#
LLM API Reliability: The Reality Nobody Talks About
If you have run more than a few thousand LLM calls in production, you have seen the pattern: things work perfectly in development, then fall apart under load.
#
The Numbers
| Failure Type | Rate | Root Cause | | Timeout | 2-5 percent | Network congestion, provider throttling | | Rate Limit (429) | 1-3 percent | Burst traffic patterns | | Empty Response | 0.5-2 percent | Content filtering, model degradation | | Schema Violation | 1-4 percent | Model behavior drift | | 5xx Server Error | 0.5-1 percent | Provider-side outages |
Total: 5-15 percent of calls fail on first attempt.
#
Why Retry-Only Is Not Enough
Most teams implement exponential backoff and call it done. But retry alone does not help when:
-
The provider is genuinely down (retrying into a black hole)
-
The model has degraded silently (retrying returns the same bad output)
-
You are being rate limited (retrying makes it worse)
#
Self-Healing: A Better Approach Instead of naive retries, a self-healing approach:
Diagnoses the failure type (~19 microseconds) #
Escalates through layers: retry, degrade, failover, learned rule #
Validates output quality across multiple dimensions #
Learns from each failure for next time
#
Key Takeaways
- 5-15 percent of production LLM calls fail on first attempt
- Retry-only strategies fail when providers are degraded
- Self-healing with diagnosis and failover recovers 84.1 percent of faults
- Multi-provider routing eliminates single points of failure
#
Try It
[https://github.com/hhhfs9s7y9-code/neuralbridge-sdk](https://github.com/hhhfs9s7y9-code/neuralbridge-sdk)
NeuralBridge is Apache 2.0 open source.