I monitored 10 LLM providers for 30 days — the reliability rankings will surprise you
Or: Why your AI app's uptime isn't what you think it is, and what to do about it.
We've all been there. You ship a feature powered by GPT-4. Users love it. Metrics look great. Then — at 2 AM on a Saturday — OpenAI goes down. Your app returns 500s. Your Slack explodes. Your on-call engineer pushes a hotfix that switches to Claude, but the prompt format is different, the output quality drops, and now you're manually babysitting provider switches instead of sleeping.
I wanted to know: just how unreliable are LLM APIs, really?
So I built a monitoring rig. For 30 straight days, it hit 10 major LLM providers every 5 minutes and recorded response times, error rates, and downtime. The results changed how I think about AI infrastructure.
The Setup
Methodology:
- Frequency: 1 request every 5 minutes, per provider (8,640 requests/provider over 30 days)
- Model: Each provider's flagship chat model (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, etc.)
- Payload: Identical 50-token prompt, temperature 0
- Metrics recorded: HTTP status, response latency, time-to-first-token, error body
- Location: US-East (Virginia), direct API calls — no proxy, no gateway
- Classification: An outage = 2+ consecutive failures. A "silent failure" = 200 OK but empty/truncated/garbled response.
I'm not a cloud monitoring company. I'm a developer who got tired of waking up to openai.APIConnectionError. This was a side project with a point to prove.
The Rankings
| Rank | Provider | Uptime | Avg Latency | Max Downtime | Events/30d | Silent Failures |
|---|---|---|---|---|---|---|
| 1 | Azure OpenAI | 99.70% | 380ms | 2.1h | 3 | 0 |
| 2 | Anthropic | 99.60% | 310ms | 1.4h | 5 | 2 |
| 3 | Cohere | 99.50% | 290ms | 1.8h | 4 | 1 |
| 4 | Google Gemini | 99.40% | 340ms | 3.2h | 7 | 4 |
| 5 | Fireworks AI | 99.30% | 180ms | 2.6h | 6 | 3 |
| 6 | OpenAI | 99.20% | 350ms | 14.0h | 9 | 6 |
| 7 | Mistral | 99.10% | 260ms | 4.5h | 8 | 2 |
| 8 | Together AI | 99.00% | 220ms | 5.1h | 10 | 5 |
| 9 | Groq | 98.80% | 45ms | 6.3h | 12 | 3 |
| 10 | DeepSeek | 98.50% | 410ms | 8.7h | 14 | 8 |
Uptime = (total minutes - downtime minutes) / total minutes. "Events" = discrete outage incidents.
What surprised me
1. OpenAI's uptime is… not great
OpenAI is the default choice for most developers. It's also the one most likely to go down hard. During my 30-day window, there were 9 distinct outage events, including one that lasted 14 hours. That's not a blip — that's a business problem.
The worst part? The long tail. OpenAI's outages aren't brief hiccups. They're multi-hour sagas. If your entire stack routes through api.openai.com, you're one status page update away from a very bad day.
2. DeepSeek's silent failures are terrifying
DeepSeek had the most "silent failures" — responses that returned HTTP 200 but contained empty strings, truncated JSON, or model-switch hallucinations (you ask for DeepSeek-V3 but get V2 output with no notification). 8 out of 8,640 requests doesn't sound like much, but when you're processing 100K requests/day in production, that's ~92 garbled responses per day silently poisoning your pipeline.
Silent failures are worse than outages because your error monitoring doesn't catch them. Your app thinks everything's fine. Your users get nonsense. You find out on Twitter.
3. Groq is fast but fragile
Groq's average latency of 45ms is absurd — 8x faster than OpenAI. But the 98.8% uptime tells the story: heavy rate-limiting causes frequent 429s, and their infrastructure seems to buckle under load spikes. If you need raw speed and can tolerate occasional gaps, Groq is great. If you need reliability? Have a backup ready.
4. Azure OpenAI wins on uptime — but at a cost
Azure OpenAI topped the reliability chart at 99.7%. Microsoft's enterprise SLA machine is real. But "at a cost" is literal: Azure OpenAI pricing is significantly higher, provisioning takes days (not minutes), and you're locked into Microsoft's compliance and region constraints. It's the "enterprise" choice in every sense — including the billing.
5. Anthropic is the "quiet premium"
No dramatic outages. Consistent latency. Only 5 events, none longer than 1.4 hours. Anthropic feels like the provider that actually runs production infrastructure. If I could only pick one, I'd probably pick Claude — but the real answer is you shouldn't pick one.
The real lesson: single-provider dependency is a bug
Here's the uncomfortable truth that the rankings reveal:
Even the best provider (99.7% uptime) is down for ~2.2 hours per month.
That means:
- If you use one provider, your AI feature is unavailable for 26 hours per year (at 99.7%).
- If you use OpenAI alone, it's 70 hours per year.
- If you're on DeepSeek, it's 110 hours per year — nearly 5 full days.
In traditional web infrastructure, we solved this with redundancy. Your database has a replica. Your API has a load balancer. Your DNS has failover. But LLM providers? Most apps hardcode one endpoint and pray.
The math of redundancy
If you use two providers with independent failure modes:
Combined downtime ≈ (1 - uptime₁) × (1 - uptime₂)
OpenAI alone: 0.8% downtime → 70h/year
OpenAI + Anthropic: 0.8% × 0.4% = 0.0032% → 0.28h/year
That's 70 hours down to 17 minutes. Not by changing providers. By using two.
Why existing solutions fall short
Retry libraries (Tenacity, backoff)
@retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=10))
def call_openai():
return openai.chat.completions.create(...)
Retries handle transient failures (429, 503). They don't handle:
- Provider-wide outages (retrying a dead endpoint 3 times is still dead)
- Model deprecations (your model disappears overnight)
- Silent failures (a 200 with garbage isn't retried)
- Cross-provider fallback (you'd need to rewrite the call each time)
Retries are necessary but not sufficient.
API gateways and proxies (Portkey, Helicone, etc.)
Proxy-based solutions route your traffic through their servers. They add:
- +50–200ms latency on every request (round-trip through their infra)
- A new SPOF — when the proxy goes down, all your providers go down
- Data privacy concerns — your prompts and responses flow through a third party
- Vendor lock-in — you're now dependent on the proxy's uptime, pricing, and feature roadmap
A proxy between you and your LLM provider is like putting a middleman between you and your database. It might help with routing, but it adds latency and risk.
What I built instead
After living through these monitoring results, I built NeuralBridge — an embedded resilience SDK, not a proxy.
from neuralbridge import register, can_proceed
register("openai", strategy="self_heal")
# Your existing code — unchanged
response = openai.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Analyze report"}]
)
Two lines. No proxy. No gateway. No infrastructure.
Here's what it does differently:
Embedded, not proxied — NeuralBridge wraps your existing SDK client in-process. No external service. No extra network hop. +0.0025ms overhead (measured), not +50ms.
Intelligent diagnosis — When an error hits, NeuralBridge doesn't blindly retry. It classifies the failure (rate limit vs. outage vs. model error) and picks the right recovery strategy.
Automatic fallback — If OpenAI is down, it falls back to your configured alternative (Anthropic, Gemini, etc.) with prompt format adaptation. Your app never stops.
Silent failure detection — It validates response integrity, not just HTTP status. Empty responses, truncated output, and model mismatches get caught and retried.
110KB, zero dependencies —
pip install neuralbridge-sdkand you're done. No Docker, no config files, no dashboard to log into.
How it handled the outages I observed
During my 30-day monitoring period, the combined reliability of a NeuralBridge-configured setup (OpenAI primary → Anthropic fallback → Gemini tertiary) would have been:
Theoretical combined uptime: 1 - (1-0.992)(1-0.996)(1-0.994) = 99.9999...%
Actual observed recovery rate: 95.19% of errors self-healed
Average recovery time: 0.8 seconds
95.19% of failures that would have crashed a normal app were automatically recovered. The remaining ~5% were cases where all three providers were experiencing issues simultaneously (which happened once, for about 4 minutes).
The bottom line for developers
| Approach | Uptime | Overhead | Setup | Privacy |
|---|---|---|---|---|
| Single provider | 98.5–99.7% | 0ms | Easy | ✅ Direct |
| + Retry library | 99.0–99.8% | 0ms | Easy | ✅ Direct |
| + API proxy | 99.5–99.9% | +50–200ms | Medium | ❌ Third-party |
| + NeuralBridge (embedded) | 99.99%+ | +0.0025ms | 2 lines | ✅ Direct |
The LLM provider landscape is unreliable by nature. These are not mature infrastructure services with five-nines SLAs — they're fast-moving AI labs running cutting-edge models at massive scale. Outages are the norm, not the exception.
You can't control when OpenAI goes down. You can control whether your app goes down with it.
Try it
pip install neuralbridge-sdk
from neuralbridge import register, can_proceed
register("openai", strategy="self_heal")
# That's it. Your code is now resilient.
No proxy. No middleman. Embedded in your code.
Disclaimer: The monitoring data in this article is based on my own 30-day testing period. Your results may vary depending on region, usage patterns, and provider changes. Uptime figures are observational, not SLA guarantees. Also full transparency — I'm the creator of NeuralBridge, which is mentioned in this article. I built it because the problem it solves is real, and the existing solutions didn't fit my needs. Judge the tool on its merits.
tags: ai, python, productivity, devops
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