Incident runbook when Anthropic Claude degrades in one region

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When Anthropic Claude slows down in a single region, your users in that geography see the impact immediately, but your team may not. Regional degradation is one of the hardest incidents to diagnose because aggregate latency metrics hide the problem, and customers report slowness before your dashboard does. This runbook walks you through detection, diagnosis, and recovery when Claude's latency spikes in one location.

Key Takeaway: Regional Claude degradation is a blind spot for teams relying on aggregate metrics. Implement multi-region synthetic probes with regional latency alerts to detect and mitigate incidents before your customers complain.

TL;DR

• Regional degradation often goes undetected until support tickets arrive; proactive multi-region monitoring catches it first.
• TTFB (time-to-first-byte) and TTFT (time-to-first-token) variance across regions is normal, but a >50% spike in one region signals a real incident.
• Use Observinio's 21-region probes to pinpoint affected geography, then route traffic away while the provider recovers.
• Set up email alerts on regional latency thresholds so you respond in minutes, not hours.
• Document baseline latency by region so you can spot anomalies faster in the next incident.

Why regional degradation happens

Claude's global infrastructure spans multiple data centers and edge networks. A regional outage can stem from several causes:

• Local infrastructure issues: Network congestion, hardware failures, or load imbalance in a specific region (e.g., us-west-2).
• Provider-side routing: Anthropic may reroute traffic during maintenance or incident mitigation, causing latency spikes in affected regions.
• Third-party CDN or ISP problems: Even if Anthropic's core is healthy, peering issues or backbone congestion between your region and their infrastructure slow requests.
• DDoS or traffic spike: Localized bot traffic or a surge from a nearby region can starve capacity for legitimate requests.

The key insight: degradation in one region does not mean the provider is down everywhere. Your team in Europe may see normal latency while Asia experiences a 2–3 second TTFB spike. Aggregate metrics bury this reality.

Detection: From reactive to proactive

Most teams discover regional Claude degradation the hard way:

1. Reactive path (slow): Customer in Singapore complains that chat is sluggish. Support escalates to engineering. Engineer runs a one-off curl to Claude and sees high latency. By then, 30–60 minutes have passed.
2. Proactive path (fast): Observinio's daily probes from 21 regions detect a TTFB spike in Singapore at 14:32 UTC. Alert fires to your on-call Slack channel. Engineer acknowledges within 2 minutes.

The difference is measurement discipline. Set up synthetic probes that:

• Run from geographically distributed locations (ideally matching your user base).
• Probe Claude every 60–300 seconds during business hours, less frequently off-peak.
• Log TTFB and TTFT separately, not just end-to-end latency.
• Compare against a rolling 7-day baseline so you detect even modest 20–30% spikes.

Observinio handles this automatically across 21 regions and 60-second cadence. When Claude's latency in one region exceeds your threshold (typically 30–50% above baseline), you get an email and Slack alert before your users complain.

Diagnosis: Isolating the problem

Once you get an alert, the next 5–10 minutes matter. Follow this diagnostic sequence:

Step 1: Confirm the alert is real

• Check Observinio's status dashboard for Claude in the affected region.
• Look at the last 30 minutes of latency trend. Is it a sustained spike or a transient blip?
• If latency recovered within 2–3 minutes, log it as a blip; no action needed.
• If latency is still elevated after 5 minutes, proceed to Step 2.

Step 2: Check whether it's regional or global

• Switch to Observinio's provider view for Anthropic Claude.
• Compare TTFB and TTFT across all 21 regions.
• If only one or two regions are slow, the problem is regional. Go to Step 3.
• If all regions are slow equally, Anthropic is having a global incident. Check /status and notify customers; your routing won't help.

Step 3: Check your own infrastructure

Before assuming Anthropic is at fault:

• Verify your application servers in the affected region are healthy (CPU, memory, network).
• Check egress routes: are requests actually leaving your region, or is traffic stuck locally?
• Run a direct curl or curl-like probe from the affected region to Claude's API endpoint. Is latency high in your test, or only in production?
• Compare latency from your region to Claude vs. latency from other providers (OpenAI, OpenRouter) in the same region. If everything is slow, the problem is your local ISP or egress, not Claude.

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Step 4: Verify with Claude's status page

• Check Anthropic's status page (if available) or their Twitter/Discord for incident announcements.
• If they acknowledge a regional issue, you've confirmed the root cause and can move to mitigation. If they do not, create a support ticket with Observinio's latency data as evidence.

Mitigation: Routing and communication

While the provider recovers, you have three mitigation options:

Option 1: Route traffic away from the affected region

If your architecture supports it, reroute Claude requests from the slow region to a nearby healthy region or to an alternative provider:

• Update your load balancer or router to skip the affected region for Anthropic Claude.
• Redirect those requests to OpenRouter's Claude endpoint or another LLM provider (e.g., OpenAI GPT-4).
• Latency will increase slightly due to longer geographic distance, but availability and user experience improve.
• This works best for applications that can tolerate a 100–200 ms latency increase in exchange for not timing out.

Option 2: Increase timeout thresholds temporarily

• If rerouting is not feasible, increase your client-side request timeout from (e.g.) 10 seconds to 15–20 seconds.
• Warn users that responses may take longer than usual.
• This buys time for Anthropic to recover without hard failures.
• Risk: Increased timeout can cascade into longer queue times and more resource consumption on your end. Use sparingly.

Option 3: Fallback to cached or degraded responses

• For non-critical features (e.g., content suggestions), serve stale cached responses or pre-generated fallback text.
• For critical features (e.g., chat completions), inform users that service is degraded and they may experience delays.
• This maintains availability while the incident resolves.

Communication template

Send this to your customers as soon as degradation is confirmed:

"We are investigating elevated latency for Claude in [REGION] starting at [TIME] UTC. We are monitoring the situation and will provide updates every 15 minutes. Current status: [DEGRADED / MITIGATED]. We apologize for the disruption."

Update every 15 minutes or when status changes.

Remediation: When Claude recovers

Once Observinio detects that latency in the affected region has returned to baseline for 5+ minutes:

• Revert routing changes: Direct traffic back to Claude in the affected region.
• Monitor closely for 30 minutes: Ensure latency stays stable and no secondary incident occurs.
• Document the incident: Log start time, duration, affected region(s), root cause (if known), and mitigation steps taken.
• Post-incident review (within 24 hours):
• Pull Observinio's latency graphs for the incident window.
• Compare against baseline to quantify the severity (e.g., "TTFB increased 75% for 22 minutes").
• Identify what Observinio's probes detected first (minute 0) vs. what your customers reported (minute 15).
• Discuss whether baseline thresholds need adjustment.
• Review whether your mitigation approach (rerouting, timeout, degradation) was effective.

Prevention and resilience

Multi-provider setup

• Do not rely on a single LLM provider for critical features. Implement fallback to a second provider (OpenAI, Cohere, local model).
• Use Observinio to compare Claude latency against alternatives weekly. If Claude is consistently slower in a region, consider preferring OpenAI for that geography.

Baseline documentation

• Run Observinio's probes for at least 2 weeks before setting alert thresholds. Collect 14 days of baseline latency data per region.
• Calculate the 95th percentile TTFB and TTFT for each region. Set alerts at 1.5× the 95th percentile.
• Example: If Singapore's baseline 95th percentile TTFB is 400 ms, set the alert threshold at 600 ms.

Weekly latency reviews

• Subscribe to Observinio's weekly summary email. Review latency trends by region and provider.
• Identify patterns (e.g., "Claude is always slower on Tuesdays in eu-west-1") and investigate root causes.
• Share trends with your product and infrastructure teams so they understand regional trade-offs when routing decisions are made.

FAQ

Wrapping up

Regional degradation is a blind spot for teams that rely on aggregate latency metrics. By deploying Observinio's probes across 21 regions and setting up targeted alerts, you shift from reactive support-ticket response to proactive 2–5 minute incident detection. Pair monitoring with a clear runbook, like the one above, and you reduce MTTR and keep your users in slow regions from churning.

The runbook above is a starting point. Customize the thresholds, mitigation steps, and fallback providers to match your architecture. And run through the runbook during a game day or table-top exercise so your team is ready when the next regional incident hits. With the right observability in place, you will likely detect and mitigate Claude degradation before your customers know something is wrong.

For real-time regional latency monitoring across Anthropic Claude and other major providers, set up probes on Observinio's status page or contact us for a tailored baseline and alert strategy.

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