What this error means
litellm.types.router.RouterRateLimitErrorBasic: No deployments available for selected model. — 429 response from LiteLLM proxy with full stack trace is a LiteLLM failure pattern reported for developers trying to developer running litellm proxy gets routerratelimiterrorbasic with 'no deployments available for selected model' when calling /chat/completions. proxy returns 429 and crashes with visible traceback. needs config fix to resolve deployment routing.. 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 repo documents this exact bug where rate limit error is reported as 'No deployments available' with full Python stack trace showing _pre_call_checks failure. Affects LiteLLM proxy billing/routing logic. Category mapping: LiteLLM proxy internal error → 'LiteLLM'. Not in dev-error-db.
Common causes
- GitHub issue #20867 on BerriAI/litellm repo documents this exact bug where rate limit error is reported as 'No deployments available' with full Python stack trace showing _pre_call_checks failure. Affects LiteLLM proxy billing/routing logic. Category mapping: LiteLLM proxy internal error → 'LiteLLM'. Not in dev-error-db.
Quick fixes
- Confirm the exact error signature matches
litellm.types.router.RouterRateLimitErrorBasic: No deployments available for selected model. — 429 response from LiteLLM proxy with full stack trace. - 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.