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

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

Quick Answer: To check if Deepgram is down, visit apistatuscheck.com/api/deepgram for real-time monitoring, or check the official status.deepgram.com page. Common signs include transcription failures, real-time streaming disconnects, HTTP 500/502 errors, audio format rejection, and webhook delivery delays.

When your speech-to-text pipeline suddenly stops working, every minute of downtime impacts customer experience. Deepgram powers millions of audio transcriptions daily for call centers, podcasting platforms, meeting tools, and accessibility services, making any service disruption a critical operational blocker. Whether you're experiencing failed transcriptions, streaming disconnects, or accuracy degradation, quickly verifying Deepgram's status can save hours of debugging and help you implement the right incident response.

How to Check Deepgram Status in Real-Time

1. API Status Check (Fastest Method)

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

  • Tests actual transcription endpoints every 60 seconds
  • Monitors both batch and streaming APIs for comprehensive coverage
  • Shows response times and latency trends across regions
  • Tracks historical uptime over 30/60/90 days
  • Provides instant alerts via email, Slack, or webhook when issues are detected
  • Monitors multiple regions (US, EU, APAC)

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

2. Official Deepgram Status Page

Deepgram maintains status.deepgram.com as their official communication channel for service incidents. The page displays:

  • Current operational status for all services
  • Active incidents and ongoing investigations
  • Scheduled maintenance windows
  • Historical incident reports and postmortems
  • Component-specific status (Batch API, Streaming API, Webhooks, Language Models)

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

3. Test Your API Key Directly

For developers, making a test transcription request can quickly confirm connectivity and functionality:

from deepgram import Deepgram
import asyncio

async def test_deepgram_health():
    deepgram = Deepgram('YOUR_API_KEY')
    
    # Test with a short audio URL
    source = {'url': 'https://static.deepgram.com/examples/interview_speech-analytics.wav'}
    
    try:
        response = await deepgram.transcription.prerecorded(source, {
            'punctuate': True,
            'model': 'nova-2',
        })
        print("✅ Deepgram is operational")
        print(f"Response time: {response.get('metadata', {}).get('duration', 'N/A')}s")
        return True
    except Exception as e:
        print(f"❌ Deepgram error: {str(e)}")
        return False

asyncio.run(test_deepgram_health())

Look for HTTP response codes outside the 2xx range, connection timeouts, or transcription accuracy anomalies.

4. Check Streaming WebSocket Connection

If you're using Deepgram's real-time streaming API, test the WebSocket connection:

from deepgram import Deepgram
import asyncio

async def test_streaming():
    deepgram = Deepgram('YOUR_API_KEY')
    
    try:
        deepgramLive = await deepgram.transcription.live({
            'punctuate': True,
            'interim_results': True
        })
        
        deepgramLive.registerHandler(
            deepgramLive.event.CLOSE,
            lambda c: print(f'Connection closed: {c}')
        )
        
        # Connection successful
        print("✅ Streaming connection established")
        return True
        
    except Exception as e:
        print(f"❌ Streaming error: {str(e)}")
        return False

asyncio.run(test_streaming())

5. Monitor Dashboard and Console

Log into your Deepgram Console to check:

  • Recent request logs and error rates
  • Usage metrics and anomalies
  • API key health status
  • Billing and quota information

If the console is slow to load or showing stale data, this may indicate infrastructure issues.

Common Deepgram Issues and How to Identify Them

Transcription Failures

Symptoms:

  • Requests returning 500/502/503 HTTP errors
  • Timeout errors after 30-60 seconds
  • Empty transcription results for valid audio
  • TRANSCRIPT_FAILED webhook events
  • Consistent failures across multiple audio files

What it means: When Deepgram's transcription pipeline is degraded, audio files that should process successfully start failing. This differs from audio format errors—you'll see a pattern of failures across different audio sources and formats.

Example error response:

{
  "err_code": "INTERNAL_SERVER_ERROR",
  "err_msg": "An internal error occurred during transcription",
  "request_id": "550e8400-e29b-41d4-a716-446655440000"
}

Real-Time Streaming Disconnects

Streaming API issues are often the first indicator of platform instability:

Common symptoms:

  • WebSocket connections dropping unexpectedly
  • CONNECTION_CLOSED events without client action
  • Reconnection attempts failing repeatedly
  • Increased latency before transcript chunks arrive
  • Missing interim results or final transcripts

Detection code:

disconnect_count = 0
last_disconnect_time = None

def on_close(self, close_code):
    global disconnect_count, last_disconnect_time
    disconnect_count += 1
    last_disconnect_time = time.time()
    
    if disconnect_count > 5:
        # Likely a Deepgram service issue
        send_alert("Multiple streaming disconnects detected")
        
deepgramLive.registerHandler(deepgramLive.event.CLOSE, on_close)

Impact: Real-time applications like live captioning, customer service transcription, and voice assistants become unusable during streaming outages.

Rate Limiting and Quota Issues

Normal rate limits (should not be exceeded during typical usage):

  • Batch API: 500 concurrent requests per API key
  • Streaming API: 500 concurrent connections per API key
  • Request rate: varies by plan tier

Outage indicators:

  • 429 Too Many Requests errors when you're well below your quota
  • Rate limit headers showing unexpectedly low limits
  • Throttling during off-peak hours

Example response:

{
  "err_code": "RATE_LIMIT_EXCEEDED",
  "err_msg": "Too many requests. Please try again later.",
  "retry_after": 60
}

If you're receiving rate limit errors despite normal usage patterns, this may indicate Deepgram is throttling requests due to infrastructure strain.

Audio Format Errors

Legitimate format errors (not outages):

  • INVALID_AUDIO_FORMAT - audio codec not supported
  • AUDIO_TOO_SHORT - file under minimum duration
  • UNSUPPORTED_SAMPLE_RATE - sample rate outside 8-48 kHz

Outage-related format issues:

  • Previously working audio files suddenly rejected
  • Inconsistent format validation (same file sometimes works, sometimes fails)
  • Generic error messages instead of specific format feedback

Validation script:

import requests

def validate_audio_processing(audio_url, api_key):
    """Test if audio processing is consistent"""
    results = []
    
    for attempt in range(3):
        response = requests.post(
            'https://api.deepgram.com/v1/listen',
            headers={'Authorization': f'Token {api_key}'},
            json={'url': audio_url}
        )
        results.append(response.status_code)
    
    # If results are inconsistent, likely an outage
    if len(set(results)) > 1:
        print("⚠️ Inconsistent responses detected - possible Deepgram issue")
        return False
    
    return all(code == 200 for code in results)

Language Model and Accuracy Issues

Symptoms:

  • Sudden drops in transcription accuracy
  • Language detection failures
  • Model-specific errors (e.g., nova-2 failing while nova works)
  • Punctuation and formatting inconsistencies
  • Entity recognition failures

Detection approach:

from difflib import SequenceMatcher

def monitor_accuracy(audio_url, expected_transcript, api_key):
    """Monitor if accuracy suddenly degrades"""
    response = transcribe_audio(audio_url, api_key)
    actual_transcript = response['results']['channels'][0]['alternatives'][0]['transcript']
    
    similarity = SequenceMatcher(None, expected_transcript, actual_transcript).ratio()
    
    if similarity < 0.7:  # Below 70% match
        print(f"⚠️ Accuracy degraded: {similarity:.2%}")
        send_alert("Deepgram accuracy anomaly detected")

What it means: Language model infrastructure may be degraded, causing accuracy regressions even when requests succeed.

Webhook Delivery Delays

Deepgram can send transcription results via webhook callbacks:

Normal behavior: Webhooks arrive within 1-5 seconds of transcription completion

Outage indicators:

  • Webhooks delayed by minutes or hours
  • Missing webhook deliveries
  • Callbacks arriving out of order
  • Retry attempts exhausted

Monitoring webhook latency:

import time

webhook_start_times = {}

def initiate_transcription(audio_id, audio_url):
    webhook_start_times[audio_id] = time.time()
    
    # Submit transcription with callback
    deepgram.transcription.prerecorded(
        {'url': audio_url},
        {'callback': f'https://yourserver.com/webhook/{audio_id}'}
    )

def webhook_handler(audio_id):
    latency = time.time() - webhook_start_times.get(audio_id, 0)
    
    if latency > 30:  # More than 30 seconds
        log_warning(f"Webhook delayed: {latency}s for {audio_id}")

The Real Business Impact When Deepgram Goes Down

Call Centers and Customer Service

Immediate operational impact:

  • Live agent assist tools stop working
  • Real-time sentiment analysis unavailable
  • Automatic call summaries fail to generate
  • Compliance recording and transcription halted
  • Quality assurance workflows blocked

Financial impact: A call center handling 1,000 calls/hour with agents relying on real-time transcription for assistance sees:

  • 30-50% increase in average handle time
  • Quality scores drop as agents lose context
  • Customer satisfaction declines
  • Lost productivity: 500+ hours of manual note-taking per outage hour

Example: A healthcare call center cannot generate HIPAA-compliant call transcripts, forcing manual documentation and creating compliance risks.

Podcasting and Media Production

Workflow disruptions:

  • Automated podcast transcriptions for show notes blocked
  • Captioning pipelines fail for video content
  • Search and discovery features break (no transcripts to index)
  • Content accessibility requirements violated (ADA/WCAG)
  • Ad insertion based on content analysis fails

Revenue impact:

  • Delayed content publication affects ad revenue
  • SEO suffers without transcribed content
  • Audience reach limited without captions
  • Premium features (searchable transcripts) unavailable to paid subscribers

Recovery time: Even after Deepgram recovers, processing backlogged episodes can take hours or days, extending the business impact beyond the outage window.

Meeting and Video Conferencing Platforms

User-facing failures:

  • Live captions disappear mid-meeting
  • Post-meeting transcripts fail to generate
  • Action item extraction and summaries break
  • Search within recordings becomes unavailable
  • Accessibility features for deaf/hard-of-hearing users fail

Brand reputation risk: Users may perceive transcription features as unreliable, leading to:

  • Feature adoption decline
  • Negative reviews and social media complaints
  • Enterprise customer churn
  • Support ticket volume spikes

Example: A remote-first company relies on automated meeting transcripts for distributed team collaboration. An outage means:

  • Missed action items and decisions
  • Timezone-shifted employees lack meeting context
  • Reduced productivity and alignment

Voice Assistant and Conversational AI

System failures:

  • Voice commands not recognized
  • Chatbot voice interactions fail
  • IVR (Interactive Voice Response) systems break
  • Voice authentication unavailable
  • Multilingual support degrades

Customer experience impact:

  • Users must fall back to slower text or phone menus
  • Accessibility tools for visually impaired users fail
  • Smart home integrations become unusable
  • In-car voice controls stop working

Accessibility Services

Critical impact on users with disabilities:

  • Real-time captioning for deaf students in classrooms fails
  • Live event captioning unavailable (conferences, concerts, theater)
  • Assistive technology for speech recognition breaks
  • Video accessibility compliance violations

Legal and regulatory risk: Organizations may be in violation of:

  • Americans with Disabilities Act (ADA)
  • Section 508 compliance requirements
  • WCAG accessibility standards
  • University accessibility policies

An outage during a live event or classroom session cannot be easily recovered—the damage is immediate and irreversible for those relying on accessibility features.

Incident Response Playbook for Deepgram Outages

1. Implement Robust Error Handling and Retries

Exponential backoff with jitter:

import time
import random
from deepgram import Deepgram

async def transcribe_with_retry(audio_source, max_retries=5):
    deepgram = Deepgram('YOUR_API_KEY')
    
    for attempt in range(max_retries):
        try:
            response = await deepgram.transcription.prerecorded(
                audio_source,
                {'punctuate': True, 'model': 'nova-2'}
            )
            return response
            
        except Exception as e:
            if attempt == max_retries - 1:
                raise  # Final attempt failed
            
            # Exponential backoff: 1s, 2s, 4s, 8s, 16s
            wait_time = (2 ** attempt) + random.uniform(0, 1)
            print(f"Attempt {attempt + 1} failed, retrying in {wait_time:.1f}s")
            time.sleep(wait_time)

Idempotency for transcription jobs:

import hashlib

def generate_job_id(audio_url):
    """Create deterministic job ID for deduplication"""
    return hashlib.sha256(audio_url.encode()).hexdigest()[:16]

async def transcribe_idempotent(audio_url):
    job_id = generate_job_id(audio_url)
    
    # Check if already processed
    if cached_result := cache.get(job_id):
        return cached_result
    
    # Process and cache
    result = await transcribe_with_retry({'url': audio_url})
    cache.set(job_id, result, ttl=3600)
    return result

2. Queue Transcription Jobs for Later Processing

When Deepgram is down, queue audio files instead of failing user requests:

from redis import Redis
from rq import Queue

queue = Queue('transcription', connection=Redis())

def enqueue_transcription(audio_url, user_id, callback_url):
    """Queue transcription for background processing"""
    job = queue.enqueue(
        'transcribe_audio',
        audio_url=audio_url,
        user_id=user_id,
        callback_url=callback_url,
        retry=Retry(max=10, interval=[60, 300, 600])  # Retry over several hours
    )
    
    # Notify user
    send_notification(user_id, 
        "Your transcription is queued and will be processed shortly. "
        "We're experiencing higher than normal processing times."
    )
    
    return job.id

Worker with health checking:

async def transcription_worker():
    while True:
        job = queue.dequeue()
        
        # Health check before processing
        if not await deepgram_health_check():
            queue.enqueue(job)  # Re-queue for later
            await asyncio.sleep(60)  # Wait before checking again
            continue
        
        # Process job
        result = await transcribe_with_retry(job.audio_url)
        await callback_url.post(result)

3. Implement Fallback Transcription Services

Enterprise applications often implement multi-provider strategies:

Provider abstraction layer:

from abc import ABC, abstractmethod

class TranscriptionProvider(ABC):
    @abstractmethod
    async def transcribe(self, audio_url, options):
        pass

class DeepgramProvider(TranscriptionProvider):
    async def transcribe(self, audio_url, options):
        deepgram = Deepgram(os.getenv('DEEPGRAM_API_KEY'))
        return await deepgram.transcription.prerecorded({'url': audio_url}, options)

class WhisperProvider(TranscriptionProvider):
    async def transcribe(self, audio_url, options):
        # OpenAI Whisper fallback
        import openai
        audio_file = download_audio(audio_url)
        return openai.Audio.transcribe("whisper-1", audio_file)

class AssemblyAIProvider(TranscriptionProvider):
    async def transcribe(self, audio_url, options):
        import assemblyai as aai
        aai.settings.api_key = os.getenv('ASSEMBLYAI_API_KEY')
        transcriber = aai.Transcriber()
        return transcriber.transcribe(audio_url)

Automatic failover logic:

providers = [
    DeepgramProvider(),
    WhisperProvider(),
    AssemblyAIProvider()
]

async def transcribe_with_failover(audio_url, options):
    errors = []
    
    for provider in providers:
        try:
            result = await provider.transcribe(audio_url, options)
            log_metric(f'transcription.provider.{provider.__class__.__name__}')
            return result
            
        except Exception as e:
            errors.append(f"{provider.__class__.__name__}: {str(e)}")
            continue
    
    # All providers failed
    raise Exception(f"All transcription providers failed: {errors}")

Multi-provider considerations:

  • Cost differences: Deepgram typically more cost-effective than alternatives
  • Accuracy variations: Model outputs differ across providers
  • Feature parity: Not all features available on all platforms (diarization, custom vocabulary)
  • Format compatibility: Audio format support varies

4. Graceful Degradation for Streaming

When real-time streaming fails, fallback to buffered transcription:

class StreamingWithFallback:
    def __init__(self, api_key):
        self.deepgram = Deepgram(api_key)
        self.audio_buffer = []
        self.streaming_active = True
    
    async def start_streaming(self):
        try:
            self.connection = await self.deepgram.transcription.live({
                'punctuate': True,
                'interim_results': True
            })
            
            self.connection.registerHandler(
                self.connection.event.TRANSCRIPT_RECEIVED,
                self.on_transcript
            )
            
            self.connection.registerHandler(
                self.connection.event.CLOSE,
                self.on_disconnect
            )
            
        except Exception as e:
            print(f"Streaming failed, switching to buffered mode: {e}")
            self.streaming_active = False
    
    def send_audio(self, audio_chunk):
        if self.streaming_active:
            try:
                self.connection.send(audio_chunk)
            except:
                self.streaming_active = False
                print("Stream disconnected, buffering audio")
        
        # Always buffer as backup
        self.audio_buffer.append(audio_chunk)
    
    async def on_disconnect(self, code):
        print(f"Connection closed: {code}")
        self.streaming_active = False
        
        # Process buffered audio as batch
        if self.audio_buffer:
            print("Processing buffered audio as batch transcription")
            await self.process_buffer()
    
    async def process_buffer(self):
        # Combine audio chunks and process as batch
        full_audio = b''.join(self.audio_buffer)
        result = await self.deepgram.transcription.prerecorded(
            {'buffer': full_audio},
            {'punctuate': True}
        )
        return result

5. Monitor and Alert Proactively

Comprehensive health monitoring:

import asyncio
from dataclasses import dataclass
from datetime import datetime

@dataclass
class HealthStatus:
    timestamp: datetime
    batch_api_healthy: bool
    streaming_api_healthy: bool
    average_latency_ms: float
    error_rate: float

async def comprehensive_health_check():
    """Run multiple health checks in parallel"""
    results = await asyncio.gather(
        check_batch_api(),
        check_streaming_api(),
        check_webhook_delivery(),
        check_accuracy_baseline(),
        return_exceptions=True
    )
    
    health = HealthStatus(
        timestamp=datetime.now(),
        batch_api_healthy=not isinstance(results[0], Exception),
        streaming_api_healthy=not isinstance(results[1], Exception),
        average_latency_ms=calculate_latency(results),
        error_rate=calculate_error_rate()
    )
    
    if not health.batch_api_healthy or not health.streaming_api_healthy:
        send_alert("🚨 Deepgram health check failed", health)
    
    return health

Alerting strategy:

def send_alert(message, health_status):
    """Send alerts through multiple channels"""
    
    # Immediate alert for critical issues
    if not health_status.batch_api_healthy:
        pagerduty.trigger_incident(
            service_id='DEEPGRAM_SERVICE',
            summary=message,
            severity='critical'
        )
    
    # Slack notification
    slack.post_message(
        channel='#engineering-alerts',
        text=f"{message}\n"
             f"Batch API: {'✅' if health_status.batch_api_healthy else '❌'}\n"
             f"Streaming API: {'✅' if health_status.streaming_api_healthy else '❌'}\n"
             f"Latency: {health_status.average_latency_ms}ms\n"
             f"Error rate: {health_status.error_rate:.2%}"
    )
    
    # Log to monitoring system
    datadog.event(
        title='Deepgram Health Alert',
        text=message,
        alert_type='error' if not health_status.batch_api_healthy else 'warning'
    )

Subscribe to external monitoring:

  • API Status Check alerts for automated monitoring
  • Deepgram status page notifications (email/Slack)
  • Your own synthetic monitoring with test audio files
  • Error rate monitoring in application logs

6. Communicate with Users Transparently

Status banner component:

function TranscriptionStatusBanner() {
  const [deepgramStatus, setDeepgramStatus] = useState('operational');
  
  useEffect(() => {
    // Check status every 60 seconds
    const checkStatus = async () => {
      const response = await fetch('https://apistatuscheck.com/api/deepgram');
      const data = await response.json();
      setDeepgramStatus(data.status);
    };
    
    checkStatus();
    const interval = setInterval(checkStatus, 60000);
    return () => clearInterval(interval);
  }, []);
  
  if (deepgramStatus !== 'operational') {
    return (
      <div className="alert alert-warning">
        ⚠️ Transcription services are experiencing delays. 
        Your audio is queued and will be processed shortly.
        <a href="/status">View status page →</a>
      </div>
    );
  }
  
  return null;
}

Proactive user communication:

  • Email notifications to affected users
  • In-app status indicators
  • Extended processing time estimates
  • Offer alternatives (manual transcription, delayed processing)

7. Post-Outage Recovery Checklist

Once Deepgram service is restored:

  1. Process queued transcriptions from your job queue
  2. Retry failed webhook deliveries for time-sensitive callbacks
  3. Validate accuracy of transcriptions processed during degraded performance
  4. Review buffered audio from streaming fallback mode
  5. Audit for missing transcripts by comparing audio uploads to completed jobs
  6. Analyze financial impact (queued jobs, lost users, support costs)
  7. Update incident documentation with timeline and lessons learned
  8. Review and improve resilience (adjust retry logic, consider additional fallbacks)
  9. Communicate resolution to users who experienced issues

Post-mortem template:

## Deepgram Outage Post-Mortem

**Date:** [Date of outage]
**Duration:** [Start time] - [End time] ([X] hours)
**Impact:** [Number] transcription requests affected

### Timeline
- [Time]: First errors detected
- [Time]: Deepgram status page updated
- [Time]: Failover to backup provider initiated
- [Time]: Service restored

### Root Cause
[Deepgram's reported cause or your analysis]

### Impact Assessment
- Transcriptions queued: [X]
- Failed requests: [X]
- User complaints: [X]
- Revenue impact: $[X]

### What Went Well
- [Monitoring detected issue within X minutes]
- [Automatic retry logic prevented data loss]

### What Went Wrong
- [Alerts didn't trigger until X minutes after first failure]
- [Backup provider not configured for feature parity]

### Action Items
- [ ] Implement multi-provider failover
- [ ] Add streaming health checks
- [ ] Improve alert sensitivity
- [ ] Document communication playbook

Frequently Asked Questions

How often does Deepgram go down?

Deepgram maintains strong uptime, typically exceeding 99.9% availability. Major outages affecting all customers are rare (typically 1-2 per year), though regional or model-specific issues may occur more frequently. Most businesses experience minimal disruption from Deepgram in a typical year, but having redundancy for mission-critical applications is recommended.

What's the difference between batch and streaming API outages?

The batch (prerecorded) API and streaming (live) API run on separate infrastructure. During an outage, one may be affected while the other remains operational. Streaming API issues are more common due to the complexity of maintaining persistent WebSocket connections, while batch API tends to be more stable. Monitor both separately if your application uses both.

Should I use Deepgram webhooks or polling for transcription results?

For batch transcriptions, webhooks are more efficient and provide faster results. However, implement a polling fallback for critical operations—if a webhook hasn't arrived within your expected timeframe (typically 1-2x the audio duration), poll the API to retrieve results. During outages, webhooks may be delayed while direct API access might still work.

How do I prevent duplicate transcriptions during Deepgram outages?

Use idempotency keys or generate deterministic job IDs based on audio file hashes. When implementing retry logic, check your database or cache before re-submitting the same audio. Deepgram doesn't currently provide built-in idempotency keys like Stripe, so you must implement this logic in your application layer.

What are the best fallback transcription services if Deepgram goes down?

Popular alternatives include:

  • OpenAI Whisper - High accuracy, supports 99 languages, but slower processing
  • AssemblyAI - Similar feature set to Deepgram, comparable accuracy
  • AWS Transcribe - Enterprise-grade reliability, AWS ecosystem integration
  • Google Speech-to-Text - Strong language support, GCP integration

Each has different pricing, accuracy profiles, and feature sets. Evaluate based on your specific needs (language support, real-time vs batch, accuracy requirements).

Can I get refunded or compensated for losses during Deepgram outages?

Deepgram's Terms of Service include uptime SLAs for Enterprise customers, which may include service credits for downtime below guaranteed thresholds. Standard and Growth plans typically don't include SLA credits. Review your specific plan agreement or contact Deepgram support for clarification on your account's terms. Indirect losses (lost revenue, user churn) are typically not covered.

Why did my transcription accuracy suddenly drop?

Sudden accuracy drops without a service outage can be caused by:

  • Model changes - Deepgram occasionally updates models; specify version explicitly (e.g., nova-2-general instead of nova-2)
  • Audio quality issues - Background noise, low bitrate, or format issues on your end
  • Language detection errors - Specify language explicitly if auto-detection is incorrect
  • Domain mismatch - Use specialized models (finance, medical, conversational) for better accuracy

If accuracy drops across all audio types simultaneously, check Deepgram's status page or contact support—it may indicate infrastructure issues.

How long do Deepgram outages typically last?

Based on historical incidents:

  • Minor degradation: 15-30 minutes (partial functionality, increased latency)
  • Moderate outages: 1-2 hours (regional or service-specific failures)
  • Major outages: 2-4 hours (rare, platform-wide impacts)

Streaming API issues often resolve faster (15-45 minutes) than batch API problems. Check apistatuscheck.com/api/deepgram for real-time status and historical incident data.

Should I cache Deepgram transcription results?

Yes, absolutely. Implement caching to:

  • Reduce API costs - Avoid re-transcribing the same audio
  • Improve performance - Instant retrieval vs waiting for transcription
  • Increase resilience - Serve cached results during outages
import hashlib

def get_audio_hash(audio_url):
    return hashlib.sha256(audio_url.encode()).hexdigest()

async def transcribe_with_cache(audio_url):
    cache_key = f"transcript:{get_audio_hash(audio_url)}"
    
    # Check cache first
    if cached := redis.get(cache_key):
        return json.loads(cached)
    
    # Transcribe and cache
    result = await deepgram.transcription.prerecorded({'url': audio_url})
    redis.setex(cache_key, 86400 * 30, json.dumps(result))  # Cache 30 days
    return result

What regions does Deepgram operate in?

Deepgram operates global infrastructure with primary regions in:

  • United States (primary)
  • Europe (GDPR-compliant processing)
  • Asia-Pacific (lower latency for APAC customers)

An outage may affect specific regions while others remain operational. Enterprise customers can specify regions for data residency and compliance requirements. Monitor regional status separately if your application serves a global user base.

How do I know if the issue is Deepgram or my audio files?

Test with known-good audio:

# Deepgram's sample audio files (known to work)
TEST_AUDIO = 'https://static.deepgram.com/examples/interview_speech-analytics.wav'

async def diagnose_issue():
    # Test with Deepgram's sample
    try:
        result = await deepgram.transcription.prerecorded({'url': TEST_AUDIO})
        print("✅ Deepgram is working - issue is with your audio")
    except Exception as e:
        print(f"❌ Deepgram error: {e} - platform issue")
        
    # Test with your audio
    try:
        result = await deepgram.transcription.prerecorded({'url': YOUR_AUDIO})
        print("✅ Your audio works - was a transient issue")
    except Exception as e:
        print(f"❌ Your audio error: {e} - check format/encoding")

If Deepgram's samples work but your audio fails, the issue is with your audio format, encoding, or accessibility (URL not reachable by Deepgram).

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Last updated: February 4, 2026. Deepgram status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.deepgram.com.

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