LiteLLM / LiteLLM

LiteLLM Spendlog Cleanup Silently Fails with Unhelpful Error Message

Fix LiteLLM spend_log_cleanup silent failure on Kubernetes Includes evidence for LiteLLM troubleshooting demand.

Source-backed Last updated May 12, 2026 1 source Needs local verification

Category: LiteLLM
Error signature: spend_log_cleanup.py:153 - Error during cleanup
Quick fix: Compare the failing environment with a known working setup, then change one configuration value at a time.
Updated: May 12, 2026
Verification status: Source-backed
Evidence: 1 public source URL

Before you change production

This page includes public source URLs in the imported troubleshooting record. Compare those references with your version and environment before applying changes.

Reproduce the smallest failing action and save non-secret logs before changing configuration.
Check versions for LiteLLM, related SDKs, package managers, CI runners, and hosting providers.
Change one setting or dependency at a time, then rerun the same failing command or request.
Avoid destructive commands, credential rotation, billing changes, or security relaxations without a rollback plan.

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

Confirm the exact error signature matches spend_log_cleanup.py:153 - Error during cleanup.
Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
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

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

Diagnostic flow for this page

Match spend_log_cleanup.py:153 - Error during cleanup exactly before applying the quick fix.
Compare the failing environment with LiteLLM versions, account scope, provider settings, and deployment context.
Check the listed common causes in order, starting with the cause that best matches your logs.
Use the evidence status below to decide whether to confirm against public sources or official documentation.
Apply one reversible change, rerun the smallest failing action, and keep rollback notes.

FAQ

What does spend_log_cleanup.py:153 - Error during cleanup mean in LiteLLM?

spend_log_cleanup.py:153 - Error during cleanup indicates the workflow described by "LiteLLM Spendlog Cleanup Silently Fails with Unhelpful Error Message" failed in a LiteLLM context. Start by confirming the exact signature and the component that emitted it.

What should I check first for spend_log_cleanup.py:153 - Error during cleanup?

Check whether this cause matches your environment: 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.

Is the spend_log_cleanup.py:153 - Error during cleanup guidance source-backed?

The "LiteLLM Spendlog Cleanup Silently Fails with Unhelpful Error Message" page is labeled source-backed. It has 1 public source URL from github.com, so use that evidence status when deciding how much additional verification you need.

When should I avoid the quick fix for spend_log_cleanup.py:153 - Error during cleanup?

For "LiteLLM Spendlog Cleanup Silently Fails with Unhelpful Error Message", avoid applying "Compare the failing environment with a known working setup, then change one configuration value at a time." when the error text differs, the affected version or provider is different, or the change would touch credentials, billing, DNS, production deploy settings, or irreversible data.

What should I collect before debugging spend_log_cleanup.py:153 - Error during cleanup?

For "LiteLLM Spendlog Cleanup Silently Fails with Unhelpful Error Message", collect the failing LiteLLM command or request, non-secret configuration, versions, timestamps, request IDs when available, and the smallest reproduction that still triggers the signature.