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

Quick Answer: To check if Voyage AI is down, visit apistatuscheck.com/api/voyage-ai for real-time monitoring of embedding API endpoints. Common signs include embedding API timeouts, rate limiting errors, dimension mismatch failures, batch processing delays, and authentication issues. Voyage AI powers RAG pipelines, semantic search, and recommendation systems—making downtime critical for AI applications.

When your RAG pipeline suddenly stops generating embeddings, every minute of downtime cascades through your entire AI infrastructure. Voyage AI has emerged as a leading embeddings provider, offering state-of-the-art text representations that outperform many alternatives on retrieval benchmarks. Whether you're building semantic search, question-answering systems, or recommendation engines, knowing how to quickly verify Voyage AI's operational status can save hours of debugging and help you make informed decisions about your vector infrastructure.

How to Check Voyage AI Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify Voyage AI's operational status is through apistatuscheck.com/api/voyage-ai. This real-time monitoring service:

• Tests actual embedding endpoints every 60 seconds
• Monitors response times and latency trends across models
• Tracks API availability over 30/60/90 days
• Provides instant alerts when embedding failures are detected
• Validates authentication and rate limiting behavior

Unlike status pages that rely on manual updates, API Status Check performs active health checks against Voyage AI's production embedding endpoints, giving you the most accurate real-time picture of service availability across their model suite (voyage-large-2, voyage-code-2, voyage-lite-2).

2. Official Voyage AI Status Page

Check Voyage AI's official status communications through:

• Status page: status.voyageai.com (if available)
• Documentation: docs.voyageai.com for maintenance notices
• Community channels: Discord or Slack communities where Voyage AI engineers post updates

Pro tip: Join Voyage AI's developer community for real-time incident updates and direct communication with their engineering team during outages.

3. Test Embedding Endpoints Directly

For developers, making a test embedding call can quickly confirm connectivity and functionality:

import voyageai

# Initialize client
vo = voyageai.Client(api_key="YOUR_API_KEY")

# Test basic embedding
try:
    result = vo.embed(
        texts=["This is a test query"],
        model="voyage-large-2"
    )
    print(f"✓ Voyage AI operational - {len(result.embeddings[0])} dimensions")
except Exception as e:
    print(f"✗ Voyage AI error: {e}")

Look for timeout errors, authentication failures, or unexpected HTTP status codes (500/502/503).

4. Monitor Your Vector Database Query Performance

Since Voyage AI embeddings feed into vector databases, degraded performance often appears as:

• Increased query latency in Pinecone
• Embedding ingestion delays in Weaviate
• Failed similarity searches in Qdrant or Milvus
• Stale results due to indexing backlogs

If your vector database is healthy but embeddings aren't being generated, the issue is likely upstream with Voyage AI.

5. Check Social Media and Developer Communities

Real-world incident reports often surface on:

• Twitter/X: Search "Voyage AI down" or "@voyageai status"
• Reddit: r/MachineLearning, r/LangChain for user reports
• GitHub Issues: Voyage AI SDK repositories for bug reports
• Stack Overflow: Recent questions about connection errors

Community signals can confirm whether issues are widespread or isolated to your implementation.

Common Voyage AI Issues and How to Identify Them

Embedding API Timeouts

Symptoms:

• Requests hanging for 60+ seconds before timeout
• ReadTimeout or ConnectTimeout exceptions
• Inconsistent response times (some requests succeed, others timeout)
• Increased P95/P99 latency metrics

What it means: Timeout errors indicate Voyage AI's backend is overloaded, experiencing infrastructure issues, or network connectivity problems between your application and their API. Unlike normal processing delays (1-3 seconds for large batches), timeouts suggest the request never completed.

Example error:

requests.exceptions.ReadTimeout: HTTPSConnectionPool(host='api.voyageai.com', port=443): 
Read timed out. (read timeout=60)

Troubleshooting steps:

1. Check if other embedding providers (OpenAI, Cohere) are also affected
2. Test from different networks to rule out local connectivity issues
3. Verify you're using the latest SDK version with proper timeout configuration
4. Monitor apistatuscheck.com/api/voyage-ai for widespread reports

Rate Limiting Errors

Common error codes:

• 429 Too Many Requests - You've exceeded your quota
• RateLimitError - SDK-specific rate limit exception
• Retry-After headers indicating when to retry

Signs of rate limiting during outages:

• Rate limits triggering at unusually low request volumes
• Inconsistent rate limit behavior (sometimes works, sometimes doesn't)
• Rate limit errors across multiple API keys
• No Retry-After header or unrealistic retry times

Normal rate limiting vs. outage-related:

Normal Rate Limiting	Outage-Related Rate Limiting
Consistent with your tier	Affects all tiers simultaneously
Includes clear retry guidance	Missing or incorrect retry headers
Resolves after waiting	Persists even after extended delays
Specific to your API key	Reported by multiple users

Example rate limit handling:

from voyageai import Client, RateLimitError
import time

def embed_with_retry(texts, model="voyage-large-2", max_retries=3):
    """Embed texts with exponential backoff for rate limits"""
    vo = Client()
    
    for attempt in range(max_retries):
        try:
            return vo.embed(texts=texts, model=model)
        except RateLimitError as e:
            if attempt == max_retries - 1:
                raise
            
            # Parse retry-after header or use exponential backoff
            wait_time = 2 ** attempt
            print(f"Rate limited. Retrying in {wait_time}s...")
            time.sleep(wait_time)

Dimension Mismatch Errors

Symptoms:

• Embeddings returning unexpected dimensions
• ValueError when inserting into vector databases
• Model returning different dimensions than documented
• Inconsistent embedding sizes across batches

What causes this: While rare, API degradation can cause:

• Model version inconsistencies (wrong model served)
• Truncated responses due to infrastructure issues
• Partial embedding generation before timeout

Expected dimensions by model:

• voyage-large-2: 1536 dimensions
• voyage-code-2: 1536 dimensions
• voyage-2: 1024 dimensions
• voyage-lite-02-instruct: 1024 dimensions

Validation code:

def validate_embedding_dimensions(result, expected_dim=1536):
    """Validate embedding dimensions match expectations"""
    for idx, embedding in enumerate(result.embeddings):
        actual_dim = len(embedding)
        if actual_dim != expected_dim:
            raise ValueError(
                f"Dimension mismatch at index {idx}: "
                f"expected {expected_dim}, got {actual_dim}"
            )
    return True

# Usage
result = vo.embed(texts=documents, model="voyage-large-2")
validate_embedding_dimensions(result, expected_dim=1536)

Batch Processing Failures

Indicators:

• Batch requests timing out while single requests succeed
• Partial batch results (some embeddings missing)
• Inconsistent batch size limits
• OOM (Out of Memory) errors from API

Batch processing best practices:

def embed_in_batches(texts, batch_size=128, model="voyage-large-2"):
    """Process large text collections in manageable batches"""
    vo = Client()
    all_embeddings = []
    
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i + batch_size]
        
        try:
            result = vo.embed(texts=batch, model=model)
            all_embeddings.extend(result.embeddings)
            
            # Rate limiting protection
            if i + batch_size < len(texts):
                time.sleep(0.5)  # 500ms between batches
                
        except Exception as e:
            print(f"Batch {i}-{i+batch_size} failed: {e}")
            # Implement retry logic or fallback to smaller batches
            
    return all_embeddings

During outages, consider:

• Reducing batch size from 128 to 32 or 16
• Increasing delays between batches
• Implementing exponential backoff for failed batches
• Queuing failed batches for later retry

Authentication Issues

Common authentication errors:

• 401 Unauthorized - Invalid or expired API key
• 403 Forbidden - API key lacks required permissions
• InvalidAuthenticationError - SDK-specific auth failure

Outage-related authentication patterns:

• Previously working API keys suddenly failing
• Intermittent authentication (sometimes works, sometimes doesn't)
• Multiple users reporting auth issues simultaneously
• API key validation endpoints timing out

Verify authentication health:

import voyageai
import requests

def check_voyage_auth(api_key):
    """Verify API key is valid and Voyage AI auth system is operational"""
    
    # Method 1: Simple embedding test
    try:
        vo = voyageai.Client(api_key=api_key)
        vo.embed(texts=["auth test"], model="voyage-large-2")
        print("✓ Authentication successful")
        return True
    except voyageai.error.InvalidAuthenticationError:
        print("✗ Invalid API key")
        return False
    except Exception as e:
        print(f"✗ Auth system error: {e}")
        return False

# Usage
check_voyage_auth("YOUR_API_KEY")

The Real Impact When Voyage AI Goes Down

RAG Pipeline Failures

Retrieval-Augmented Generation systems depend on real-time embedding generation:

Broken user flows:

• Question-answering systems return "no results found"
• Document upload and indexing halts
• Semantic search queries fail silently
• Contextual chat responses degrade to generic answers

Example RAG pipeline impact:

# Typical RAG flow that fails during Voyage AI outage
def ask_question(question, knowledge_base):
    # Step 1: Embed user question (FAILS HERE)
    query_embedding = voyage_client.embed(
        texts=[question],
        model="voyage-large-2"
    )[0]
    
    # Step 2: Search vector database
    relevant_docs = vector_db.search(query_embedding, top_k=5)
    
    # Step 3: Generate answer with LLM
    answer = llm.generate(context=relevant_docs, question=question)
    
    return answer

# When Voyage AI is down, the entire pipeline fails at step 1

Cascading effects:

• Support chatbots can't access knowledge bases
• Customer queries go unanswered
• Internal Q&A tools become unusable
• AI-powered search results disappear

Semantic Search Degradation

For applications using Voyage embeddings for semantic search:

Impact on user experience:

• Search results fall back to keyword matching (much lower quality)
• Synonym detection stops working
• Multilingual search breaks down
• Personalized recommendations disappear

Business metrics affected:

• Search click-through rates drop 40-60%
• User engagement decreases significantly
• Bounce rates increase
• Revenue from recommended products declines

Example: E-commerce semantic search failure: A customer searching "comfortable running shoes" would normally get relevant results via Voyage embeddings understanding semantic similarity. During an outage, the system falls back to exact keyword matching, returning poor results and losing the sale.

Real-Time Content Processing Delays

Applications processing content in real-time face immediate backlog:

Affected workflows:

• Document ingestion pipelines halt
• Content moderation queues grow
• User-generated content isn't indexed
• Recommendation systems serve stale results

Example: Content platform impact:

# Content processing that backs up during outages
def process_new_articles(articles):
    for article in articles:
        # Generate embeddings for semantic indexing
        embedding = voyage_client.embed(
            texts=[article.full_text],
            model="voyage-large-2"
        )
        
        # Store in vector database for search
        vector_db.upsert(
            id=article.id,
            embedding=embedding[0],
            metadata=article.metadata
        )

During a 2-hour Voyage AI outage, a platform publishing 500 articles/hour would have a backlog of 1,000 unprocessed articles, all invisible to search until embeddings are generated.

AI Development and Training Disruptions

For teams building and iterating on AI systems:

Development impacts:

• Cannot test new RAG configurations
• Evaluation benchmarks can't run
• A/B tests produce incomplete data
• Model comparison experiments halt

Financial impact:

• Engineer time wasted waiting or debugging
• Missed product launch deadlines
• Customer demos fail
• Investor presentations disrupted

Lost productivity calculation:

• 10 AI engineers blocked: $5,000/hour (salary cost)
• 4-hour outage: $20,000 in lost productivity
• Plus opportunity cost of delayed features

Customer Trust and Churn Risk

When AI-powered features fail repeatedly:

Short-term effects:

• Increased support ticket volume
• Frustrated users posting negative reviews
• Social media complaints about "AI not working"
• Refund requests from paying customers

Long-term consequences:

• Users lose confidence in AI features
• Competitors gain advantage with more reliable embeddings
• Premium feature adoption decreases
• Customer lifetime value drops

For B2B SaaS companies selling AI-powered features, extended Voyage AI outages can directly trigger churn during renewal periods.

Incident Response Playbook: What to Do When Voyage AI Goes Down

1. Implement Fallback Embedding Providers

Multi-provider architecture:

from voyageai import Client as VoyageClient
import openai
import cohere

class EmbeddingManager:
    """Resilient embedding service with automatic failover"""
    
    def __init__(self):
        self.voyage = VoyageClient()
        self.providers = [
            ("voyage", self.voyage_embed),
            ("openai", self.openai_embed),
            ("cohere", self.cohere_embed)
        ]
        self.current_provider = 0
    
    def voyage_embed(self, texts):
        """Primary: Voyage AI embeddings"""
        result = self.voyage.embed(
            texts=texts,
            model="voyage-large-2"
        )
        return result.embeddings
    
    def openai_embed(self, texts):
        """Fallback 1: OpenAI embeddings"""
        response = openai.Embedding.create(
            input=texts,
            model="text-embedding-3-large"
        )
        return [item.embedding for item in response.data]
    
    def cohere_embed(self, texts):
        """Fallback 2: Cohere embeddings"""
        co = cohere.Client()
        response = co.embed(
            texts=texts,
            model="embed-english-v3.0"
        )
        return response.embeddings
    
    def embed(self, texts, max_retries=3):
        """Embed with automatic failover"""
        for provider_name, provider_func in self.providers:
            try:
                print(f"Trying {provider_name}...")
                embeddings = provider_func(texts)
                
                # Log successful provider
                if provider_name != "voyage":
                    print(f"⚠️ Failover: Using {provider_name} instead of Voyage AI")
                
                return embeddings
                
            except Exception as e:
                print(f"{provider_name} failed: {e}")
                continue
        
        raise Exception("All embedding providers failed")

# Usage
em = EmbeddingManager()
embeddings = em.embed(["document 1", "document 2", "document 3"])

Important considerations:

• Different providers have different embedding dimensions
• Mixing embeddings from different models breaks similarity search
• Build separate vector indexes per provider or rebuild index during failover
• Document which provider was used for each embedding in metadata

2. Queue Embedding Jobs for Later Processing

Graceful degradation with job queuing:

import redis
import json
from datetime import datetime

class EmbeddingQueue:
    """Queue embedding jobs when Voyage AI is unavailable"""
    
    def __init__(self):
        self.redis = redis.Redis(host='localhost', port=6379)
        self.queue_key = "embedding_queue"
    
    def queue_job(self, texts, model, metadata=None):
        """Add embedding job to queue"""
        job = {
            "texts": texts,
            "model": model,
            "metadata": metadata or {},
            "queued_at": datetime.utcnow().isoformat()
        }
        
        self.redis.rpush(self.queue_key, json.dumps(job))
        print(f"Queued {len(texts)} texts for later processing")
    
    def process_queue(self, batch_size=100):
        """Process queued jobs when service recovers"""
        vo = VoyageClient()
        processed = 0
        
        while True:
            job_json = self.redis.lpop(self.queue_key)
            if not job_json:
                break
            
            job = json.loads(job_json)
            
            try:
                result = vo.embed(
                    texts=job["texts"],
                    model=job["model"]
                )
                
                # Store embeddings in vector database
                store_embeddings(result.embeddings, job["metadata"])
                processed += len(job["texts"])
                
            except Exception as e:
                # Requeue failed job
                self.redis.rpush(self.queue_key, job_json)
                print(f"Requeued job due to error: {e}")
                break
        
        print(f"Processed {processed} queued embeddings")
        return processed

# Usage during outage
queue = EmbeddingQueue()

try:
    embeddings = vo.embed(texts=new_documents, model="voyage-large-2")
except:
    # Voyage AI down - queue for later
    queue.queue_job(texts=new_documents, model="voyage-large-2")
    # Show user message: "Content uploaded, will be searchable shortly"

3. Implement Circuit Breaker Pattern

Prevent cascade failures:

from datetime import datetime, timedelta

class CircuitBreaker:
    """Circuit breaker for Voyage AI API calls"""
    
    def __init__(self, failure_threshold=5, timeout_duration=60):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.timeout_duration = timeout_duration  # seconds
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
    
    def call(self, func, *args, **kwargs):
        """Execute function with circuit breaker protection"""
        
        # Check if circuit should reset
        if self.state == "OPEN":
            if datetime.now() - self.last_failure_time > timedelta(seconds=self.timeout_duration):
                self.state = "HALF_OPEN"
                self.failure_count = 0
            else:
                raise Exception("Circuit breaker OPEN - Voyage AI unavailable")
        
        try:
            result = func(*args, **kwargs)
            
            # Success - reset on half-open
            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()
            
            # Open circuit if threshold exceeded
            if self.failure_count >= self.failure_threshold:
                self.state = "OPEN"
                print(f"Circuit breaker OPEN after {self.failure_count} failures")
            
            raise e

# Usage
breaker = CircuitBreaker(failure_threshold=5, timeout_duration=60)

def embed_with_breaker(texts):
    return breaker.call(
        vo.embed,
        texts=texts,
        model="voyage-large-2"
    )

4. Cache Embeddings Aggressively

Reduce dependency on real-time embedding generation:

import hashlib
import pickle

class EmbeddingCache:
    """Cache embeddings to reduce API dependency"""
    
    def __init__(self, cache_dir="./embedding_cache"):
        self.cache_dir = cache_dir
        os.makedirs(cache_dir, exist_ok=True)
    
    def cache_key(self, text, model):
        """Generate cache key from text and model"""
        content = f"{model}:{text}"
        return hashlib.md5(content.encode()).hexdigest()
    
    def get(self, text, model):
        """Retrieve cached embedding"""
        key = self.cache_key(text, model)
        cache_path = os.path.join(self.cache_dir, f"{key}.pkl")
        
        if os.path.exists(cache_path):
            with open(cache_path, 'rb') as f:
                return pickle.load(f)
        return None
    
    def set(self, text, model, embedding):
        """Cache embedding"""
        key = self.cache_key(text, model)
        cache_path = os.path.join(self.cache_dir, f"{key}.pkl")
        
        with open(cache_path, 'wb') as f:
            pickle.dump(embedding, f)
    
    def embed_with_cache(self, texts, model="voyage-large-2"):
        """Embed with cache fallback"""
        vo = VoyageClient()
        results = []
        texts_to_embed = []
        text_indices = []
        
        # Check cache first
        for idx, text in enumerate(texts):
            cached = self.get(text, model)
            if cached:
                results.append(cached)
            else:
                texts_to_embed.append(text)
                text_indices.append(idx)
        
        # Embed uncached texts
        if texts_to_embed:
            try:
                fresh_embeddings = vo.embed(
                    texts=texts_to_embed,
                    model=model
                ).embeddings
                
                # Cache new embeddings
                for text, embedding in zip(texts_to_embed, fresh_embeddings):
                    self.set(text, model, embedding)
                    results.append(embedding)
                    
            except Exception as e:
                print(f"Embedding failed, using cache only: {e}")
                # Return partial results from cache
        
        return results

# Usage
cache = EmbeddingCache()
embeddings = cache.embed_with_cache(documents)

5. Monitor and Alert Proactively

Comprehensive monitoring setup:

import requests
import time
from datetime import datetime

class VoyageAIHealthMonitor:
    """Monitor Voyage AI health and alert on issues"""
    
    def __init__(self, alert_webhook_url):
        self.alert_webhook = alert_webhook_url
        self.vo = VoyageClient()
        self.consecutive_failures = 0
    
    def health_check(self):
        """Perform health check"""
        start_time = time.time()
        
        try:
            result = self.vo.embed(
                texts=["health check"],
                model="voyage-large-2"
            )
            
            latency = (time.time() - start_time) * 1000  # ms
            
            # Check response validity
            if len(result.embeddings) != 1:
                raise ValueError("Unexpected embedding count")
            
            if len(result.embeddings[0]) != 1536:
                raise ValueError(f"Unexpected dimensions: {len(result.embeddings[0])}")
            
            # Reset failure count on success
            if self.consecutive_failures > 0:
                self.send_alert(
                    "✅ Voyage AI Recovered",
                    f"Service is operational after {self.consecutive_failures} failures"
                )
            self.consecutive_failures = 0
            
            return {
                "status": "healthy",
                "latency_ms": latency,
                "timestamp": datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            self.consecutive_failures += 1
            
            if self.consecutive_failures >= 3:
                self.send_alert(
                    "🚨 Voyage AI Down",
                    f"Failed {self.consecutive_failures} consecutive health checks\nError: {str(e)}"
                )
            
            return {
                "status": "unhealthy",
                "error": str(e),
                "consecutive_failures": self.consecutive_failures,
                "timestamp": datetime.utcnow().isoformat()
            }
    
    def send_alert(self, title, message):
        """Send alert to webhook (Slack, Discord, etc.)"""
        payload = {
            "text": f"**{title}**\n{message}",
            "timestamp": datetime.utcnow().isoformat()
        }
        
        try:
            requests.post(self.alert_webhook, json=payload)
        except:
            print(f"Failed to send alert: {title}")
    
    def start_monitoring(self, interval_seconds=60):
        """Start continuous monitoring"""
        print(f"Starting Voyage AI health monitoring (every {interval_seconds}s)")
        
        while True:
            result = self.health_check()
            print(f"{result['timestamp']} - {result['status']}")
            time.sleep(interval_seconds)

# Usage
monitor = VoyageAIHealthMonitor(alert_webhook_url="YOUR_SLACK_WEBHOOK")
# Run in background thread or separate process

6. Communicate with Users During Outages

Transparent status updates:

# Update service status banner
def update_status_banner(status):
    """Show status banner to users"""
    banners = {
        "operational": None,  # No banner
        "degraded": {
            "message": "⚠️ Search functionality may be slower than usual",
            "color": "yellow"
        },
        "outage": {
            "message": "🔴 Search temporarily unavailable - we're working on it",
            "color": "red",
            "link": "https://apistatuscheck.com/api/voyage-ai"
        }
    }
    
    return banners.get(status)

# Email notification template
def notify_affected_users(outage_duration_minutes):
    """Send apology email to affected users"""
    subject = "Service Update: Search Functionality Restored"
    
    body = f"""
    Hi there,
    
    We wanted to let you know that our search and recommendation features 
    experienced issues for approximately {outage_duration_minutes} minutes today 
    due to a third-party service disruption.
    
    Service has been fully restored, and all functionality is now operating normally.
    
    We apologize for any inconvenience and appreciate your patience.
    
    - The Team
    """
    
    # Send to users active during outage
    send_email(to=affected_users, subject=subject, body=body)

Frequently Asked Questions

How often does Voyage AI experience outages?

Voyage AI maintains strong uptime as a specialized embeddings provider, though as a newer service compared to giants like OpenAI or Cohere, incident history is still being established. Most issues are brief (< 30 minutes) and related to rate limiting or regional API latency. Major outages affecting all customers are rare. Monitor apistatuscheck.com/api/voyage-ai for historical uptime data and incident tracking.

Can I use multiple embedding providers in the same vector database?

Technically yes, but it's not recommended. Embeddings from different models (Voyage vs. OpenAI vs. Cohere) live in different semantic spaces and aren't directly comparable. If you store mixed embeddings in one index, similarity searches will produce meaningless results. Best practice: Maintain separate vector indexes per embedding provider, or rebuild your entire index when switching providers during failover scenarios.

What's the difference between Voyage AI and OpenAI embeddings?

Voyage AI specializes exclusively in embeddings and typically outperforms OpenAI on retrieval benchmarks (measured by NDCG@10, Recall@100). Voyage models are trained specifically for semantic similarity tasks in RAG pipelines. OpenAI's text-embedding-3-large offers good general-purpose embeddings and broader ecosystem integration. For production RAG systems, consider both OpenAI as a battle-tested fallback to specialized Voyage embeddings.

How do I handle dimension mismatches when switching embedding providers?

When failing over from Voyage AI (1536 dimensions) to another provider with different dimensions:

Option 1: Separate indexes - Maintain parallel vector databases for each provider Option 2: Dimension reduction - Use PCA or truncation to normalize dimensions (degrades quality) Option 3: Rebuild index - Generate new embeddings for entire corpus with fallback provider (time-intensive)

Most production systems opt for Option 1 with pre-built indexes for each provider, enabling instant failover without quality loss.

Should I cache embeddings or generate them on-demand?

Cache when:

• Documents are relatively static (knowledge bases, documentation)
• Same queries repeat frequently (common user questions)
• Storage costs less than compute costs for your scale

Generate on-demand when:

• Content changes frequently (news, social media)
• Queries are highly unique (long-tail)
• Embedding updates needed for model improvements

Most applications use hybrid approaches: cache document embeddings aggressively, generate query embeddings on-demand with short TTL caching.

What SLA does Voyage AI offer?

Voyage AI's specific SLA terms vary by plan (free tier, pay-as-you-go, enterprise). Enterprise customers typically negotiate custom SLAs with uptime guarantees (99.9%+) and response time commitments. Check your account agreement or contact Voyage AI sales for specific SLA details applicable to your plan.

How do I prevent duplicate embeddings during retry logic?

Implement idempotency by:

1. Content-based deduplication:

import hashlib

def get_document_id(text):
    """Generate stable ID from content"""
    return hashlib.sha256(text.encode()).hexdigest()

# Use as vector database ID
doc_id = get_document_id(document_text)
vector_db.upsert(id=doc_id, embedding=embedding)
# Retries with same text won't create duplicates

2. Request tracking:

# Track processed documents
processed_ids = set()

if doc_id not in processed_ids:
    embedding = vo.embed([text])
    vector_db.upsert(id=doc_id, embedding=embedding[0])
    processed_ids.add(doc_id)

What's the cost impact of Voyage AI downtime?

Cost impact depends on your application:

RAG-powered chatbots: Lost customer queries can't be answered, increasing support costs Semantic search e-commerce: Degraded search relevance typically reduces conversion rates by 20-40% Content platforms: Unindexed content = invisible to users = lost ad revenue or subscriptions B2B AI features: Paying customers experiencing failures may churn during renewal

Calculate your specific impact: (average queries/hour) × (revenue per query) × (outage hours) = lost revenue

Are there open-source alternatives to Voyage AI for offline failover?

Yes, several options exist for on-premise embedding generation:

Sentence Transformers: Local models like all-mpnet-base-v2 (768 dim) Instructor models: hkunlp/instructor-large for task-specific embeddings BGE models: BAAI/bge-large-en-v1.5 competitive with cloud providers

Trade-offs: Lower quality than Voyage AI, requires GPU infrastructure, maintenance overhead. Best used as emergency fallback during extended cloud provider outages, not as primary solution.

Stay Ahead of Voyage AI Outages

Don't let embedding failures break your RAG pipeline. Subscribe to real-time Voyage AI alerts and get notified instantly when issues are detected—before your users notice.

API Status Check monitors Voyage AI 24/7 with:

• 60-second health checks across all embedding models
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime tracking and latency metrics
• Multi-API monitoring for your entire AI infrastructure stack

Also monitor related services:

• OpenAI Embeddings Status - Alternative embeddings provider
• Cohere Status - Specialized embeddings for enterprise
• Pinecone Status - Vector database for storing embeddings
• Weaviate Status - Open-source vector search
• Anthropic Claude Status - LLM for RAG generation
• LangChain Status - RAG orchestration framework

Start monitoring Voyage AI now →

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Last updated: February 4, 2026. Voyage AI status information is provided in real-time based on active monitoring. For the most accurate service status, always cross-reference multiple sources including official Voyage AI communications.