API Uptime in 2026: Who's Most Reliable?

API downtime costs money. Stripe goes down, you can't take payments. Auth0 goes down, users can't log in. AWS goes down, and half the internet goes with it. Here's who's most reliable, how to measure it, and how to build resilience into your integrations.

Uptime Benchmarks

What "Five Nines" Means

Uptime	Downtime/Year	Downtime/Month	Realistic?
99%	3.65 days	7.3 hours	Unacceptable for production
99.9%	8.77 hours	43.8 minutes	Minimum for business APIs
99.95%	4.38 hours	21.9 minutes	Good
99.99%	52.6 minutes	4.38 minutes	Excellent
99.999%	5.26 minutes	26.3 seconds	Marketing claim (rarely real)

Reliability by Category

Payments (Business Critical)

Provider	Published SLA	Observed Uptime (2025)	Notable Incidents
Stripe	99.99%	~99.97%	Payment delays, dashboard outages
PayPal	99.95%	~99.9%	Checkout failures, settlement delays
Square	99.95%	~99.95%	Minor API latency spikes
Adyen	99.99%	~99.98%	Regional outages

Authentication

Provider	Published SLA	Observed Reliability
Auth0	99.99% (Enterprise)	Generally good, occasional login delays
Clerk	99.99%	Good track record
Firebase Auth	No published SLA	Tied to Google Cloud reliability
Okta	99.99%	High-profile incidents in 2024-2025

Cloud Infrastructure

Provider	Compute SLA	Observed	Impact of Outages
AWS	99.99% (per region)	~99.95%	Cascading — takes down many services
GCP	99.95-99.99%	~99.97%	Significant but less cascade
Azure	99.95-99.99%	~99.95%	Enterprise-impacting
Cloudflare	100% SLA (Enterprise)	~99.99%	Wide blast radius (CDN + DNS)

AI APIs

Provider	Published SLA	Observed Reliability
OpenAI	No public SLA	Variable — rate limits, capacity issues
Anthropic	No public SLA	Generally reliable, less capacity pressure
Google Gemini	99.9% (Cloud)	Tied to GCP reliability
Groq	No public SLA	Good for inference speed, capacity limits

How to Measure API Reliability

Key Metrics

Metric	What It Measures	Target
Uptime	Is the API responding?	>99.9%
Latency (P50)	Median response time	<200ms
Latency (P99)	Tail latency	<1s
Error rate	% of requests returning 5xx	<0.1%
Throughput	Requests per second at peak	Depends on SLA
MTTR	Mean time to recovery	<30 minutes
MTTD	Mean time to detect	<5 minutes

Monitoring Setup

// Simple API health check
async function checkApiHealth(name: string, url: string) {
  const start = Date.now();
  try {
    const res = await fetch(url, {
      signal: AbortSignal.timeout(5000),
    });
    const latency = Date.now() - start;

    return {
      name,
      status: res.ok ? 'up' : 'degraded',
      latency,
      statusCode: res.status,
      timestamp: new Date().toISOString(),
    };
  } catch (error) {
    return {
      name,
      status: 'down',
      latency: Date.now() - start,
      error: error.message,
      timestamp: new Date().toISOString(),
    };
  }
}

// Monitor critical APIs
const apis = [
  { name: 'Stripe', url: 'https://api.stripe.com/v1/charges' },
  { name: 'Auth0', url: 'https://YOUR_DOMAIN.auth0.com/authorize' },
  { name: 'OpenAI', url: 'https://api.openai.com/v1/models' },
];

Building Resilient Integrations

1. Circuit Breaker Pattern

class CircuitBreaker {
  private failures = 0;
  private lastFailure = 0;
  private state: 'closed' | 'open' | 'half-open' = 'closed';

  constructor(
    private threshold: number = 5,
    private timeout: number = 30000,
  ) {}

  async execute<T>(fn: () => Promise<T>, fallback?: () => Promise<T>): Promise<T> {
    if (this.state === 'open') {
      if (Date.now() - this.lastFailure > this.timeout) {
        this.state = 'half-open';
      } else if (fallback) {
        return fallback();
      } else {
        throw new Error('Circuit breaker is open');
      }
    }

    try {
      const result = await fn();
      this.failures = 0;
      this.state = 'closed';
      return result;
    } catch (error) {
      this.failures++;
      this.lastFailure = Date.now();
      if (this.failures >= this.threshold) {
        this.state = 'open';
      }
      if (fallback) return fallback();
      throw error;
    }
  }
}

const paymentCircuit = new CircuitBreaker(3, 60000);

await paymentCircuit.execute(
  () => stripe.charges.create({ amount: 2000, currency: 'usd' }),
  () => queuePaymentForRetry({ amount: 2000, currency: 'usd' }),
);

2. Retry with Exponential Backoff

async function withRetry<T>(
  fn: () => Promise<T>,
  options: {
    maxRetries?: number;
    baseDelay?: number;
    maxDelay?: number;
    retryableErrors?: number[];
  } = {}
): Promise<T> {
  const {
    maxRetries = 3,
    baseDelay = 1000,
    maxDelay = 30000,
    retryableErrors = [429, 500, 502, 503, 504],
  } = options;

  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await fn();
    } catch (error: any) {
      if (attempt === maxRetries) throw error;

      const statusCode = error.status || error.statusCode;
      if (statusCode && !retryableErrors.includes(statusCode)) throw error;

      const delay = Math.min(
        baseDelay * Math.pow(2, attempt) + Math.random() * 1000,
        maxDelay
      );
      await new Promise(resolve => setTimeout(resolve, delay));
    }
  }

  throw new Error('Unreachable');
}

3. Multi-Provider Failover

const aiProviders = [
  { name: 'anthropic', fn: () => callAnthropic(prompt) },
  { name: 'openai', fn: () => callOpenAI(prompt) },
  { name: 'google', fn: () => callGemini(prompt) },
];

async function aiWithFailover(prompt: string) {
  for (const provider of aiProviders) {
    try {
      return await provider.fn();
    } catch (error) {
      console.warn(`${provider.name} failed, trying next...`);
    }
  }
  throw new Error('All AI providers failed');
}

4. Graceful Degradation

async function getProductRecommendations(userId: string) {
  try {
    // Try AI-powered recommendations
    return await aiRecommendations(userId);
  } catch {
    try {
      // Fallback: popularity-based
      return await getPopularProducts();
    } catch {
      // Final fallback: static list
      return DEFAULT_PRODUCTS;
    }
  }
}

Status Page Best Practices

For API Providers

A good status page includes:

Element	Why
Real-time status per service	Users know which part is affected
Historical uptime (90 days)	Builds trust
Incident timeline	Shows response speed
Subscription notifications	Email/webhook alerts
API endpoint for status	Programmatic monitoring

Best status pages: Stripe (status.stripe.com), Cloudflare, GitHub, Vercel.

For API Consumers

Don't just check the status page — monitor yourself:

• Status pages can be delayed (5-15 min lag)
• Some issues affect your region/use case but not others
• Partial degradation may not trigger status page updates

Common Mistakes

Mistake	Impact	Fix
No monitoring on third-party APIs	Don't know it's down until users report	Monitor all critical API dependencies
Trusting the status page alone	Delayed updates, partial outages missed	Run your own health checks
No retry logic	One failed request = failed user action	Implement retry with backoff
Same retry for all errors	Retrying 400s wastes time	Only retry 429 and 5xx
No fallback plan	Vendor outage = your outage	Define degraded mode for each dependency
No SLA tracking	Can't hold vendors accountable	Log uptime, latency, error rates

SLA vs SLO vs SLI: Understanding the Difference

These three terms are frequently confused but describe different levels of the reliability conversation:

SLI (Service Level Indicator) is the measurement: the actual metric you're tracking. Uptime percentage, P99 latency, error rate, and request success rate are all SLIs. SLIs are facts — they describe what happened.

SLO (Service Level Objective) is the internal target: the threshold you're trying to hit. "P99 latency under 500ms" or "99.9% error-free responses per 30-day rolling window" are SLOs. SLOs are commitments your team makes to itself about what good looks like. Google's SRE book popularized the concept of an error budget — the amount of downtime you're allowed before breaching your SLO. A 99.9% monthly SLO gives you 43.8 minutes of downtime per month. When you've spent 40 minutes of that budget, the team knows it's in a high-stakes reliability posture.

SLA (Service Level Agreement) is the external contract: the formal commitment to customers or partners, usually with financial penalties for breach. SLAs are typically more lenient than SLOs — you set internal targets more aggressively than you commit to externally, giving you a buffer before customers are impacted. A provider with a 99.99% SLA might set an internal 99.995% SLO so they catch problems before they breach the contract.

As an API consumer, you care about SLAs because they're legally binding and trigger compensation (credits) on breach. As an API builder, you care about SLOs because they drive your reliability engineering priorities. Monitor both: your SLIs against your own SLOs, and the vendor SLIs against their published SLAs.

Building Your Own Reliability Baseline

Before you can set meaningful SLOs, you need to know what your current performance actually is. Most teams skip this step and set targets based on ambition ("we should have 99.99% uptime") rather than measurement. The result is SLOs that are immediately in breach, which causes teams to stop taking them seriously.

The practical approach: instrument first, set targets second. For each API or service your system depends on, log the HTTP status code, response latency, and timestamp for every request. Aggregate into 1-minute, 5-minute, and 1-hour windows. After 30 days of data, you have actual baseline performance — which often reveals surprising patterns. A payment API that shows 99.95% aggregate uptime might have 15 minutes of 100% failure every Tuesday morning during a batch settlement run. Average uptime hides that pattern; time-series data doesn't.

Tools for this: Datadog APM, Grafana + Prometheus, and AWS CloudWatch all provide the primitives. For simpler setups, even logging responses to a database and running nightly SQL aggregations provides enough data to set informed SLOs. The key is capturing enough data to understand not just average reliability but the shape of incidents: how often they occur, how long they last, and whether they're improving or worsening over time.

Incident Response When APIs Go Down

When a critical API dependency goes down, the response sequence matters:

The first five minutes are communication: update your status page, post in your team's incident channel, and if customer-facing — acknowledge the issue proactively rather than waiting for support tickets. Users who see "we know, we're on it" are more forgiving than users who discover the problem themselves.

The next task is mitigation, not root cause. Switch to fallback providers if available (AI inference → secondary provider; payments → queue for retry; auth → cached sessions). Deploy the degraded mode you prepared in advance. The goal is restoring customer functionality, even in reduced form, before understanding exactly what failed.

Root cause analysis happens after the incident is resolved. A blameless postmortem that documents what happened, when it was detected, what the impact was, what resolved it, and what would have prevented it is far more valuable than assigning fault. The output should be specific action items — improved monitoring, updated runbooks, added fallbacks — that reduce the likelihood or impact of the next incident.

Methodology

Uptime figures in provider comparison tables are estimates based on public incident reports, status page histories (via StatusGator and Better Uptime historical data), and published provider reports from 2024-2025. Exact uptime varies by region, account tier, and time period — figures represent approximate industry-observed performance, not independently audited SLA achievement. Monitor your own integrations for authoritative reliability data.

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Check API reliability ratings on APIScout — we track uptime, latency, and incident history for hundreds of APIs.

Related: Best API Monitoring and Uptime Services in 2026, API Analytics: Measuring Developer Experience 2026, Best API Monitoring Tools 2026