can deploy separate Qdrant clusters per region and implement application-level routing:

class GeoDistributedQdrant:
    def __init__(self):
        self.clusters = {
            "us-east": QdrantClient(url="https://us-east.qdrant.io", api_key="key"),
            "eu-west": QdrantClient(url="https://eu-west.qdrant.io", api_key="key"),
            "ap-south": QdrantClient(url="https://ap-south.qdrant.io", api_key="key"),
        }
    
    def search(self, region: str, collection: str, query: List[float], limit: int):
        """Route search to nearest region"""
        client = self.clusters.get(region, self.clusters["us-east"])
        return client.search(
            collection_name=collection,
            query_vector=query,
            limit=limit
        )

This reduces latency for global users and provides regional redundancy if one region experiences issues.

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Last updated: February 4, 2026. Qdrant status information is provided in real-time based on active monitoring. For official incident reports, refer to status.qdrant.io.

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