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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFirst find out whether the gateway or the upstream AI provider returned the error. Then use the structured error code and logs to classify it: 401 Unauthorized usually points to an identity or credential problem; 403 Forbidden can mean missing permission, a policy restriction, or—in some cloud services—a quota condition; and a quota or rate-limit error may require either slower requests or an account change. The HTTP status alone is not a reliable diagnosis.
Start by locating where the error originated
An inference gateway can reject a client request itself, or forward the request and pass back an error from the upstream provider. Those failures can look similar to the client but involve different identities and fixes: the client authenticates to the gateway, while the gateway may use a separate credential or service identity to call the provider.
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Capture evidence before changing settings
- Record the HTTP status, response body’s machine-readable code and message, any
Retry-Afterheader, timestamp, and request or correlation ID. - Note the gateway, upstream provider, model or resource, project or organization, region, and which identity is used at each hop.
- Check gateway logs to determine whether the gateway rejected the caller or the upstream service rejected the gateway’s request. In Google Cloud API Gateway, the documented log field
jsonPayload.responseDetailscan help; the valuevia_upstreamindicates that the error came from the backend. See Google Cloud API Gateway troubleshooting. - Redact API keys, bearer tokens, and other secrets before sharing a response or log excerpt.
Do not assume the same body fields, headers, or log format exist across gateways. Use the documentation for the gateway and provider in your stack.
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A 401 usually means that the identity handling the request could not authenticate. First determine which hop returned it: a client-to-gateway authentication failure is not the same as an upstream provider rejecting the gateway’s credential.
Check the credential and its scope
- Confirm the expected credential is present in the required header, correctly formatted, and intended for this endpoint.
- Check that it has not expired or been revoked, and that it belongs to the intended provider organization or project.
- Verify that the key or identity is permitted to use the endpoint. A credential can be valid but still have insufficient permissions; inspect the provider’s error details rather than treating every access problem as a bad key.
- If the gateway calls the provider, check that its configured upstream secret is current and that the gateway forwards the intended credential. Do not substitute the client’s gateway credential for the gateway’s provider credential.
OpenAI’s error guidance lists incorrect or revoked keys, organization or project mismatch, and insufficient key permissions among possible causes. Anthropic documents malformed, revoked, or expired keys; Gemini documents missing, invalid, or expired keys. Their precise credential requirements differ, so follow the guide for the provider you use: OpenAI error codes, Anthropic API errors, and Gemini troubleshooting.
Google Cloud API Gateway: check the backend identity too
If Google Cloud API Gateway logs indicate that a 401 came from the backend, investigate the deployed API’s service account and backend authentication path. The service account may have been disabled or deleted, or may lack access to the backend. Google also distinguishes token types: API Gateway uses an ID token for backends, whereas some other Google Cloud APIs require an access token. This is Google-specific behavior, not a general rule for inference gateways. See Google Cloud API Gateway troubleshooting.
Why does my AI gateway return 403 when my key is valid?
A valid key proves only that the credential is recognized; it does not prove that the identity may use a particular model, endpoint, project, or operation. A 403 can also represent a quota or rate-limit condition in some cloud services, so inspect the structured reason and gateway logs before changing permissions.
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Check authorization and policy
- Confirm the model, API, or service is enabled for the correct project or organization, and that the identity has the required role or permission.
- Check permissions for the gateway’s service identity on the backend, if the request uses one.
- Review IP allowlists and regional restrictions, and verify that the model or resource is available in the region being used.
Provider documentation describes these as distinct possibilities: OpenAI includes IP authorization and unsupported-region errors; Anthropic and Gemini document permission errors; Google Cloud API Gateway identifies backend service-account roles as a common 403 cause. Consult the relevant provider guidance before granting broader access: OpenAI error codes, Anthropic API errors, Gemini troubleshooting, and Google Cloud API Gateway troubleshooting.
Read the error reason before treating 403 as a permission failure
Google Cloud quota troubleshooting documents QUOTA_EXCEEDED and RATE_LIMIT_EXCEEDED responses as HTTP 403 in relevant Cloud contexts. OpenAI, Anthropic, and Gemini commonly document 429 for rate or quota conditions. These are provider-specific mappings, not a universal gateway standard. A permission change will not fix a quota rejection, and an unnecessary permission expansion can add risk without restoring access. See Google Cloud quota troubleshooting and the relevant provider error documentation.
How to classify quota-exceeded and rate-limit errors
Before retrying or changing billing, identify which limit was reached and its scope. A request-rate limit, token-rate limit, short burst, daily or model quota, exhausted prepaid balance, and spend cap require different responses.
| Failure type | What to check | Likely next action |
|---|---|---|
| Request-rate limit | Requests over time, burst size, concurrency, and the applicable model, project, or organization limit. | Reduce bursts and concurrency, spread requests over time, and honor Retry-After when supplied. |
| Token-rate limit | Input and output token volume and the relevant token-per-time limit; request and token limits are separate in OpenAI’s guidance. | Reduce token demand or pace requests, then retry within the applicable limit. |
| Daily, model, project, organization, or regional quota | The provider’s current quota or limits page for the exact model, project or organization, region, and time window. | Wait for a recurring window to reset, or use the provider’s authorized quota-change process if available. |
| Exhausted balance, usage limit, or spend cap | Account balance, billing status, and enforced usage or spend settings. | Resolve billing or request an authorized limit adjustment. Repeated requests do not replenish credits or remove a cap. |
OpenAI says rate limits can apply at both organization and project levels, with separate request and token limits. Anthropic describes organization limits and spend caps; Gemini distinguishes rate limiting from quota exhaustion. Check the provider’s current controls because exact limits and account settings depend on the provider and account: OpenAI rate limits, Anthropic rate limits, and Gemini rate limits.
When should you retry, and when must someone change settings?
For a temporary rate limit
Reduce request bursts and concurrency, spread traffic over time, and honor Retry-After if the response includes it. Use bounded retries with backoff only for errors that are retryable; unbounded or immediate retries can create a retry storm and increase load. Official OpenAI SDKs retry eligible rate-limit responses, while Anthropic SDKs retry transient errors and honor Retry-After when present. These behaviors are provider-specific and configurable, so verify how the SDK in your deployment is configured. See OpenAI rate limits and Anthropic retries.
For a balance, billing, or spend-limit error
Retrying alone will not restore access when credits are exhausted or a billing or spend limit is enforced. Take the corresponding authorized account action; allow for changes to spend settings to take time to apply. OpenAI explicitly distinguishes these errors from transient rate limiting in its guidance: OpenAI API billing and quota errors.
A quick decision path
- Capture the status, structured response code and message, relevant headers, timestamp, request ID, and gateway logs; redact secrets.
- Determine whether the gateway or upstream provider produced the error, then identify the credential or service identity used at that hop.
- For 401, verify credential presence, validity, organization or project, endpoint access, and—if applicable—the gateway’s upstream credential and backend identity.
- For 403, check permissions, service-account roles, API or model access, IP policy, and region; inspect the structured reason for a quota classification before changing IAM.
- For quota or rate-limit failures, identify the resource, model, account scope, and time window. Pace traffic for transient limits; address billing, credits, or authorized usage settings for account ceilings.
Because gateways and providers do not share one error taxonomy, treat the status as a clue, not a diagnosis. The response’s structured reason and the logs for the failing hop determine the next useful action.
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