Handling LLM Platform Outages: What to Do When OpenAI, Anthropic, DeepSeek, or Others Go Down

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Topics Covered:

• What it means when an LLM platform is “down”

• Common reasons for outages at providers like OpenAI, Anthropic, DeepSeek, Together AI, Deepinfra, Nebius, or OpenRouter

• Strategies to mitigate downtime—from caching, to fallback providers, to load balancing

• How Requesty Router can simplify dealing with partial or full service interruptions

• Best practices for status monitoring, failover, and user communication

If you’ve ever typed “OpenAI down”, “Anthropic down”, “DeepSeek down”, “OpenRouter down”, “Together AI down”, or any of the above with “AI” appended (for instance, “OpenRouter AI down”), you’re likely not alone. Large Language Model (LLM) providers can—and occasionally do—experience service interruptions. Whether they’re planned maintenance windows, unexpected traffic spikes, or hardware/network issues, outages can wreak havoc on your applications if you aren’t prepared. Let’s dive into how to handle these scenarios effectively.

Understanding LLM Outages

1. Full vs. Partial Downtime

• Full downtime is when the provider’s APIs are completely unavailable—requests fail instantly or time out, and you have no way to continue using the service.

• Partial downtime can manifest as degraded performance, longer response times, or intermittent error codes. For instance, you might get “server busy” or 503/504 errors sporadically.

2. Status Messages and Official Channels

• Providers often post status updates on a dedicated status page. For instance, OpenAI and Anthropic have official pages, while smaller providers like DeepSeek, Together AI, Deepinfra, or Nebius might share updates on their developer portal or via Discord/Slack announcements.

• System notifications: Some providers send email alerts or Slack messages for planned maintenance. If your app relies heavily on real-time LLM calls, subscribe to these alerts so you’re never caught off-guard.

3. Common Causes of Outages

• Traffic Spikes: Sudden surges (e.g., product launches, viral content, large events) can overload API servers.

• Infrastructure Failures: Hardware issues, data center outages, or networking disruptions can bring services offline.

• Scheduled Maintenance: Providers may schedule updates or major expansions that require downtime or rolling restarts.

• DDoS or Security Incidents: Malicious attacks can cause an emergency shutdown or rate-limiting that affects legitimate traffic.

Is “OpenAI Down”? Checking for Yourself

When users notice errors or slow responses, they often Google “OpenAI down” or “OpenAI status” to verify. Here’s how to check quickly:

1. Visit the Provider’s Status Page:

   • OpenAI: status.openai.com

   • Anthropic: status.anthropic.com

   • Others (DeepSeek, Together AI, etc.): Check their docs or official site for an uptime or incident page.

2. Look for Real-Time Updates:

   • Many providers list ongoing incidents, e.g., “Partial Degradation” or “Major Outage.”

   • They’ll often post an estimated resolution time or immediate next steps.

3. API Error Codes:

   • 429 (“Too Many Requests”) or 503 (“Service Unavailable”) might indicate a partial outage or rate-limiting.

   • 5xx errors can signal server problems unrelated to rate limits.

4. Community Channels:

   • Twitter/X, Discord, or Slack communities: see if other devs are reporting the same issue.

If everything seems normal yet you still get failures, it might be a local issue—like an expired API key, rate-limit exceedance, or networking glitch. Always double-check your usage logs and developer dashboards before concluding the provider is down.

Platforms Frequently Mentioned in “Down” Searches

Below are some popular LLM platforms that can (and occasionally do) face downtime.

1. OpenAI:

   • Large user base means downtime can spark widespread tweets or frantic “is GPT-4 down?” queries.

   • Provides official status updates and scheduled maintenance notifications.

2. Anthropic (Claude):

   • Known for robust infrastructure, but no provider is immune to partial disruptions.

   • Their “Claude” models, especially new releases (like Claude 3.7 Sonnet), can see heavy spikes right after launch.

3. DeepSeek:

   • Marketed as not enforcing explicit rate limits, but can slow down heavily under traffic surges—sometimes to the point of perceived downtime.

   • They keep long connections open but can effectively stall requests when overloaded.

4. OpenRouter:

   • Routes requests across multiple LLM providers, occasionally experiences outages if the underlying providers or the router infrastructure have issues.

   • Searching “OpenRouter down” or “OpenRouter AI down” often leads to their system status page or community forums.

5. Together AI:

   • Focuses on community-run HPC for AI. Outages can occur if node providers have network failures or resource constraints.

6. Deepinfra:

   • A specialized platform for custom LLM deployments. Maintenance on GPU clusters can temporarily stall requests.

7. Nebius:

   • A newer solution offering distributed AI computing. Occasional downtime might happen during cluster expansions or region failures.

Mitigating Downtime: Strategies & Tactics

1. Implement Fallback Providers

• Multi-Provider Architecture: If your app can switch from Anthropic to OpenAI (or vice versa) in real-time, you remain operational when one provider experiences trouble.

• Regional Redundancy: If a provider has multi-region endpoints, you can redirect traffic to a different region. This helps if only a single data center is down.

2. Use Requesty Router’s Failover

• Automatic Failover: Requesty can detect when your primary provider is returning a high error rate and failover to another configured model or service.

• Load Balancing: Spread calls across multiple providers to reduce risk of hitting a single point of failure.

3. Caching & Offline Processing

• Cache Frequently Accessed Responses: For example, if your application returns a popular FAQ answer from a model, store it locally or in a database. If “OpenAI is down,” you can still serve the last known response.

• Offline/Bulk Jobs: If tasks aren’t time-sensitive, schedule them in batch mode to run overnight. Even if you hit an outage window, you can retry automatically later.

4. Graceful Degradation

• If you can’t fully switch providers, show partial results or alternative content. For instance, if you rely on LLM-based suggestions in an e-commerce app, you might revert to a simpler rule-based recommendation system until the LLM returns.

5. User Communication

• Proactively inform users if your LLM-based features might be unavailable or limited. Show a notice: “We’re experiencing higher-than-usual error rates from our AI provider. Some features may be delayed.”

When OpenAI or Anthropic Is “Down”: A Checklist

1. Check Official Status: Confirm it’s truly an outage, not your local environment.

2. Look at Error Codes: 429 and 503 are common for partial or complete downtime.

3. Fail Over to Another Provider (If Possible): e.g., redirect calls from Claude to GPT-3.5 or GPT-4.

4. Notify End Users: Show real-time banners or alerts in your UI.

5. Limit Non-Essential Calls: Slow down background tasks, reduce concurrency, or turn off auto-scaling that might compound the problem with more requests.

6. Retry with Exponential Backoff: Don’t hammer the API—respect the provider’s meltdown.

Requesty Router: Simplifying Outage Management

Requesty Router is designed to route queries to multiple LLM providers, handle token usage and rate limits, and detect when a service is underperforming. Key features for downtime:

• Health Checks: Built-in detection of repeated 5xx or 429 errors from a provider.

• Failover Rules: You can specify that if “primary=OpenAI” returns errors for 30 seconds, switch all traffic to “backup=Anthropic” or “DeepSeek.”

• Queue & Retry: If you’re seeing 503 errors from all providers, the Router can queue requests and retry once at least one provider recovers.

This means you’ll spend less time coding custom fallbacks and more time focusing on your core application logic.

Monitoring & Alerting

Keeping tabs on your LLM usage and availability is crucial. Here are some best practices:

1. API Health Dashboards

   • Track response codes, latency, and success rates. Tools like Datadog, New Relic, or custom dashboards help you quickly spot anomalies (e.g., a sudden spike in errors).

2. Status Page Integrations

   • Many providers have APIs for their status pages. You can automate notifications whenever a provider transitions from “operational” to “partial outage” or “major outage.”

3. Real-Time Alerts

   • Set up Slack or email alerts for error thresholds. If you see a 20% error rate on your LLM requests over 5 minutes, escalate an alert to investigate or switch providers.

4. Periodic Testing

   • Run cron jobs or synthetic monitors that ping each provider’s endpoint. If any check fails repeatedly, you know that “Anthropic might be down” or “OpenRouter is experiencing issues.”

SEO Tips for “OpenAI Down” or “Anthropic Down” Searches

Because thousands of developers search phrases like “OpenAI down?” or “Anthropic down?” when they suspect issues, you might want to:

• Publish a short real-time blog post or system status note with those keywords in the title: “Is OpenAI Down? How to Detect and Respond to GPT Outages.”

• Use relevant tags or categories on your site: #OpenAI, #Anthropic, #Claude, #LLMOutage, #LLMStatus.

• Keep content updated: Once the outage is resolved, update your post with final details, e.g., “Update: Service resumed at 2:13 PM PST.”

This not only helps your users find quick solutions but also positions your site as a go-to resource for real-time LLM updates.

Frequently Asked Questions (FAQ)

Q1: Does partial downtime mean I shouldn’t rely on LLM APIs?
A: Not necessarily. No modern web service has 100% uptime. Implementing fallbacks, caching, and multi-provider strategies helps maintain a high level of reliability.

Q2: Can I get an SLA (Service Level Agreement) for guaranteed uptime?
A: Some providers (like enterprise tiers of OpenAI or Anthropic) offer SLAs, but typically with disclaimers and partial credits for missed uptime. Always read the fine print.

Q3: How do I handle smaller providers with fewer official status tools?
A: Implement your own health checks (synthetic requests) and maintain good communication with their support or dev community. If you rely heavily on a less established provider, consider adding a second fallback.

Q4: I keep searching “Deepinfra down” but can’t find info.
A: Check if they have a Slack or Discord channel. Some smaller providers rely on direct communication over public status pages. Alternatively, consider a multi-LLM approach to mitigate uncertainty.

Q5: My application can’t failover easily from one LLM to another due to specialized prompting.
A: In that scenario, caching, local model inference (if feasible), or at least queuing requests for later reprocessing are your best bets. Fine-tune your fallback model to match your primary if possible.

Conclusion

Outages happen—even for the best LLM platforms. Whether you’re confronted with “OpenAI down,” “Anthropic down,” “DeepSeek slowdown,” or “OpenRouter partial outage,” you can minimize disruptions by planning ahead:

1. Monitor your provider’s status.

2. Implement failover solutions and fallback providers.

3. Cache results when possible.

4. Communicate openly with your users during downtime.

These strategies ensure your application remains stable (or at least degrades gracefully) whenever an LLM provider experiences issues. For an even smoother experience, consider using Requesty Router, which simplifies routing, fallback, and load balancing across multiple providers. That way, your app can continue delivering AI-driven features even if one service goes dark.

Looking for further help?

• Join our Discord community to get real-time support and share your experiences handling LLM outages.

• Explore our Requesty docs for detailed setup guides on multi-LLM routing, health checks, and advanced fallback.

Remember, no platform is outage-proof—but with smart planning and the right tools, you can keep your AI-driven application resilient in the face of disruptions.