Website & API Downtime: The Complete Guide to Detection, Response, and Prevention (2026)

Quick Answer: When a website or API goes down, every second counts. The average cost of IT downtime is $5,600 per minute — and for large enterprises, that number can exceed $100,000 per minute. This guide covers the full lifecycle of downtime: how to detect it before your users do, how to respond when it happens, how to calculate the real business impact, and how to build systems that minimize the blast radius of future failures.

Whether you're a developer whose app depends on third-party APIs, an SRE managing production infrastructure, or a business owner who needs to know when your site goes dark — this guide has you covered.

What Is Downtime (And Why It Matters More Than Ever) {#what-is-downtime}

Downtime is any period when a system, service, website, or API is unavailable to its intended users. But in 2026, the definition has expanded beyond "the server is off."

Modern downtime includes:

• Total outage — the service returns no response at all (connection timeout, DNS failure)
• Partial degradation — the service responds but with errors (500 status codes, empty responses, incorrect data)
• Performance degradation — the service responds but unacceptably slowly (response times 10x normal)
• Functional failure — the service returns 200 OK but the functionality is broken (login works but checkout doesn't)
• Regional outage — the service works in some geographic regions but not others

The last three are insidious because traditional "up/down" monitoring misses them entirely. Your ping check says "200 OK" while your users in Europe can't complete a purchase.

Why Downtime Matters More in 2026

The dependency chain has never been deeper. A typical web application in 2026 depends on:

• 3-7 cloud infrastructure providers (AWS, Cloudflare, Vercel, etc.)
• 5-15 third-party APIs (Stripe for payments, Auth0 for authentication, Twilio for SMS, OpenAI for AI features)
• 2-5 SaaS tools in the critical path (databases, CDNs, email providers)

When any link in this chain breaks, your application breaks. The 2026 Anthropic outages, OpenAI degradations, and GitHub incidents demonstrated that even the most well-funded infrastructure teams have bad days — and when they do, thousands of downstream applications feel the pain.

The compounding effect: When Cloudflare goes down, it doesn't just affect Cloudflare customers — it affects every website using Cloudflare's CDN, which is roughly 20% of all websites. When AWS has an incident, entire ecosystems go offline. Understanding and preparing for downtime isn't optional anymore — it's a survival skill.

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The Real Cost of Downtime in 2026 {#cost-of-downtime}

Let's talk money, because that's what makes executives pay attention.

Industry Averages

The most-cited statistic comes from Gartner's research, updated for 2026 realities:

• Small businesses: $427 per minute of downtime
• Mid-market companies: $5,600 per minute
• Large enterprises: $100,000+ per minute
• Major cloud providers: $1M+ per minute during widespread outages

But these averages hide enormous variation. A 10-minute outage during Black Friday for an e-commerce site is catastrophically different from a 10-minute outage at 3 AM on a Tuesday.

How to Calculate YOUR Downtime Cost

Here's the formula that actually works:

Hourly downtime cost = (Revenue per hour) + (Productivity cost per hour) + (Recovery cost) + (Reputation damage estimate)

Let's break each component down:

1. Lost Revenue If your site generates $10,000/day in revenue, that's $417/hour. During a 2-hour outage, you lose $834 in direct revenue — but you also lose the customers who tried to buy, got an error, and went to a competitor. Studies show 88% of users are less likely to return after a bad experience.

2. Productivity Loss If 50 employees can't work because an internal API is down, and the average loaded cost is $75/hour per employee, that's $3,750/hour in lost productivity.

3. Recovery Costs Engineer time to diagnose and fix the issue, overtime pay, emergency vendor support, potential data recovery — these add up fast. A team of 4 senior engineers working 6 hours on an incident at $150/hour loaded cost = $3,600 in recovery costs alone.

4. Reputation and Customer Trust This is the hardest to quantify but often the largest cost. One study found that 37% of customers who experience downtime switch to a competitor. For a SaaS business with $100K MRR, losing even 5% of customers from a major outage = $5,000/month in recurring revenue gone permanently.

The SLA Math

Most cloud providers offer 99.9% uptime SLAs ("three nines"), which sounds impressive until you do the math:

• 99.9% uptime = 8.77 hours of allowed downtime per year
• 99.99% uptime = 52.6 minutes of allowed downtime per year
• 99.999% uptime = 5.26 minutes of allowed downtime per year

Here's the uncomfortable truth: if your application depends on 5 services each offering 99.9% uptime, your combined theoretical uptime is 99.9%^5 = 99.5% — which means 43.8 hours of potential downtime per year. This is why multi-provider redundancy and understanding SLAs vs. SLOs vs. SLIs is critical.

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How to Detect Downtime: 5 Methods Ranked {#detecting-downtime}

Detection speed is everything. The faster you know something is down, the faster you can respond. Here are the five primary detection methods, ranked from fastest to slowest.

1. Synthetic Monitoring (Fastest — Sub-Minute Detection)

Synthetic monitoring uses automated scripts that continuously test your website or API from multiple locations around the world. When a check fails, you're alerted immediately.

How it works:

• Monitoring service sends HTTP requests every 30-60 seconds
• Checks from 5+ geographic locations to avoid false positives
• Validates response code, response time, and response body content
• Triggers alerts via Slack, PagerDuty, SMS, or webhook on failure

Best for: Detecting total outages and performance degradation Detection time: 30 seconds to 2 minutes Blind spots: Can miss functional failures (API returns 200 but data is wrong)

Tools: Better Stack (formerly Uptime Robot competitor), Checkly, Datadog Synthetics, API Status Check

2. Status Page Aggregation (Fast — Minutes)

Instead of monitoring every third-party dependency yourself, use a status aggregator that watches the official status pages of services you depend on.

How it works:

• Aggregator monitors status pages of AWS, Stripe, OpenAI, GitHub, etc.
• When a service reports an incident, you're notified immediately
• Correlates multiple service incidents to identify cascade failures

Best for: Third-party API and cloud service outage awareness Detection time: 1-5 minutes (depends on how fast the provider updates their status page) Blind spots: Some providers are slow to acknowledge incidents — status pages can lie

Tools: API Status Check (monitors 200+ APIs), StatusGator, IsDown.app

3. Real User Monitoring (RUM) (Moderate — Minutes)

RUM collects performance and error data from actual users' browsers and devices.

How it works:

• JavaScript snippet embedded in your pages
• Reports page load times, JS errors, failed network requests
• Aggregates data by geography, device type, browser

Best for: Detecting user-facing performance issues and regional outages Detection time: 2-10 minutes (needs enough data points to distinguish signal from noise) Blind spots: No data during zero-traffic periods (night hours, new products)

4. Log Monitoring & Alerting (Moderate — Minutes)

Your application logs contain early warning signals — if you're watching them.

How it works:

• Application logs errors, slow queries, failed API calls, timeout events
• Log aggregation tool (Datadog, Grafana Loki, ELK stack) processes logs in near-real-time
• Alerting rules trigger when error rates exceed thresholds

Best for: Detecting subtle degradation patterns before they become full outages Detection time: 2-15 minutes (depends on log ingestion delay and alert thresholds) Blind spots: Only catches what you log — silent failures go unnoticed

5. User Reports (Slowest — But Catches Everything Else)

Sometimes the first person to notice an outage is a user tweeting "is [service] down?" or filing a support ticket.

How it works:

• Customer support tickets spike
• Social media mentions increase
• DownDetector-style crowdsourced reports appear

Best for: Catching the issues that every automated tool missed Detection time: 15-60+ minutes Blind spots: By the time users report it, you've already lost trust

The Optimal Detection Stack

Don't rely on just one method. The best setup combines:

1. Synthetic monitoring for your own services (sub-minute detection)
2. Status aggregation for third-party dependencies (API Status Check)
3. RUM for user-facing experience validation
4. Log alerting for internal system health
5. Social listening as a final safety net

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Is It Down Right Now? How to Check Any Website or API {#check-if-down}

When you suspect a service is having issues, here's a systematic approach to verify.

Step 1: Check from Multiple Locations

The issue might be regional. Use these tools to check from different geographic points:

• API Status Check Website Down Checker — Tests from multiple regions simultaneously
• API Status Check API Dashboard — Real-time status for 200+ major APIs
• curl from your terminal — curl -o /dev/null -s -w "%{http_code} %{time_total}s\n" https://example.com

Step 2: Check the Service's Status Page

Most major services maintain public status pages:

• OpenAI Status | GitHub Status | AWS Status
• Cloudflare Status | Stripe Status | Vercel Status
• Slack Status | Shopify Status | Twilio Status

⚠️ Important caveat: Status pages are often updated AFTER the outage has already impacted users. Some providers take 10-30 minutes to acknowledge incidents. That's why independent monitoring like API Status Check is essential — we detect outages before the official status page updates.

Step 3: Check Social Media and Community Reports

• Twitter/X: Search for "[service name] down" or "[service name] outage"
• Reddit: Check relevant subreddits
• DownDetector: Crowdsourced outage reports

Step 4: Test the Specific Functionality

If the service appears "up" but something isn't working:

• API: Test the specific endpoint that's failing, not just the base URL
• Website: Try the specific user flow that's broken (login, checkout, search)
• Regional: VPN to different regions to test geographic issues

Popular Services to Monitor

We track real-time status for the services people search for most:

AI & ML Services: ChatGPT/OpenAI · Claude/Anthropic · Gemini · Grok · Character.AI · Cursor · GitHub Copilot · DeepSeek

Developer Tools: GitHub · Vercel · Supabase · Heroku · Jira · Airtable

Cloud Platforms: AWS · Cloudflare · GCP · Snowflake

Consumer Services: Hulu · Disney+ · DoorDash · Shopify · Pinterest · Slack

Payments & Business: Stripe · Chase · Square · Salesforce · Okta

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The Anatomy of a Major Outage: Real-World Case Studies {#outage-case-studies}

Understanding how major outages unfold helps you prepare for your own. Here are three instructive examples from recent history.

Case Study 1: The Cloudflare June 2024 Outage

What happened: A configuration change to Cloudflare's network caused a cascading failure that took down millions of websites globally.

Timeline:

• T+0: Configuration deployed
• T+2 min: Automated monitoring detected anomalies
• T+5 min: Engineers began investigating
• T+15 min: Root cause identified
• T+45 min: Rollback completed, services restoring
• T+90 min: Full recovery

Lesson: Even with world-class infrastructure, a single bad configuration can cascade. Cloudflare's sub-2-minute detection was excellent, but recovery still took 90 minutes because rollbacks at global scale are slow.

What you should do: Don't rely solely on Cloudflare (or any single provider) for critical path infrastructure. Use multi-CDN strategies for truly critical services. Monitor Cloudflare's status independently.

Case Study 2: OpenAI's Recurring 2026 Degradations

What happened: Throughout early 2026, OpenAI experienced frequent API degradations — not full outages, but elevated error rates and increased latency that affected applications using ChatGPT and the API.

Pattern:

• Services showed "Operational" on the status page
• API error rates climbed from baseline 0.1% to 5-15%
• Response latencies doubled or tripled
• Applications using OpenAI's API started timing out

Lesson: This is the most dangerous type of downtime — partial degradation that official monitoring doesn't flag. Applications need circuit breakers, fallback providers, and timeout handling specifically for AI API dependencies. We documented these incidents in detail: Anthropic outage analysis.

What you should do: Implement circuit breaker patterns for all AI API integrations. Set aggressive timeouts. Have fallback options (if OpenAI is slow, try Anthropic; if both fail, show cached results).

Case Study 3: AWS US-East-1 Incidents

What happened: AWS's US-East-1 region has historically been the most incident-prone, simply because it's the oldest and most heavily used region.

Pattern: When US-East-1 has issues, it creates a domino effect:

1. Services hosted in US-East-1 fail
2. AWS services that have hard dependencies on US-East-1 (like IAM, Route53 console) are also affected
3. Developers can't even access the AWS console to diagnose the issue
4. Even the AWS status page (hosted on... AWS) sometimes fails to update

Lesson: Critical infrastructure should be multi-region, and your monitoring and communication channels should NOT depend on the same infrastructure you're monitoring.

What you should do: Deploy critical services across multiple AWS regions, use multi-cloud strategies for true resilience, and ensure your monitoring tools run independently of your production infrastructure.

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Incident Response: What to Do When Things Go Down {#incident-response}

When downtime hits, having a practiced response is the difference between a 15-minute blip and a 4-hour catastrophe. Here's the playbook.

The First 5 Minutes (Triage)

1. Confirm the outage — Is it real or a false positive? Check from multiple sources.
2. Assess scope — Total outage or partial? Which users/regions are affected?
3. Classify severity — SEV1 (total outage, revenue impact), SEV2 (partial degradation), SEV3 (minor, limited impact)
4. Assign incident commander — One person owns coordination. No committees.
5. Open a war room — Slack channel, Zoom call, whatever your team uses.

The Next 15 Minutes (Diagnose)

6. Check the dependency chain — Is it your code, your infrastructure, or a third-party? Check API Status Check for third-party status.
7. Review recent changes — Was anything deployed in the last hour? Check your CI/CD pipeline.
8. Check metrics dashboards — Error rates, latency, CPU, memory, disk, network.
9. Read the logs — Filter for errors in the last 30 minutes.
10. Communicate — Update your status page. Even "We're aware and investigating" is better than silence.

The Recovery Phase (Fix)

11. Rollback first, root-cause later — If a recent deployment caused it, roll back immediately. Don't debug in production during an outage.
12. If it's a third-party — Activate your fallback plan. Switch DNS, enable circuit breakers, serve cached responses, or display a maintenance page.
13. Test the fix — Before declaring "all clear," verify from multiple locations and test the specific functionality that was broken.
14. Monitor closely for 30 minutes — Outages have a nasty habit of recurring within the first hour after "recovery."

The Postmortem (Learn)

15. Write a blameless incident postmortem within 48 hours
16. Identify action items — Not "be more careful" but specific, measurable improvements: "Add circuit breaker to OpenAI integration," "Deploy to second AWS region," "Add synthetic monitoring for checkout flow"
17. Track follow-through — Action items without deadlines and owners are wishes, not commitments

For a complete incident response framework, read our Incident Response Playbook for Engineering Teams.

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The Downtime Prevention Stack: Building for Resilience {#prevention}

You can't prevent all downtime, but you can dramatically reduce its frequency and blast radius.

Redundancy at Every Layer

DNS: Use multiple DNS providers. If Cloudflare DNS goes down, Route53 takes over. CDN: Multi-CDN setup with automatic failover. Compute: Multi-region deployment at minimum. Multi-cloud for critical services. Database: Read replicas, automatic failover, cross-region replication. API Dependencies: Multiple providers for critical functions (payments, auth, AI).

Circuit Breakers for API Dependencies

When a third-party API starts failing, your application shouldn't keep hammering it — that makes things worse for everyone. Implement circuit breakers:

// Pseudocode: Circuit breaker pattern
const breaker = new CircuitBreaker(openAIClient, {
  failureThreshold: 5,      // Open after 5 failures
  resetTimeout: 30000,       // Try again after 30 seconds
  timeout: 5000,             // Individual request timeout
  fallback: cachedResponse   // Return cached data when circuit is open
});

Graceful Degradation

Design your application so that when a non-critical dependency fails, the rest of the application still works:

• AI features down? Show a "temporarily unavailable" message but keep the rest of the app working
• Payment processor down? Let users add to cart but show "checkout temporarily offline"
• Analytics down? Your users don't care — just skip the tracking calls silently

Health Checks That Actually Work

Bad health check: GET /health → 200 OK (just checks the server is responding)

Good health check: GET /health → 200 OK with body:

{
  "status": "healthy",
  "database": "connected",
  "redis": "connected",
  "openai_api": "degraded (latency: 2400ms)",
  "stripe_api": "connected",
  "uptime": "14d 6h 32m"
}

The difference? The good health check verifies actual dependencies, not just that your web server process is alive.

Chaos Engineering (Break Things on Purpose)

Netflix's Chaos Monkey philosophy: if you break things intentionally during business hours with engineers watching, you'll find weaknesses before they find you at 3 AM.

Start small:

1. Kill a single container — Does your orchestrator restart it? How long does it take?
2. Block access to one API — Does your circuit breaker activate? Does the fallback work?
3. Simulate high latency — Does your application timeout gracefully or hang forever?
4. Take down a region — Does traffic failover to the backup region?

Deployment Safety

Most outages are caused by deployments. Protect yourself:

• Canary deployments — Roll out to 1% of traffic first, monitor for 15 minutes
• Feature flags — Decouple deployment from activation. Ship code dark, enable gradually.
• Automatic rollback — If error rates spike within 5 minutes of deployment, roll back automatically
• Deploy during low-traffic windows — Not during peak hours, not on Fridays

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API-Specific Downtime: Why APIs Fail and What to Do About It {#api-downtime}

APIs have unique failure modes that deserve special attention.

The Top 5 Causes of API Downtime

1. Rate Limiting (429 Too Many Requests) You hit the API's rate limit. This isn't technically downtime — the API is working fine, you're just sending too many requests. Solution: implement rate limiting on your end, use exponential backoff with jitter, and cache aggressively.

2. Authentication Failures (401/403) Expired tokens, rotated API keys, or permission changes. These often look like "the API is down" to your users but it's actually a credential issue. Solution: implement token refresh logic, monitor authentication success rates, set up alerts for auth failure spikes.

3. Timeout Errors The API is responding, just too slowly. Your client gives up. This is the most common failure mode for AI APIs like OpenAI and Anthropic during high-demand periods. Solution: set reasonable timeouts (5-30 seconds depending on the API), implement circuit breakers, have fallback responses ready.

4. Payload/Schema Changes The API changes its response format without warning. Your deserialization breaks. Solution: validate API responses before processing, don't assume fields exist, test against the API's staging environment, subscribe to the API's changelog.

5. Infrastructure Failures The provider's actual infrastructure is down. This is the least common but most impactful. Solution: monitor the provider's status page via API Status Check, have a multi-provider strategy for critical APIs.

Building API Resilience

For a deep dive into managing third-party API dependencies, read our Complete API Dependency Monitoring Strategy.

The key principles:

• Never trust a single API provider for critical functionality
• Always set timeouts — an API call without a timeout is a ticking time bomb
• Cache what you can — reduce your dependency on real-time API availability
• Monitor independently — don't rely on the provider to tell you they're down
• Handle errors gracefully — 500 errors, timeouts, and empty responses should all have defined behavior in your code

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Monitoring Tools Compared: Free vs. Paid in 2026 {#monitoring-tools}

Here's an honest comparison of the monitoring landscape.

Free Tier Options

API Status Check (Free)

• Real-time status monitoring for 200+ major APIs
• Website down checker tool
• Outage history and incident tracking
• Best for: Checking third-party API status without setting up your own monitoring

UptimeRobot (Free tier: 50 monitors)

• HTTP, keyword, ping, and port monitoring
• 5-minute check intervals
• Email and webhook alerts
• Best for: Basic uptime monitoring on a budget

Freshping (Free tier — note: Freshping has been sunsetting features)

• Check the latest status before relying on Freshping

Paid Options Worth Considering

Better Stack ($24/month+)

• Uptime monitoring + incident management + status pages + log management
• 30-second check intervals
• Integrated on-call rotation
• Best for: All-in-one monitoring and incident management
• Read our comparison: Better Stack vs API Status Check

Datadog ($15/host/month+)

• Full observability platform: metrics, logs, traces, synthetics, RUM
• Enterprise-grade but complex and expensive
• Best for: Large teams with dedicated SRE resources

Checkly ($40/month+)

• Synthetic monitoring focused on APIs and web flows
• Playwright-based browser checks
• Best for: Developer teams who want code-first monitoring

PagerDuty ($21/user/month+)

• Industry-standard incident management and on-call rotation
• Alert routing, escalation policies, postmortem tools
• Best for: Teams that need robust on-call management
• See also: Best Incident Management Software 2026

Which Monitoring Stack Should You Choose?

Solo developer or small startup: API Status Check (free, for third-party monitoring) + UptimeRobot free tier (for your own services) + Slack alerts

Growing team (5-20 engineers): Better Stack (monitoring + status page + incidents) + API Status Check (third-party API visibility)

Enterprise (50+ engineers): Datadog or Grafana Cloud (full observability) + PagerDuty (incident management) + Statuspage.io or Instatus (public status page)

For a comprehensive tool comparison, see our Best API Monitoring Tools guide and API Monitoring Comparison 2026.

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Downtime Communication: Status Pages and Customer Trust {#communication}

How you communicate during downtime determines whether customers forgive you or leave.

The Golden Rules of Outage Communication

1. Acknowledge fast. Even before you know the root cause, tell people you're aware: "We're investigating increased error rates affecting [feature]. We'll update within 15 minutes."

2. Update regularly. Every 15-30 minutes during a major incident. Silence breeds panic.

3. Be honest. "We deployed a bad configuration change" earns more trust than "we experienced an unforeseen infrastructure event."

4. Share what you know AND what you don't. "We've identified the issue is related to our database cluster. We don't yet know the root cause, but we're working on restoring service."

5. Provide a clear resolution timeline — or be honest that you don't have one. "We expect to restore service within 1 hour" is better than "we're working on it" but only if you actually can estimate.

Your Status Page

Every production service should have a public status page. Options include:

• Atlassian Statuspage.io — Industry standard, starts at $29/month
• Instatus — Modern alternative, starts at $20/month
• Better Stack — Included with their monitoring product
• DIY with API Status Check — Use our status page features for free

For a deeper analysis of why some status pages are misleading and how to build one that actually helps users, read Why Status Pages Lie.

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Frequently Asked Questions {#faqs}

How do I check if a website is down for everyone or just me?

Use a tool that checks from multiple geographic locations simultaneously. API Status Check's Website Down Checker tests from several regions and tells you whether the site is down globally or if it's a local issue (your ISP, DNS cache, or network).

What's the difference between downtime and degraded performance?

Downtime means the service is completely unavailable — it returns no response or error codes. Degraded performance means the service is responding but much slower than normal or with elevated error rates. Both impact users, but degraded performance is harder to detect because basic health checks show "up."

How much downtime is acceptable?

It depends on your SLA commitments and business requirements. "Three nines" (99.9%) uptime means about 8.7 hours of allowed downtime per year. "Four nines" (99.99%) means about 52 minutes. Most modern SaaS companies target at least 99.9%. Read our SLA vs. SLO vs. SLI guide for a complete breakdown.

Should I monitor third-party APIs separately from my own infrastructure?

Absolutely yes. Your own monitoring infrastructure (Datadog, Grafana, etc.) tells you about YOUR systems. But you also depend on APIs from OpenAI, Stripe, GitHub, and dozens of others. Use a status aggregation service like API Status Check to monitor those dependencies independently. When ChatGPT goes down, you should know before your users do.

What causes most website outages?

In order of frequency: (1) deployment/configuration changes, (2) traffic spikes and capacity limits, (3) third-party dependency failures, (4) infrastructure failures (hardware, network), (5) security incidents (DDoS attacks). The majority are human-caused (bad deploys, misconfigurations) rather than infrastructure failures.

How do I prevent downtime for my API?

The core strategies: implement health checks, use circuit breakers for dependencies, deploy across multiple regions, use canary deployments, set up comprehensive monitoring, practice incident response, and run regular chaos engineering experiments. No single technique prevents all downtime — it's the combination that builds true resilience.

What should I do when a third-party API I depend on goes down?

Immediately: activate your fallback plan (cached responses, alternative provider, graceful degradation). Short-term: monitor the provider's status page and API Status Check for recovery updates. Long-term: implement circuit breakers, add redundant providers for critical integrations, and improve your caching strategy to reduce real-time API dependency.

How do I set up downtime alerts?

Start with synthetic monitoring — configure automated checks that run every 1-5 minutes and alert you via Slack, email, or PagerDuty when your site or API fails to respond. For third-party dependencies, use API Status Check to get notified when services you depend on have incidents. Combine with log-based alerting for internal system health.

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The Bottom Line

Downtime is inevitable. Your response to it is not.

The organizations that handle downtime best share three qualities: they detect fast (automated monitoring, not user reports), they respond with a practiced playbook (not panicked improvisation), and they learn from every incident (blameless postmortems that drive real improvements).

Start with the basics:

1. Set up synthetic monitoring for your critical endpoints
2. Use API Status Check to monitor your third-party dependencies
3. Write a one-page incident response runbook
4. Practice it before you need it

The best time to prepare for downtime was yesterday. The second best time is right now.

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Want to monitor the APIs your application depends on? API Status Check tracks 200+ services in real-time so you know about outages before your users do.