LiteLLM / LiteLLM

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

Fix LiteLLM not tracking cost and token usage for Vertex AI batch jobs Includes evidence for LiteLLM troubleshooting demand.

Source-backed Last updated May 14, 2026 1 source Needs local verification

Category: LiteLLM
Error signature: Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0
Quick fix: Compare the failing environment with a known working setup, then change one configuration value at a time.
Updated: May 14, 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

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

Confirm the exact error signature matches Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0.
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 Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0 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 Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0 mean in LiteLLM?

Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0 indicates the workflow described by "LiteLLM Vertex AI Batch Jobs Zero Cost/Usage — Spend Tracking Broken" failed in a LiteLLM context. Start by confirming the exact signature and the component that emitted it.

What should I check first for Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0?

Check whether this cause matches your environment: Vertex AI batch jobs completed successfully but LiteLLM records zero spend and zero tokens; billing and usage tracking completely broken

Is the Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0 guidance source-backed?

The "LiteLLM Vertex AI Batch Jobs Zero Cost/Usage — Spend Tracking Broken" 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 Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0?

For "LiteLLM Vertex AI Batch Jobs Zero Cost/Usage — Spend Tracking Broken", 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 Vertex AI batch jobs always record spend=0, prompt_tokens=0, completion_tokens=0?

For "LiteLLM Vertex AI Batch Jobs Zero Cost/Usage — Spend Tracking Broken", 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.