惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 聂微东
GbyAI
GbyAI
S
SegmentFault 最新的问题
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
Visual Studio Blog
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
B
Blog
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI
雷峰网
雷峰网
爱范儿
爱范儿
Vercel News
Vercel News
人人都是产品经理
人人都是产品经理
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
Microsoft Security Blog
Microsoft Security Blog
Jina AI
Jina AI
P
Proofpoint News Feed
A
About on SuperTechFans
I
InfoQ
F
Fortinet All Blogs
L
LangChain Blog
T
Tailwind CSS Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
We built a free status monitor for 77 AI APIs. Here's wha...
Prismix · 2026-06-22 · via DEV Community

Prismix

Every AI developer has been here: your app is throwing 503s, users are pinging you, and you have 12 browser tabs open — OpenAI status page, Anthropic status page, the GitHub Copilot health page, three different Discord servers — trying to figure out is this me or is it them?

That's the problem we set out to solve. Prismix aggregates status from 77 AI services in one place. Six weeks of running it in production taught us some things that might save you time.


The problem is worse than you think

AI APIs don't fail like traditional infrastructure. They fail in weird, partial ways:

  • Degraded performance that passes your health checks but makes your product feel broken
  • Regional outages — OpenAI US-East is down while EU is fine, so half your users are affected
  • Silent rate-limit cascades — the API returns 429s but their status page says "operational" for another 20 minutes
  • Incident lag — providers often post status updates 10–30 minutes after engineers are already aware

The official status pages are optimistic by design. They're customer-facing communications tools, not real-time engineering dashboards. There's nothing wrong with this — but it means you need a different mental model for "is this service down?"


What 77 status pages look like in aggregate

When you watch 77 AI services simultaneously, patterns emerge fast.

OpenAI is the most-watched service (and has the most incidents to watch). The pattern is almost always the same: investigatingidentifiedmonitoringresolved, typically in 45–90 minutes. The investigating phase is where most developers panic — it looks bad but usually resolves without action on your end.

Anthropic runs noticeably clean compared to its API usage growth. Incidents are rarer and shorter. When they do happen, updates arrive faster than most providers.

The long tail is interesting. Services like Replicate, Runway, ElevenLabs, and Suno have incident patterns that don't correlate with OpenAI at all. If you're routing across multiple providers for redundancy, these are genuinely independent failure domains — worth knowing.

The "silent degradation" problem is real. Multiple times we've seen a service show "operational" on its status page while our uptime probe was timing out. This is the main reason Prismix shows a latency sparkline per service — the status page is authoritative for announced incidents, but the probe catches real ones.


What Prismix built (and why it's free)

Prismix pulls from official status pages, aggregates them into a single dashboard, and adds a few things that the individual pages don't have:

Per-service latency probes — 24-hour sparklines showing actual response times, not just announced incidents. This catches the "silent degradation" cases.

Cross-service incident timeline/incidents shows everything that happened across all 77 services in one scrollable feed. Useful for postmortems ("was anything else degraded when our error rate spiked at 3pm Tuesday?").

Embeddable status badges — put a live "OpenAI: operational" badge in your own app's status page with one line of HTML.

Public REST APIGET /api/v1/statuses returns current status for all 77 services as JSON. No auth, no rate limit for reasonable use, CORS open. Free forever.

RSS feed/incidents.rss if you want AI incident updates in your feed reader.

It's free because it runs entirely on Cloudflare's free tier (Workers + KV). The Pro tier ($10/mo) adds email and webhook alerts for services you care about, but the core dashboard stays free.


The technical part (because this is dev.to)

The stack is Astro 5 SSR + Cloudflare Workers + KV. We wrote about the performance walls we hit in a previous post — the short version is that 77 parallel KV reads per request is a bad idea and a single pre-aggregated snapshot blob is much better.

One thing that surprised us: KV's free tier gives you 100,000 reads per day but only 1,000 writes. The cron job that refreshes status runs every 5 minutes, so every write is conditional — only write if the content actually changed. That dropped writes from ~8,400/day to ~600/day. Monitoring infrastructure has to be cheap to run, otherwise the incentive to keep it free disappears.


What we don't know yet — and why we're writing this

Six weeks in, Prismix tracks 77 services with a clean incident timeline and growing usage. What we don't have yet is signal on what matters to you.

Some things we're genuinely uncertain about:

  • Which services are missing? The list is opinionated — mostly LLM APIs, popular AI tools, and infrastructure adjacent to them. We've probably missed something obvious in your stack.
  • Is the latency probe useful? It tells you "this service is slow right now" but not "slow compared to what" — no historical baseline yet.
  • What would make you actually use this every day? A Slack bot? A PagerDuty integration? Something in your terminal?

If any of that resonates, drop a comment. Honest feedback shapes what gets built next.

Live at prismix.dev.


Also at Prismix: an MCP server directory with 500+ servers and a curated AI news feed — but the status monitoring is the part we're most curious to hear about.