When Claude Goes Down: Preparing for LLM API Incidents Before They Hit You

Every major LLM provider has experienced service degradation. OpenAI, Google, Anthropic, all of them. Elevated error rates, failed API calls, spiking latencies. If you're building products on top of these APIs and you haven't planned for this scenario, you're running on borrowed time.

Rather than sensationalizing any single incident, we want to talk about what these events look like in practice, why they're becoming a bigger deal, and what your team should be doing right now to stay resilient.

What "Elevated Error Rates" Actually Means

First, some clarity. An elevated error rate across multiple models isn't necessarily a full outage. It typically means a percentage of API requests are returning errors (HTTP 500s, timeouts, rate limit responses) while some traffic still gets through. The experience is inconsistent: one request succeeds, the next fails, the one after that hangs.

For developers, this is arguably worse than a clean outage. A total blackout is easy to detect and route around. Intermittent failures are harder to catch, harder to debug, and can silently degrade your product for end users who never see an error page but get garbage results or infinite loading spinners.

When multiple models from the same provider are affected simultaneously, the likely culprit sits upstream of the models themselves. Shared API gateways, authentication layers, load balancers, or regional infrastructure problems can all cause cross-model degradation. We won't speculate on specific root causes for any particular provider's incidents, because without a published post-mortem, that's just guessing dressed up as analysis.

Why This Matters More Than It Used To

A year or two ago, most LLM integrations were experiments. Demos. Internal tools. Today, Claude and its competitors power customer-facing features, revenue-generating workflows, and sometimes entire products. The blast radius of an API degradation event has grown dramatically.

When your chatbot stops responding, your document summarizer returns errors, or your code assistant goes silent, real users notice. Support tickets spike. Revenue takes a hit. And if you're in a regulated industry, you might have compliance implications too.

LLM APIs have quietly become critical infrastructure for many organizations, but most teams haven't built the redundancy that label demands.

What You Should Be Doing Right Now

Here's the practical playbook. None of this is theoretical. These are patterns we see working in production environments.

Build multi-provider fallback logic. If Claude is your primary, have a secondary provider configured and tested. Not just "we could switch to OpenAI." Actually tested, with prompt adjustments accounted for, because models respond differently to the same prompts. Implement circuit breakers. When error rates from a provider cross a threshold, automatically route traffic to your fallback. Don't wait for a human to notice and flip a switch at 2 AM. Cache aggressively where appropriate. For requests that are repeated often (common questions, standard summaries), cached responses can keep your product functional during an upstream outage. Monitor beyond uptime. Track latency percentiles, error rates, and response quality, not just "is the endpoint reachable." A 200 response with degraded output quality is still an incident for your users. Set honest SLA expectations internally. If your provider doesn't guarantee five-nines uptime (and none of the major LLM providers do), don't promise it to your stakeholders. Align expectations with reality. Have a status communication plan. When your AI features degrade, your users deserve a clear explanation. "We're experiencing issues with our AI provider" is better than silence.

The Bigger Picture

Every cloud service goes down sometimes. AWS has had major incidents. Google Cloud has had major incidents. This isn't an Anthropic-specific problem or an AI-specific problem. It's a distributed systems problem that the industry has been dealing with for decades.

What is relatively new is how many teams are treating LLM APIs with less operational rigor than they'd give a database or a payment processor. That gap needs to close.

The companies that will build durable AI-powered products aren't the ones who pick the best model. They're the ones who build systems that handle failure gracefully, regardless of which model they're calling.

Start building that resilience now. Not after the next incident.