Is Pinecone Down? How to Check Pinecone Status in Real-Time
Is Pinecone Down? How to Check Pinecone Status in Real-Time
Quick Answer: To check if Pinecone is down, visit apistatuscheck.com/api/pinecone for real-time monitoring, or check the official status.pinecone.io page. Common signs include index creation failures, upsert timeouts, query latency spikes, rate limiting errors, and namespace operation failures.
When your AI-powered search suddenly stops returning results or your RAG application starts timing out, every second of diagnosis matters. Pinecone is the leading vector database powering millions of AI applications—from semantic search engines to recommendation systems and retrieval-augmented generation (RAG) pipelines. Any disruption to Pinecone can cascade through your entire AI infrastructure, breaking user experiences and halting critical ML workflows. This comprehensive guide shows you exactly how to verify Pinecone's status, identify common issues, and respond effectively to minimize business impact.
How to Check Pinecone Status in Real-Time
1. API Status Check (Fastest Method)
The most reliable way to verify Pinecone's operational status is through apistatuscheck.com/api/pinecone. This real-time monitoring service:
- Tests actual vector operations every 60 seconds (describe_index, query, stats)
- Measures query latency and P95/P99 response times
- Tracks regional availability across AWS, GCP, and Azure deployments
- Monitors both control plane and data plane operations
- Provides instant alerts when degradation is detected
- Shows historical uptime over 30/60/90 day periods
Unlike status pages that require manual updates, API Status Check performs active health checks against Pinecone's production infrastructure, testing the same endpoints your AI applications depend on. This gives you the earliest possible warning when issues emerge—often before official incident reports.
2. Official Pinecone Status Page
Pinecone maintains status.pinecone.io as their primary communication channel for service incidents. The page displays:
- Real-time operational status for all services
- Active incidents under investigation
- Scheduled maintenance announcements
- Component-level status (API, Control Plane, Data Plane, Dashboard)
- Regional availability (US-East, US-West, EU-West, Asia-Pacific)
- Historical incident timeline with post-mortems
Pro tip: Subscribe to status updates via email, SMS, Slack, or webhook at the bottom of the status page. During incidents, Pinecone provides regular updates on investigation progress and estimated time to resolution.
3. Test Your Index Health
The Pinecone Dashboard at app.pinecone.io provides visual indicators of index health:
- Index loading status and configuration
- Recent operation metrics (upserts, queries, deletes)
- Pod utilization and resource consumption
- Error rate graphs
- Query performance charts
If the dashboard shows elevated error rates, unusual latency patterns, or fails to load index statistics, this often signals broader infrastructure issues.
4. Direct API Health Check
For developers, making test API calls provides immediate diagnostic information:
import pinecone
from pinecone import Pinecone, ServerlessSpec
import time
# Initialize Pinecone client
pc = Pinecone(api_key="your-api-key")
try:
# Test 1: Control plane health (list indexes)
start = time.time()
indexes = pc.list_indexes()
control_latency = time.time() - start
print(f"✓ Control plane healthy ({control_latency:.2f}s)")
# Test 2: Data plane health (connect to index)
index = pc.Index("your-index-name")
# Test 3: Stats operation
start = time.time()
stats = index.describe_index_stats()
stats_latency = time.time() - start
print(f"✓ Stats operation successful ({stats_latency:.2f}s)")
# Test 4: Query operation
start = time.time()
results = index.query(
vector=[0.1] * 1536, # Match your dimension
top_k=10,
include_metadata=True
)
query_latency = time.time() - start
print(f"✓ Query successful ({query_latency:.2f}s)")
# Alert if latency is abnormal
if query_latency > 1.0:
print(f"⚠ High query latency detected: {query_latency:.2f}s")
except Exception as e:
print(f"✗ Pinecone health check failed: {str(e)}")
Expected results:
- Control plane operations: < 500ms
- Stats operations: < 200ms
- Query operations: < 100ms (serverless) or < 50ms (pod-based)
Significantly elevated latencies or timeout errors indicate service degradation.
5. Community Monitoring
The AI and ML community actively reports Pinecone issues:
- Twitter/X: Search for "pinecone down" or "pinecone outage"
- Reddit: r/MachineLearning and r/LangChain threads
- Discord: LangChain, LlamaIndex, and AI builder communities
- GitHub Issues: Check pinecone-io repositories
If multiple developers are reporting similar issues simultaneously, especially across different regions or index types, it's likely a platform-wide incident rather than an application-specific problem.
Common Pinecone Issues and How to Identify Them
Index Creation Failures
Symptoms:
create_index()calls hang or timeout- "Failed to provision resources" error messages
- Indexes stuck in "Initializing" state for extended periods
- Pod capacity unavailable errors
Example error:
pinecone.exceptions.PineconeException: (503)
Reason: Service Unavailable
HTTP response body: {"error": "Failed to provision index resources. Please retry."}
What it means: Index creation failures typically indicate control plane issues or resource capacity constraints in specific regions. During high-demand periods or infrastructure problems, new index provisioning can be delayed or fail entirely.
Diagnosis:
import time
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key="your-api-key")
try:
# Attempt index creation
pc.create_index(
name="test-health-check",
dimension=1536,
metric="cosine",
spec=ServerlessSpec(
cloud="aws",
region="us-east-1"
)
)
# Poll for readiness
max_wait = 300 # 5 minutes
start = time.time()
while time.time() - start < max_wait:
index_desc = pc.describe_index("test-health-check")
if index_desc.status.ready:
print("✓ Index creation successful")
pc.delete_index("test-health-check")
break
time.sleep(10)
else:
print("✗ Index creation timeout - possible control plane issue")
except Exception as e:
print(f"✗ Index creation failed: {str(e)}")
Upsert Timeouts
Symptoms:
upsert()operations taking > 5 seconds consistently- "Request timeout" errors during vector insertion
- Batch upserts failing midway through processing
- Increased 504 Gateway Timeout responses
Common error patterns:
pinecone.core.client.exceptions.ServiceException: (504)
Reason: Gateway Timeout
What it means: Upsert timeouts indicate data plane degradation—the infrastructure handling vector writes is overwhelmed or experiencing issues. This is often one of the first symptoms during partial outages because upsert operations are write-heavy and more sensitive to backend performance.
Diagnostic code:
import time
import statistics
def diagnose_upsert_performance(index, sample_size=10):
"""Test upsert latency to detect degradation"""
latencies = []
failures = 0
for i in range(sample_size):
vectors = [(
f"test-{i}-{j}",
[0.1] * 1536,
{"test": True}
) for j in range(100)] # 100 vectors per batch
try:
start = time.time()
index.upsert(vectors=vectors)
latency = time.time() - start
latencies.append(latency)
print(f"Batch {i+1}: {latency:.2f}s")
except Exception as e:
failures += 1
print(f"Batch {i+1}: FAILED - {str(e)}")
if latencies:
avg_latency = statistics.mean(latencies)
p95_latency = statistics.quantiles(latencies, n=20)[18] if len(latencies) > 5 else max(latencies)
print(f"\nResults:")
print(f"Average latency: {avg_latency:.2f}s")
print(f"P95 latency: {p95_latency:.2f}s")
print(f"Failure rate: {failures}/{sample_size}")
if avg_latency > 2.0 or failures > 0:
print("⚠ DEGRADED: Upsert performance below normal")
else:
print("✓ HEALTHY: Upsert performance normal")
Query Latency Spikes
Symptoms:
- Query operations taking 2-10x longer than baseline
- Inconsistent response times (some fast, some slow)
- P99 latency elevated significantly
- User-facing search becoming noticeably sluggish
What it means: Query latency spikes often indicate:
- Backend pod resource saturation
- Network congestion between regions
- Index compaction or maintenance operations
- Underlying cloud provider issues (AWS, GCP, Azure)
Impact on applications:
# Normal query latency: 50-100ms
# During degradation: 500-2000ms or timeouts
from pinecone import Pinecone
import time
pc = Pinecone(api_key="your-api-key")
index = pc.Index("your-index")
# Simulate RAG application query
user_question = "What is semantic search?"
query_embedding = get_embedding(user_question) # From OpenAI, Cohere, etc.
start = time.time()
try:
results = index.query(
vector=query_embedding,
top_k=5,
include_metadata=True,
filter={"category": "documentation"}
)
latency = time.time() - start
if latency > 0.5: # 500ms threshold
print(f"⚠ High query latency: {latency:.2f}s")
# Implement fallback or caching
results = get_cached_results(user_question)
except Exception as e:
print(f"✗ Query failed: {str(e)}")
# Fallback to basic search or error handling
For RAG applications, query latency directly impacts user experience. If your typical end-to-end latency is 2 seconds (embedding generation + vector search + LLM generation), a 1-second increase in Pinecone query time means 50% slower responses to users.
Rate Limiting Errors
Symptoms:
- 429 "Too Many Requests" HTTP responses
- "Rate limit exceeded" error messages
- Operations succeeding intermittently with backoff
- Lower throughput than your plan's specified limits
Common scenarios:
pinecone.core.client.exceptions.ServiceException: (429)
Reason: Too Many Requests
HTTP response body: {"error": "Rate limit exceeded. Retry after 60 seconds."}
What it means: While rate limits are documented and expected, unexpected 429 errors during normal operation can indicate:
- Pinecone applying temporary throttling during incidents
- Incorrectly calculated rate limits on Pinecone's side
- Your application accidentally creating request spikes
- Shared infrastructure resource contention
Handling rate limits gracefully:
import time
from pinecone.core.client.exceptions import ServiceException
def upsert_with_retry(index, vectors, max_retries=3):
"""Robust upsert with exponential backoff for rate limits"""
for attempt in range(max_retries):
try:
return index.upsert(vectors=vectors)
except ServiceException as e:
if e.status == 429:
# Extract retry-after header if available
retry_after = int(e.headers.get('Retry-After', 2 ** attempt))
print(f"Rate limited. Retrying after {retry_after}s...")
time.sleep(retry_after)
else:
raise # Not a rate limit error
raise Exception(f"Failed after {max_retries} retries due to rate limiting")
# Usage
vectors = generate_embeddings(documents)
upsert_with_retry(index, vectors)
Namespace Operation Errors
Symptoms:
- Namespace creation or deletion hanging
- Queries returning no results despite data existing in namespace
- "Namespace not found" errors for recently created namespaces
- Inconsistent namespace listing (showing different results on repeated calls)
Example issue:
# Create namespace and immediately query
index.upsert(vectors=vectors, namespace="new-namespace")
# This might fail during Pinecone issues
results = index.query(
vector=query_vec,
top_k=10,
namespace="new-namespace"
)
# Returns empty results even though upsert succeeded
What it means: Namespace operations rely on metadata consistency across Pinecone's distributed infrastructure. During partial outages or network partitions, namespace metadata can become temporarily inconsistent, leading to namespace visibility issues.
Robust namespace handling:
import time
def ensure_namespace_ready(index, namespace, max_wait=60):
"""Wait for namespace to be consistently visible"""
start = time.time()
while time.time() - start < max_wait:
try:
stats = index.describe_index_stats()
if namespace in stats.namespaces:
print(f"✓ Namespace '{namespace}' is ready")
return True
time.sleep(2)
except Exception as e:
print(f"Waiting for namespace... ({e})")
time.sleep(2)
print(f"✗ Namespace '{namespace}' not ready after {max_wait}s")
return False
# Usage
index.upsert(vectors=vectors, namespace="user-123")
ensure_namespace_ready(index, "user-123")
# Now safe to query
The Real Impact When Pinecone Goes Down
Broken AI Applications
Pinecone sits at the critical path of modern AI infrastructure. When it goes down, the impact cascades immediately:
RAG (Retrieval-Augmented Generation) systems:
- Chatbots can't retrieve relevant context → generic, unhelpful responses
- Documentation assistants fail → users can't find answers
- Customer support AI degrades → ticket backlog increases
- Code assistants lose access to codebase knowledge
Example failure:
# Typical RAG pipeline
def answer_question(question: str):
# Step 1: Get embedding (still works)
embedding = openai.Embedding.create(input=question)
# Step 2: Search Pinecone (FAILS during outage)
context = pinecone_index.query(vector=embedding) # ❌ Timeout
# Step 3: Generate answer with LLM
# Without context, answer is generic and often wrong
answer = openai.ChatCompletion.create(
messages=[{"role": "user", "content": question}]
# Missing: context from Pinecone
)
return answer # Poor quality without retrieval
Without Pinecone, your RAG application essentially becomes a basic LLM—losing the domain-specific knowledge and accuracy that makes it valuable.
Semantic Search Downtime
E-commerce, media, and content platforms rely on Pinecone for semantic search:
- Product discovery: Users can't find products using natural language
- Content recommendations: Personalization engine fails
- Image search: Visual similarity searches return errors
- Music/video platforms: "Similar items" features break
Business metrics impact:
- 30-50% drop in search conversion rates
- Increased bounce rates as users can't find content
- Support ticket spike ("search not working")
- Revenue loss for e-commerce platforms
ML Pipeline Failures
Data scientists and ML engineers depend on Pinecone for development workflows:
- Model evaluation: Can't query test sets for similarity analysis
- Dataset deduplication: Duplicate detection pipelines halt
- Feature stores: Vector feature retrieval fails
- Active learning: Can't identify uncertain examples for labeling
Example production impact:
# Nightly model evaluation pipeline
def evaluate_new_model():
# Load test queries
test_queries = load_queries()
# Retrieve ground truth from Pinecone
for query in test_queries:
embedding = model.encode(query)
# ❌ This fails during Pinecone outage
ground_truth = pinecone_index.query(
vector=embedding,
top_k=100,
namespace="ground-truth"
)
# Can't compute metrics without ground truth
precision = compute_precision(predictions, ground_truth)
A 2-hour Pinecone outage during your nightly model evaluation window means delayed model deployments and potentially missing SLA commitments.
Recommendation System Degradation
Modern recommendation engines use vector similarity at their core:
- Content platforms: "More like this" features fail
- E-learning: Course recommendations disappear
- Job boards: Candidate-job matching breaks
- Social media: Feed personalization degrades to chronological
Quantified impact:
- 40-60% reduction in click-through rates
- Lower user engagement metrics
- Decreased session duration
- Reduced revenue per user
Embedding Pipeline Backlog
Many applications continuously generate and upsert embeddings:
- Document processing: New documents can't be indexed
- User-generated content: Comments, posts can't be made searchable immediately
- Real-time updates: Inventory changes, price updates don't reflect in search
- Sync operations: Failed upserts create data inconsistency
Queue buildup example:
# Message queue processing embeddings
@celery.task
def process_document(doc_id):
# Generate embedding (still works)
text = fetch_document(doc_id)
embedding = generate_embedding(text)
# Upsert to Pinecone (FAILS during outage)
try:
pinecone_index.upsert([(doc_id, embedding, metadata)])
except Exception:
# Task fails and re-queues
# Queue grows while Pinecone is down
raise # Retry later
After a 3-hour outage, you might have 50,000+ failed upsert tasks in your queue, creating a recovery backlog that takes hours to process even after Pinecone returns to service.
Incident Response Playbook: What to Do When Pinecone Goes Down
1. Implement Circuit Breaker Pattern
Prevent cascading failures by detecting and handling Pinecone outages gracefully:
from datetime import datetime, timedelta
import threading
class PineconeCircuitBreaker:
"""Prevent repeated calls to failing Pinecone service"""
def __init__(self, failure_threshold=5, timeout=60):
self.failure_count = 0
self.failure_threshold = failure_threshold
self.timeout = timeout # seconds
self.last_failure_time = None
self.state = "CLOSED" # CLOSED, OPEN, HALF_OPEN
self.lock = threading.Lock()
def call(self, func, *args, **kwargs):
with self.lock:
# If circuit is OPEN, check if timeout expired
if self.state == "OPEN":
if datetime.now() - self.last_failure_time > timedelta(seconds=self.timeout):
self.state = "HALF_OPEN"
print("Circuit breaker: Attempting recovery...")
else:
raise Exception("Circuit breaker OPEN - Pinecone unavailable")
try:
result = func(*args, **kwargs)
# Success - reset circuit breaker
with self.lock:
if self.state == "HALF_OPEN":
print("Circuit breaker: Service recovered, closing circuit")
self.failure_count = 0
self.state = "CLOSED"
return result
except Exception as e:
with self.lock:
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
circuit_breaker = PineconeCircuitBreaker(failure_threshold=5, timeout=60)
def safe_query(vector):
try:
return circuit_breaker.call(
index.query,
vector=vector,
top_k=10
)
except Exception:
# Return cached results or empty response
return get_fallback_results()
2. Implement Fallback Search Mechanisms
Option A: Cached results for common queries
import redis
import json
import hashlib
redis_client = redis.Redis(host='localhost', port=6379, db=0)
def query_with_cache(vector, top_k=10, ttl=3600):
"""Try Pinecone, fallback to Redis cache"""
# Create cache key from vector
vector_hash = hashlib.md5(json.dumps(vector).encode()).hexdigest()
cache_key = f"pinecone:query:{vector_hash}:{top_k}"
# Try Pinecone first
try:
results = index.query(vector=vector, top_k=top_k, include_metadata=True)
# Cache successful results
redis_client.setex(
cache_key,
ttl,
json.dumps(results.to_dict())
)
return results
except Exception as e:
# Pinecone failed - try cache
cached = redis_client.get(cache_key)
if cached:
print("Using cached results (Pinecone unavailable)")
return json.loads(cached)
else:
print("No cached results available")
raise # No fallback available
Option B: Fallback to traditional search
from elasticsearch import Elasticsearch
es = Elasticsearch(['localhost:9200'])
def hybrid_search(query_text, vector=None):
"""Try vector search (Pinecone), fallback to keyword search (Elasticsearch)"""
if vector is not None:
try:
# Attempt vector search
results = index.query(
vector=vector,
top_k=10,
include_metadata=True
)
return format_results(results, source="vector")
except Exception as e:
print(f"Vector search failed: {e}, falling back to keyword search")
# Fallback to Elasticsearch keyword search
es_results = es.search(index="documents", body={
"query": {
"multi_match": {
"query": query_text,
"fields": ["title^2", "content"]
}
},
"size": 10
})
return format_results(es_results, source="keyword")
3. Queue Operations for Later Processing
Implement durable queues to handle upsert operations during outages:
from celery import Celery
from kombu import Queue
import time
app = Celery('tasks', broker='redis://localhost:6379/0')
# Configure queue with retries
app.conf.task_queues = (
Queue('pinecone_upserts', routing_key='pinecone.upsert'),
)
app.conf.task_routes = {
'tasks.upsert_vectors': {'queue': 'pinecone_upserts'}
}
@app.task(bind=True, max_retries=10, default_retry_delay=60)
def upsert_vectors(self, vectors, namespace=None):
"""Retry upsert with exponential backoff"""
try:
index.upsert(vectors=vectors, namespace=namespace)
print(f"Successfully upserted {len(vectors)} vectors")
except Exception as exc:
# Exponential backoff: 1min, 2min, 4min, 8min, etc.
retry_delay = 60 * (2 ** self.request.retries)
print(f"Upsert failed, retrying in {retry_delay}s...")
raise self.retry(exc=exc, countdown=retry_delay)
# Usage: operations queue automatically during outages
documents = fetch_new_documents()
embeddings = generate_embeddings(documents)
upsert_vectors.delay(embeddings) # Will retry automatically
4. Monitor and Alert Proactively
Comprehensive monitoring setup:
import time
import requests
from datetime import datetime
class PineconeHealthMonitor:
"""Continuous health monitoring with alerting"""
def __init__(self, index, alert_webhook):
self.index = index
self.alert_webhook = alert_webhook
self.baseline_latency = 0.1 # 100ms baseline
self.alert_threshold = 0.5 # 500ms alert
self.consecutive_failures = 0
self.failure_threshold = 3
def check_health(self):
"""Run health check and return status"""
health_status = {
"timestamp": datetime.now().isoformat(),
"checks": {}
}
# Test 1: Index stats
try:
start = time.time()
stats = self.index.describe_index_stats()
latency = time.time() - start
health_status["checks"]["stats"] = {
"status": "healthy" if latency < self.alert_threshold else "degraded",
"latency": latency
}
except Exception as e:
health_status["checks"]["stats"] = {
"status": "failed",
"error": str(e)
}
# Test 2: Query operation
try:
start = time.time()
self.index.query(vector=[0.1] * 1536, top_k=1)
latency = time.time() - start
health_status["checks"]["query"] = {
"status": "healthy" if latency < self.alert_threshold else "degraded",
"latency": latency
}
except Exception as e:
health_status["checks"]["query"] = {
"status": "failed",
"error": str(e)
}
# Evaluate overall health
failed_checks = [
check for check in health_status["checks"].values()
if check["status"] == "failed"
]
if failed_checks:
self.consecutive_failures += 1
if self.consecutive_failures >= self.failure_threshold:
self.send_alert(health_status)
else:
self.consecutive_failures = 0
return health_status
def send_alert(self, health_status):
"""Send alert via webhook"""
requests.post(self.alert_webhook, json={
"text": f"🚨 Pinecone Health Alert",
"health_status": health_status
})
def run_continuous(self, interval=60):
"""Run health checks every interval seconds"""
while True:
self.check_health()
time.sleep(interval)
# Usage
monitor = PineconeHealthMonitor(
index=index,
alert_webhook="https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
)
monitor.run_continuous(interval=60) # Check every minute
Subscribe to external monitoring:
- API Status Check automated monitoring
- Pinecone status page notifications (status.pinecone.io)
- Custom Datadog/New Relic synthetic checks
5. Implement Read-Through Caching
For read-heavy applications, caching can significantly reduce impact:
from functools import lru_cache
import hashlib
import json
class PineconeCacheLayer:
"""Intelligent caching layer for Pinecone queries"""
def __init__(self, index, cache_backend):
self.index = index
self.cache = cache_backend
self.hit_count = 0
self.miss_count = 0
def _vector_key(self, vector, top_k, filter_dict):
"""Generate cache key from query parameters"""
key_data = {
"vector": vector[:10], # First 10 dims for key
"top_k": top_k,
"filter": filter_dict
}
return hashlib.md5(
json.dumps(key_data, sort_keys=True).encode()
).hexdigest()
def query(self, vector, top_k=10, filter=None, include_metadata=True, ttl=300):
"""Query with caching"""
cache_key = self._vector_key(vector, top_k, filter)
# Try cache first
cached_result = self.cache.get(cache_key)
if cached_result:
self.hit_count += 1
return json.loads(cached_result)
# Cache miss - query Pinecone
try:
result = self.index.query(
vector=vector,
top_k=top_k,
filter=filter,
include_metadata=include_metadata
)
# Store in cache
self.cache.setex(
cache_key,
ttl,
json.dumps(result.to_dict())
)
self.miss_count += 1
return result
except Exception as e:
# During outages, extend TTL of cached results
print(f"Pinecone unavailable: {e}")
# Try to find any cached version (even expired)
stale_result = self.cache.get(f"stale:{cache_key}")
if stale_result:
print("Returning stale cached results")
return json.loads(stale_result)
raise
def get_cache_stats(self):
total = self.hit_count + self.miss_count
hit_rate = self.hit_count / total if total > 0 else 0
return {
"hits": self.hit_count,
"misses": self.miss_count,
"hit_rate": f"{hit_rate:.2%}"
}
6. Post-Outage Recovery Protocol
Once Pinecone service is restored, follow this recovery checklist:
1. Verify service health:
def verify_service_recovery():
"""Comprehensive health check after outage"""
tests = []
# Test control plane
try:
indexes = pc.list_indexes()
tests.append(("List indexes", "✓ PASS"))
except Exception as e:
tests.append(("List indexes", f"✗ FAIL: {e}"))
# Test data plane operations
try:
index = pc.Index("your-index")
stats = index.describe_index_stats()
tests.append(("Describe stats", "✓ PASS"))
# Test query
results = index.query(vector=[0.1] * 1536, top_k=1)
tests.append(("Query operation", "✓ PASS"))
# Test upsert
index.upsert([("test-recovery", [0.1] * 1536, {"test": True})])
tests.append(("Upsert operation", "✓ PASS"))
# Cleanup
index.delete(ids=["test-recovery"])
except Exception as e:
tests.append(("Data plane ops", f"✗ FAIL: {e}"))
# Print results
print("\n=== Pinecone Recovery Verification ===")
for test_name, result in tests:
print(f"{test_name}: {result}")
all_passed = all("✓ PASS" in result for _, result in tests)
return all_passed
2. Process queued operations:
# Resume background workers
celery_app.control.inspect().active_queues()
# Monitor queue depth
queue_depth = celery_app.control.inspect().reserved()
print(f"Processing {len(queue_depth)} queued operations...")
3. Audit data consistency:
def audit_data_consistency(expected_count):
"""Verify all expected vectors were successfully upserted"""
stats = index.describe_index_stats()
actual_count = stats.total_vector_count
if actual_count < expected_count:
missing = expected_count - actual_count
print(f"⚠ Data inconsistency: {missing} vectors missing")
# Trigger re-sync process
trigger_resync()
else:
print(f"✓ Data consistent: {actual_count} vectors")
4. Clear expired caches:
# Flush Redis cache to ensure fresh data
redis_client.flushdb()
print("✓ Caches cleared, will rebuild from Pinecone")
5. Document incident:
## Incident Report: Pinecone Outage YYYY-MM-DD
**Timeline:**
- XX:XX - First errors detected
- XX:XX - Circuit breaker activated
- XX:XX - Fallback systems engaged
- XX:XX - Service restored
- XX:XX - Full recovery confirmed
**Impact:**
- XX,XXX failed queries
- XX,XXX queued upserts
- XX% cache hit rate during outage
- ~$X,XXX estimated revenue impact
**Response:**
- Circuit breaker prevented cascade failures
- Cached results served for XX% of requests
- Queue processed XX,XXX operations post-recovery
**Lessons Learned:**
- [What worked well]
- [What needs improvement]
**Action Items:**
- [ ] Increase cache TTL from 5min to 15min
- [ ] Add fallback to Elasticsearch for critical queries
- [ ] Set up additional monitoring for namespace operations
Related AI Infrastructure Guides
When Pinecone issues impact your AI stack, related services may also be affected or provide alternatives:
- Is OpenAI Down? - Embedding generation with
text-embedding-ada-002ortext-embedding-3-*models - Is Cohere Down? - Alternative embedding provider (
embed-english-v3.0,embed-multilingual-v3.0) - Is Anthropic Down? - Claude models for RAG generation after vector retrieval
- Is Hugging Face Down? - Open-source embedding models and vector search alternatives
Since most AI applications chain multiple services (embeddings → vector search → generation), monitoring your entire stack is critical for identifying the true source of issues.
Frequently Asked Questions
How often does Pinecone go down?
Pinecone maintains strong availability, typically exceeding 99.9% uptime. Major platform-wide outages are rare (2-4 times per year), though regional or component-specific issues may occur more frequently. Most production users experience minimal downtime from Pinecone. However, serverless indexes on free tiers may experience more variability than dedicated pod-based deployments.
What's the difference between serverless and pod-based indexes for reliability?
Pod-based indexes run on dedicated infrastructure and generally offer more predictable performance and availability. You have reserved capacity that isn't shared with other tenants. Serverless indexes share underlying infrastructure and may be more susceptible to multi-tenant resource contention during high load. For production applications requiring maximum reliability, pod-based indexes are recommended despite higher cost.
Can I run Pinecone across multiple regions for high availability?
Yes, Pinecone supports multi-region deployments. You can create replica indexes in different regions (US-East, US-West, EU-West, Asia-Pacific) and route queries based on user location or use one region as a failover. However, you'll need to manage data synchronization between regions yourself—Pinecone doesn't automatically replicate data across regions.
How do I choose between Pinecone, Weaviate, Qdrant, and Milvus?
Pinecone (managed service): Best for teams wanting zero infrastructure management, excellent developer experience, automatic scaling. Trade-off: vendor lock-in, higher cost at scale.
Weaviate/Qdrant/Milvus (self-hosted or managed): Better for large-scale deployments (100M+ vectors), cost optimization, data sovereignty requirements. Trade-off: operational complexity, need for DevOps expertise.
For most AI applications under 10M vectors, Pinecone's developer experience and reliability justify the cost. At 100M+ vectors, evaluate self-hosted alternatives for cost savings.
Should I implement my own vector search using FAISS or pgvector instead?
Use Pinecone/managed service when:
- You need production-ready infrastructure quickly
- Your team lacks ML infrastructure expertise
- You need auto-scaling and high availability
- Development speed is more valuable than cost optimization
Use FAISS/pgvector when:
- You have mature MLOps/DevOps capabilities
- You're operating at massive scale (100M+ vectors) where cost differences are significant
- You have strict data residency requirements
- You need custom indexing algorithms
For most startups and mid-size companies, managed services like Pinecone are the better choice. The engineering time saved far exceeds the cost premium.
How do I monitor Pinecone costs during an outage?
During outages with retry logic, you may inadvertently increase costs through:
- Repeated failed requests (still count against quota)
- Excessive stats/describe calls for health checks
- Duplicate upserts if idempotency isn't implemented properly
Monitor costs:
# Track request counts during incidents
import atexit
from collections import Counter
request_counts = Counter()
def track_request(operation):
request_counts[operation] += 1
# Register cleanup to report counts
atexit.register(lambda: print(f"Requests during incident: {dict(request_counts)}"))
Check your Pinecone dashboard's usage section to see operation counts and estimated costs. Implement circuit breakers to prevent runaway retry costs.
What SLAs does Pinecone offer?
Pinecone's SLA varies by plan:
- Standard (Serverless): No formal SLA, best-effort availability
- Enterprise (Pods): 99.9% uptime SLA with credits for violations
Enterprise customers receive service credits when availability drops below SLA thresholds. Review your specific agreement or contact Pinecone sales for SLA details.
How do I prevent data loss during Pinecone outages?
Pinecone handles data durability—vectors already upserted are not lost during outages. However, you need to handle in-flight operations:
1. Implement idempotent upserts:
# Use document IDs that allow safe retries
index.upsert([
("doc-12345", embedding, metadata) # Same ID = update, not duplicate
])
2. Queue operations durably:
# Use persistent queue (Redis, RabbitMQ, SQS)
# NOT in-memory queues that are lost on crashes
queue.enqueue(operation, persist=True)
3. Track upsert status:
# Store pending operations in database
db.insert_pending_upsert(doc_id, embedding, status="pending")
# Mark complete after successful upsert
db.update_status(doc_id, status="complete")
# Replay any "pending" after outages
How do Pinecone outages affect RAG application accuracy?
During outages, RAG applications often fall back to:
- Cached retrieval results (if available) - accuracy depends on cache freshness
- No retrieval (direct LLM queries) - significantly reduced accuracy, more hallucinations
- Traditional keyword search (if implemented) - moderate accuracy reduction
Typical accuracy impact:
- With recent cache: 5-10% accuracy reduction
- Without retrieval: 30-50% accuracy reduction
- With keyword search fallback: 15-25% accuracy reduction
Test your fallback mechanisms before incidents occur to understand the accuracy/availability trade-offs.
Can I get refunded for losses during Pinecone outages?
Pinecone's standard Terms of Service limit liability to service credits for Enterprise customers with SLAs. They typically don't cover consequential damages like lost revenue. Review your specific agreement—enterprise contracts may include different terms. For business-critical applications, consider:
- Purchasing business interruption insurance
- Implementing robust fallback systems
- Deploying multi-region or multi-vendor redundancy
Stay Ahead of Pinecone Outages
Don't let vector database downtime break your AI applications. Get real-time Pinecone alerts and know about issues before your users do.
API Status Check monitors Pinecone 24/7:
- ✅ 60-second health checks across all operations (query, upsert, stats)
- ✅ Regional monitoring for AWS, GCP, and Azure deployments
- ✅ Instant alerts via email, Slack, Discord, or webhook
- ✅ Historical uptime tracking and incident timeline
- ✅ Multi-service monitoring for your entire AI stack (OpenAI, Cohere, Anthropic, Pinecone)
Start monitoring Pinecone now →
Monitor Your Entire AI Infrastructure
Most AI applications depend on multiple services. Monitor them all in one place:
- OpenAI Status - GPT-4, embeddings, function calling
- Cohere Status - Embedding and generation models
- Anthropic Status - Claude for RAG completions
- Hugging Face Status - Open-source model hosting
- Pinecone Status - Vector database for semantic search
View all AI service monitors →
Last updated: February 4, 2026. Pinecone status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.pinecone.io.
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