LiteLLM Status — Is LiteLLM Down?

Operational

Last checked: just now

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Service Status

LiteLLM

Operational130ms

24-Hour Uptime

96%

Operational

Degraded

Outage

No data

Latency (24h)

Recent Incidents

No incidents in the past 30 days

Common Issues with LiteLLM

See detailed troubleshooting guides for the most common LiteLLM errors:

API Error (500 / 502 / 503)

LiteLLM is returning API errors (HTTP 500, 502, or 503), indicating the platform's backend is experiencing issues. MLOps platforms handle complex workloads — model training, experiment tracking, pipeline orchestration — making server errors particularly disruptive to active workflows.

Pipeline / Training Run Failed

Your ML pipeline or training run on LiteLLM has failed unexpectedly. This can be caused by platform-side issues (compute unavailability, orchestration bugs) or configuration problems on your end. Distinguishing between the two is key to a fast resolution.

Model Registry / Artifact Store Error

You cannot push, pull, or access model artifacts in LiteLLM's model registry. This blocks model deployment and versioning workflows. Registry errors are often caused by storage backend issues or permission problems.

About LiteLLM

Open-source proxy server for 100+ LLM APIs

Official Website

Category

Monitored Surfaces

LiteLLM

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