DeepEval Status — Is DeepEval Down?

Operational

Last checked: 8m ago

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DeepEval Web

Operational79ms

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Common Issues with DeepEval

See detailed troubleshooting guides for the most common DeepEval errors:

API Error (500 / 502 / 503)

DeepEval 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 DeepEval 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 DeepEval's model registry. This blocks model deployment and versioning workflows. Registry errors are often caused by storage backend issues or permission problems.

About DeepEval

Open-source LLM evaluation framework

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DeepEval Web

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