LiteLLM Vertex AI Batch Jobs Zero Cost/Usage — Spend Tracking Broken

What this error means

Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0 is a LiteLLM failure pattern reported for developers trying to fix litellm not tracking cost and token usage for vertex ai batch jobs. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

After change #25627, Vertex AI batch output format changed to OpenAI-shaped JSONL but _batch_cost_calculator still uses raw Vertex usageMetadata reader. Also, batch_cost_calculator skips global litellm.get_model_info() lookup when model_info dict passed without pricing fields. Both bugs cause zero cost/usage.

Common causes

• Vertex AI batch jobs completed successfully but LiteLLM records zero spend and zero tokens; billing and usage tracking completely broken
• After change #25627, Vertex AI batch output format changed to OpenAI-shaped JSONL but _batch_cost_calculator still uses raw Vertex usageMetadata reader. Also, batch_cost_calculator skips global litellm.get_model_info() lookup when model_info dict passed without pricing fields. Both bugs cause zero cost/usage.

Quick fixes

1. Confirm the exact error signature matches Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0.
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/27912

Evidence note: After change #25627, Vertex AI batch output format changed to OpenAI-shaped JSONL but _batch_cost_calculator still uses raw Vertex usageMetadata reader. Also, batch_cost_calculator skips global litellm.get_model_info() lookup when model_info dict passed without pricing fields. Both bugs cause zero cost/usage.

Related errors

• LiteLLM batch cost zero
• Vertex AI cost tracking broken

FAQ

What should I check first?

Start with the exact Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0 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 Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0.