Most Reliable APIs of 2026 — Uptime Rankings for Developers

by API Status Check

TLDR: See which APIs had the best uptime in 2026 based on real monitoring data from API Status Check. We rank the most reliable developer APIs by response time, uptime percentage, and incident frequency.

TLDR: Based on 2026 incident data, the most reliable APIs are Cloudflare (99.99%), AWS (99.97%), and Fastly (99.96%), while OpenAI, Anthropic, and newer AI providers had more frequent outages. Use this data-driven ranking to inform architecture decisions and choose dependencies wisely.

Most Reliable APIs of 2026 — Uptime Rankings for Developers

If you ship software that depends on third-party APIs — and let's be honest, that's all of us — then reliability isn't a nice-to-have. It's the foundation your app stands on. When Stripe goes down, your checkout breaks. When GitHub goes down, your CI/CD pipeline stops. When OpenAI goes down, your AI features return blank stares.

We built apistatuscheck.com to track this stuff so you don't have to obsessively refresh status pages. We currently monitor 20 APIs (expanding to 50 soon), pulling real data from their public statuspage.io endpoints every few minutes.

This post is our first annual reliability report. We analyzed incident data from the public status pages of the most popular developer APIs, covering the period from late 2025 through January 2026. We looked at incident frequency, severity (minor/major/critical), time-to-resolution, and overall patterns.

Let's see who earned their SLA and who needs to have a chat with their SRE team.

2. Linear

Category: Project Management / Dev Tools
Estimated Uptime: ~99.96%
Status Page: linearstatus.com

Linear quietly posts some of the best uptime numbers in the industry. Their status page shows 99.98% uptime for the US region application, 99.96% for the API, and 99.98% for integrations over the past 90 days. The EU region is only slightly behind at 99.93%.

Why they're reliable: Linear is a newer product built on modern infrastructure without legacy debt. Their team is small but extremely focused on performance — the app itself is famously fast, and that same engineering discipline extends to their backend reliability.

Notable incidents: Essentially none worth mentioning. That's the best kind of incident report.

3. Slack

Category: Communication
Estimated Uptime: ~99.98%
Status Page: status.slack.com

As of our latest check, Slack's status API returns a clean "status": "ok" with zero active incidents. For a platform that millions of developers use for daily communication, that's impressive. Their most recent incident update resolved cleanly on January 22, 2026.

Why they're reliable: Salesforce's acquisition brought enterprise-grade infrastructure backing. Slack has had years to mature their systems, and it shows. Their real-time messaging architecture has been battle-tested at massive scale.

4. SendGrid (Twilio)

Category: Email API
Estimated Uptime: ~99.95%
Status Page: status.sendgrid.com

SendGrid's incidents in January 2026 were refreshingly minor. A Gmail delivery latency issue on January 24 was caused by Gmail itself, not SendGrid — and they were transparent about it. A delayed Event Webhook issue on January 23 was resolved within about an hour.

Why they're reliable: Email infrastructure is mature technology, and SendGrid has been doing this for over a decade. Their incidents tend to be carrier-side issues (like the Gmail delay) rather than core platform failures. That's a sign of solid engineering.

Notable incident: The January 24 Gmail delivery latency issue was actually a Google problem — emails were accepted by Gmail's servers but delayed in reaching inboxes. SendGrid proactively communicated this even though it wasn't their fault. Good transparency.

5. Vercel

Category: Deployment / Hosting
Estimated Uptime: ~99.95%
Status Page: vercel-status.com

Vercel had a few minor hiccups in January 2026 — delayed dashboard data on January 26 (affecting Speed Insights, Web Analytics, and the Dashboard for about 3 hours) and a brief domain purchase failure on January 23 (resolved in under an hour). But their core deployment and hosting infrastructure held solid.

Why they're reliable: Vercel's edge-first architecture means your deployments are distributed globally. Dashboard issues don't affect your live sites. Their incidents tend to be in ancillary services (analytics, domain registration) rather than the core hosting platform.

Notable: The January 26 dashboard data delay affected monitoring features but not actual site delivery — an important distinction.

6. Datadog

Category: Monitoring / Observability
Estimated Uptime: ~99.93%
Status Page: status.datadoghq.com

Here's the irony: your monitoring tool went down. Datadog had a critical incident on January 22 — "Web Application Not Loading" — which affected their entire web interface. It was resolved in about 37 minutes, but for a monitoring platform, any outage hits different.

Why they still rank well: Despite the critical incident, Datadog's core data pipeline (metrics ingestion, alerting, APM) remained operational. The outage was limited to the web UI. Their agent infrastructure continued collecting and processing data, meaning your alerts still fired even if you couldn't see the dashboard. That architectural separation is smart.

Notable incident: January 22 critical outage — web application completely down for ~37 minutes. Data collection continued normally.

7. Netlify

Category: Deployment / Hosting
Estimated Uptime: ~99.93%
Status Page: netlifystatus.com

Netlify had a couple of minor incidents in late January 2026: increased function latency on January 26 (14 minutes) and UI errors the same day (about 25 minutes). They also experienced build failures on January 22 caused by an upstream GitHub outage — which honestly isn't their fault.

Why they're reliable: Like Vercel, Netlify's static hosting is inherently resilient. CDN-delivered sites don't go down easily. Their incidents tend to be in build systems or the admin UI, not in serving your actual website.

Notable: The January 22 build failure cascade was caused by GitHub's authentication outage. This is a great example of why monitoring your dependencies matters — Netlify's own infrastructure was fine.

8. GitHub

Category: Dev Tools / Source Control
Estimated Uptime: ~99.90%
Status Page: githubstatus.com

GitHub has been busier on the incident front. In January 2026 alone, we tracked:

  • Jan 26: Windows runner regression for public repos (~4.5 hours, 11% failure rate on affected runners)
  • Jan 25: Repo creation disruption (~7 hours, error rate peaked at 55%, caused by database latency)
  • Jan 22: Authentication service disruption (~50 minutes, API error rates up to 22.2%, git HTTP errors up to 10.8%)
  • Jan 21: Copilot policy pages timing out (~1.5 hours)

Why they still rank here: Despite the frequency, most incidents are minor and affect specific subsystems rather than the entire platform. GitHub's transparency is excellent — they publish detailed post-incident reviews with exact error rates and root causes. The January 25 repo creation incident, for example, included a full breakdown: "25% average error rate, peaking at 55%" caused by "increased latency on the repositories database."

Notable: GitHub's detailed incident reports are a masterclass in transparency. They include specific percentages, timelines, and root causes.

9. Anthropic (Claude API)

Category: AI / LLM
Estimated Uptime: ~99.85%
Status Page: status.claude.com

The Claude API has been experiencing a steady drumbeat of minor incidents:

  • Jan 27: Degraded performance on Claude Console (~40 minutes)
  • Jan 27: Elevated errors on Claude Haiku 3.5 (~33 minutes)
  • Jan 25-26: Increased error rate for Opus 4.5 (~30 hours to fully resolve)

The incidents are mostly short-lived, but they're frequent. The Opus 4.5 error rate issue on January 25 stands out — it took over a day to fully resolve, though it was marked as monitoring after about 2 hours.

Why they rank here: Anthropic is scaling rapidly, and the Claude API is under enormous demand. Short recovery times show good operational practices, even if incident frequency is higher than more mature platforms. The issues tend to be model-specific (Haiku 3.5, Opus 4.5) rather than platform-wide.

10. Twilio

Category: Communications
Estimated Uptime: ~99.85%
Status Page: status.twilio.com

Twilio's incidents are frequent but almost always carrier-related: SMS delivery delays to specific networks in specific countries. In late January 2026 alone:

  • SMS delivery delays to Entel in Chile
  • SMS delivery report delays to Telstra in Australia
  • SMS/MMS delivery delays to GCI network in the US

Why they rank here: Twilio's core platform is solid — these aren't Twilio infrastructure failures, they're carrier network issues. But from a developer's perspective, if your SMS doesn't get delivered, the root cause doesn't matter much. Twilio is transparent about distinguishing between platform issues and carrier issues, which is helpful for debugging.

APIs That Struggled

Not every API had a great start to 2026. Here are the ones that had developers reaching for their incident response playbooks.

OpenAI

Status Page: status.openai.com

OpenAI has been the busiest status page we monitor. In January 2026 alone, we counted 11+ incidents in under four weeks:

Date Incident Impact
Jan 28 Brief issue with image generation Minor
Jan 27 Elevated Codex error rate Minor
Jan 26 ChatGPT availability degraded Minor
Jan 22 Codex GitHub issues Minor
Jan 14 Elevated error rates for ChatGPT Minor
Jan 12 Connectors/Apps unselectable Minor
Jan 8 Increased error rate for image prompts Major
Jan 8 High error rate for DALL-E Minor
Jan 8 Codex cloud tasks failing Minor
Jan 7 Elevated Responses API errors Minor
Jan 6 ChatGPT workspace member issues Minor
Jan 6 GPT-5.1 Codex Max elevated errors Minor

That's roughly one incident every 2-3 days. Most are minor and resolve within an hour, but the January 8 image prompts issue was classified as major — affecting both ChatGPT and the API for image-based prompts.

The pattern is clear: OpenAI is shipping incredibly fast (new models, Codex, image generation, connectors) and reliability is paying the price. This is the classic speed-vs-stability tradeoff, and right now speed is winning.

If you depend on OpenAI's API: Build robust retry logic, implement fallbacks, and don't assume any single request will succeed.

Cloudflare

Status Page: cloudflarestatus.com

This one surprised us. Cloudflare — the company that literally protects other companies from outages — had a rough stretch:

  • Jan 28 (ongoing): Network performance issues affecting LAX, London, and São Paulo
  • Jan 28: Network performance issues in Singapore (resolved in ~1 hour)
  • Jan 27-28: Major network degradation in Chicago (~7 hours)
  • Multiple regional PoP degradations in the weeks prior

The January 27 Chicago incident is notable: classified as major impact, it lasted about 7 hours. Traffic was "automatically rerouted to nearby regions" but the degradation was significant enough to warrant a major classification.

Context: Cloudflare operates one of the largest networks in the world with 300+ data centers. Regional issues are somewhat expected at that scale, and their architecture is designed to route around problems. But if your users are concentrated in an affected region, "traffic rerouted" might still mean noticeable latency.

Atlassian (Jira, Confluence)

Status Page: status.atlassian.com

The October 2025 incident still casts a long shadow. Triggered by an AWS DynamoDB outage in us-east-1, Atlassian products experienced elevated error rates and degraded performance for nearly 22 hours (Oct 20 06:48 to Oct 21 04:05 UTC). The postmortem revealed cascading failures across DynamoDB, EC2, and Network Load Balancer.

Key takeaway: Even massive, well-resourced companies can be brought down by cloud provider dependencies. Atlassian's products are deployed across multiple AWS regions, but cross-region service calls created blast radius expansion during the AWS failure.

Trends We're Seeing

AI APIs Are the Least Reliable Category

This is the clearest pattern in our data. OpenAI averages an incident every 2-3 days. Anthropic sees multiple incidents per week. These companies are shipping new models, new features, and scaling to unprecedented demand simultaneously. Something has to give, and right now it's stability.

The numbers:

  • OpenAI: 11+ incidents in 28 days (~1 every 2.5 days)
  • Anthropic: 3+ incidents in 3 days (late January snapshot)
  • Traditional SaaS (Linear, Stripe): 0-1 incidents per month

If you're building on AI APIs, plan for failure. It's not a question of if but when.

Payment APIs Are Rock Solid

Stripe's status page is practically empty. Payment infrastructure benefits from decades of financial systems engineering practices, strict regulatory requirements, and the existential motivation of "if we go down, merchants lose real money in real time." There's no "we'll fix it in the next sprint" when you're processing payments.

Infrastructure APIs Fail Regionally, Not Globally

Cloudflare's incidents consistently affect specific Points of Presence (Chicago, Singapore, London) rather than the entire network. AWS outages hit specific regions (the October 2025 us-east-1 incident). This is by design — modern infrastructure is built to contain failures — but it means your experience depends heavily on where your traffic originates.

SaaS Tools Fail on Ancillary Services

Vercel's core hosting stays up while the dashboard has issues. Datadog's data pipeline keeps running while the web UI goes down. Netlify's CDN delivers your site even when builds fail. Modern SaaS companies are getting better at architectural separation, ensuring that management plane failures don't cascade to the data plane.

Upstream Dependencies Are a Hidden Risk

The most interesting incidents in our data weren't self-inflicted:

  • Netlify's build failures were caused by GitHub's authentication outage
  • Atlassian's 22-hour incident was triggered by AWS DynamoDB
  • SendGrid's delivery delays were caused by Gmail

Your reliability is only as good as your weakest dependency — and you probably have more dependencies than you think.

How to Protect Yourself

Here's what we recommend based on the patterns we've observed.

1. Monitor Your Dependencies

Don't wait for your users to tell you that an upstream API is down. Set up automated monitoring that checks the status of every API you depend on.

This is exactly why we built apistatuscheck.com — it watches 20 APIs (soon 50) and lets you know when something's off before it becomes a support ticket.

2. Implement Circuit Breakers

When an API starts failing, stop hammering it. A circuit breaker pattern detects failures and short-circuits requests for a cooldown period. This:

  • Prevents cascade failures in your system
  • Reduces load on the struggling API (helping it recover faster)
  • Gives your users a faster failure message instead of a timeout
// Simple circuit breaker concept
class CircuitBreaker {
  private failures = 0;
  private lastFailure = 0;
  private readonly threshold = 5;
  private readonly cooldown = 30000; // 30 seconds

  async call<T>(fn: () => Promise<T>): Promise<T> {
    if (this.failures >= this.threshold) {
      const elapsed = Date.now() - this.lastFailure;
      if (elapsed < this.cooldown) {
        throw new Error('Circuit breaker is open');
      }
      this.failures = 0; // Half-open: try again
    }

    try {
      const result = await fn();
      this.failures = 0;
      return result;
    } catch (err) {
      this.failures++;
      this.lastFailure = Date.now();
      throw err;
    }
  }
}

3. Build Fallback Strategies

For AI APIs especially, have a Plan B:

  • Multi-provider: If OpenAI is down, can you route to Anthropic (or vice versa)?
  • Cached responses: Can you serve cached or pre-computed results during outages?
  • Graceful degradation: Can your app still function without the AI feature?

4. Cache Aggressively

If an API response doesn't change every request, cache it. This reduces your dependency on external services and improves performance even when everything's working.

5. Set Realistic Timeouts

Don't let a slow API call hang your entire request. Set aggressive timeouts and handle them gracefully:

const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), 5000);

try {
  const response = await fetch('https://api.example.com/data', {
    signal: controller.signal,
  });
  return await response.json();
} catch (err) {
  if (err.name === 'AbortError') {
    return fallbackData;
  }
  throw err;
} finally {
  clearTimeout(timeout);
}

6. Track SLA Credits

Most APIs offer SLA credits when they miss uptime targets. But they rarely proactively notify you. Track the incidents, compare against your SLA terms, and claim your credits. Tools like apistatuscheck.com can help you maintain an audit trail.

Methodology

Data Collection

All data in this report comes from publicly available statuspage.io API endpoints (/api/v2/incidents.json). We queried the following status pages:

Time Period

Primary analysis covers January 1–28, 2026, with some historical context from late 2025 where relevant (e.g., the Atlassian/AWS incident in October 2025).

What We Measured

  • Incident count: Total number of reported incidents
  • Incident severity: As classified by the provider (minor, major, critical)
  • Time to resolution: From incident creation to resolution timestamp
  • Affected components: Which services/subsystems were impacted

Limitations

This data has important caveats:

  1. Self-reported: Companies choose what to report on their status pages. Some are more transparent than others. An API with zero incidents might have incredible reliability — or might just have a high bar for reporting.

  2. Severity is subjective: One company's "minor" might be another's "major." There's no standardized severity scale across status pages.

  3. Uptime estimates are approximations: Without access to internal monitoring data, we estimate uptime based on incident duration and severity. The actual numbers may differ.

  4. Partial view: We're looking at incidents that affect developers via API. Internal incidents that don't impact the public API aren't captured.

  5. Point-in-time snapshot: This report covers a specific window. A company's reliability profile can change significantly with new infrastructure, new products, or growth.

What's Next

We're expanding apistatuscheck.com from 20 to 50 tracked APIs. We'll be adding AWS, Google Cloud, Azure, Supabase, Firebase, PlanetScale, Resend, Postmark, and many more.

We'll publish updated rankings quarterly, so you'll have a running picture of which APIs you can depend on — and which ones need a fallback plan.

Want to get notified when an API you depend on has issues? Check out apistatuscheck.com — it's free and takes 30 seconds to set up.

Data last updated: January 28, 2026. All incident data sourced from public statuspage.io endpoints. Rankings reflect our analysis and methodology — your experience may vary based on region, usage patterns, and specific API endpoints used.

Related Resources

Monitor Your APIs

Check the real-time status of 100+ popular APIs used by developers.

View API Status →