LiteLLM / LiteLLM

LiteLLM RateLimitError Misleadingly Reported as 'No Deployments Available' — False Positive Error Masking

Fix LiteLLM proxy logging rate limit errors as deployment failures; understand why stack trace prints 3x even with num_retries: 0 Includes evidence for LiteLLM troubleshooting demand.

Source-backed Last updated June 1, 2026 3 sources Needs local verification

Category: LiteLLM
Error signature: litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model'
Quick fix: Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.
Updated: June 1, 2026
Verification status: Source-backed
Evidence: 3 public source URLs

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

litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model' is a LiteLLM failure pattern reported for developers trying to fix litellm proxy logging rate limit errors as deployment failures; understand why stack trace prints 3x even with num_retries: 0. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #20867 on BerriAI/litellm reports LiteLLM proxy misclassifying genuine rate limit errors (VertexAI 429 RESOURCE_EXHAUSTED) as 'No deployments available for selected model'. Stack trace printed 3× despite num_retries: 0. PR #20932 fixes this by surfacing clear rate-limit messages. Commercial impact: teams debugging production proxy failures waste hours chasing false deployment issues.

Common causes

GitHub issue #20867 on BerriAI/litellm reports LiteLLM proxy misclassifying genuine rate limit errors (VertexAI 429 RESOURCE_EXHAUSTED) as 'No deployments available for selected model'. Stack trace printed 3× despite num_retries: 0. PR #20932 fixes this by surfacing clear rate-limit messages. Commercial impact: teams debugging production proxy failures waste hours chasing false deployment issues.

Quick fixes

Confirm the exact error signature matches litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model'.
Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.

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 litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model' 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 litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model' mean in LiteLLM?

litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model' indicates the workflow described by "LiteLLM RateLimitError Misleadingly Reported as 'No Deployments Available' — False Positive Error Masking" failed in a LiteLLM context. Start by confirming the exact signature and the component that emitted it.

What should I check first for litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model'?

Check whether this cause matches your environment: GitHub issue #20867 on BerriAI/litellm reports LiteLLM proxy misclassifying genuine rate limit errors (VertexAI 429 RESOURCE_EXHAUSTED) as 'No deployments available for selected model'. Stack trace printed 3× despite num_retries: 0. PR #20932 fixes this by surfacing clear rate-limit messages. Commercial impact: teams debugging production proxy failures waste hours chasing false deployment issues.

Is the litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model' guidance source-backed?

The "LiteLLM RateLimitError Misleadingly Reported as 'No Deployments Available' — False Positive Error Masking" page is labeled source-backed. It has 3 public source URLs from github.com, so use that evidence status when deciding how much additional verification you need.

When should I avoid the quick fix for litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model'?

For "LiteLLM RateLimitError Misleadingly Reported as 'No Deployments Available' — False Positive Error Masking", avoid applying "Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests." 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 litellm.RateLimitError: VertexAIException - {"error": {"code": 429, "message": "Resource has been exhausted (e.g. check quota).", "status": "RESOURCE_EXHAUSTED"}} — logged as 'No deployments available for selected model'?

For "LiteLLM RateLimitError Misleadingly Reported as 'No Deployments Available' — False Positive Error Masking", 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.