LiteLLM Spendlog Cleanup Silently Fails with Unhelpful Error Message

What this error means

spend_log_cleanup.py:153 - Error during cleanup is a LiteLLM failure pattern reported for developers trying to fix litellm spend_log_cleanup silent failure on kubernetes. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue (updated 2026-05-07). Spendlog retention configured but old records not removed. Error message provides no diagnostic information. Affects Kubernetes deployments with 2 replicas. Directly impacts cost tracking and billing for LiteLLM proxy users.

Common causes

• LiteLLM users who set up spendlog retention for cost tracking find that old records are not being cleaned up. The error log shows ‘spend_log_cleanup.py:153 - Error during cleanup’ with no additional context, making debugging impossible in Kubernetes deployments.
• GitHub issue (updated 2026-05-07). Spendlog retention configured but old records not removed. Error message provides no diagnostic information. Affects Kubernetes deployments with 2 replicas. Directly impacts cost tracking and billing for LiteLLM proxy users.

Quick fixes

1. Confirm the exact error signature matches spend_log_cleanup.py:153 - Error during cleanup.
2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

• Verify the command, editor, extension, or API client that produced the error.
• Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
• Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

1. Capture the exact error message and the command, editor action, or request that triggered it.
2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
3. Review the source evidence below and compare it with your environment.
4. Apply one change at a time and rerun the smallest failing action.
5. Keep the working fix documented for the team or deployment environment.

How to prevent it

• Keep provider/tool configuration documented.
• Record non-secret diagnostics such as tool version, provider name, model name, and command path.
• Add a lightweight check before CI or production workflows depend on the tool.

Sources checked

• https://github.com/BerriAI/litellm/issues/16582

Evidence note: GitHub issue (updated 2026-05-07). Spendlog retention configured but old records not removed. Error message provides no diagnostic information. Affects Kubernetes deployments with 2 replicas. Directly impacts cost tracking and billing for LiteLLM proxy users.

Related errors

• LiteLLM spend log retention not working
• LiteLLM database cleanup fails

FAQ

What should I check first?

Start with the exact spend_log_cleanup.py:153 - Error during cleanup text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed LiteLLM workflow until the root cause is understood.

Is this guaranteed to have one fix?

No. The imported evidence supports the troubleshooting path above, but tool behavior can vary by account, plan, version, provider, and local configuration.

How do I know the fix worked?

Rerun the same command, editor action, or request. The fix is working when that action completes without spend_log_cleanup.py:153 - Error during cleanup.