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A 200 OK response does not always mean an API operation succeeded. Some APIs put application-level errors in the response body even when the HTTP status is 200; others use 200 to signal success under their own conventions. The endpoint’s contract and the response body—not the words “Something went wrong” alone—determine what happened. Retries are additional requests and can add to rate-limit pressure, but without the service, endpoint, headers, and logs, it is not possible to confirm how a particular retry was counted.
How can an API return 200 and still report an error?
HTTP status and application outcome are separate signals. The status describes the HTTP response; the API’s body can describe whether the requested operation succeeded, failed, or only partly completed. How those signals relate depends on the API’s documented contract.
For example, monday.com documents application-level errors returned with HTTP 200. Its response can include an errors array and partial data. Its documentation states, “Response will be 200 – OK for application-level errors.” That is one provider’s convention, not a rule for every API. monday.com Developers: Error handling
Other APIs describe 200 as success according to their own conventions. GitLab and News API documentation are examples. Check the exact provider and endpoint rather than assuming either that 200 guarantees the operation worked or that every 200-with-error response is expected. GitLab REST API status codes News API errors
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What to inspect in the response
A generic message such as “Something went wrong” is not enough to diagnose the cause. Check the full response and relevant request metadata, while keeping sensitive information out of logs.
- Structured errors: Look for documented error codes and details in the body, not only a human-readable message.
- Partial results: Determine whether the response contains usable data alongside an error, and whether the endpoint allows partial success.
- Request identifier: Record any request or trace ID, such as the
request_idmonday.com documents, so support or server logs can identify the call. - Headers: Inspect documented rate-limit headers and
Retry-After, if present. - HTTP status: Keep it in your logs, but interpret it together with the response body and endpoint contract.
Can retries count against a rate limit?
Retries are new requests. If a client, SDK, or application sends another request after an error, that attempt can add request volume and put more pressure on a rate limit. OpenAI’s guidance identifies frequent or concurrent requests and unnecessary or redundant calls as rate-limit concerns; it advises clients to “Pace your requests and avoid making unnecessary or redundant calls.” OpenAI: Error codes
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That does not prove that retries caused a specific limit to be reached. Rate-limit accounting depends on the provider’s rules, and the title does not identify the API or its headers. Treat retry amplification as a mechanism to investigate: retries at multiple layers can multiply attempts, especially if an application retry wraps an SDK that also retries automatically.
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- Identify the provider and exact endpoint. Read that endpoint’s response and error documentation, including how it represents application failures and partial results.
- Capture one complete attempt. Log the HTTP status, structured response body, error code, request ID, and relevant headers. Redact credentials, personal data, and other secrets.
- Classify the outcome. Check whether the body indicates an application error, whether data is partial, and whether the condition is transient. Do not treat the generic message alone as proof of success or failure.
- Check rate-limit signals. Review provider-documented limit headers and any
Retry-Aftervalue. - Count retries at every layer. Check application code and SDK configuration. Compare the original call with the total attempts actually sent so nested retry policies do not silently increase traffic.
- Change the retry behavior. Wait for
Retry-Afterwhen supplied. If there is no server-provided delay, use bounded exponential backoff with jitter, reduce concurrency or request volume, and avoid retrying errors that require a fix rather than another attempt.
When should you retry?
Retry only when the endpoint’s contract and the error indicate that another attempt may help. A transient failure may be recoverable; a validation, permission, account, or exhausted-quota problem generally needs correction rather than repetition. Read the structured error code and provider guidance before deciding.
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If the response includes Retry-After
Wait at least the duration the server specifies before retrying. OpenAI and GitLab both instruct clients to honor Retry-After when it is supplied. OpenAI: Error codes GitLab REST API status codes
If no retry delay is supplied
OpenAI recommends exponential backoff with jitter and a limited number of retries when Retry-After is absent. Exponential backoff increases the wait between attempts; jitter varies the delay so clients are less likely to retry in lockstep. Keep retries bounded and reduce request pressure rather than retrying immediately in a loop. OpenAI: Error codes
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If the error is an account or quota condition
Do not keep retrying billing, spend, or quota errors in the hope that another request will restore access. OpenAI notes that retries do not resolve those conditions; check the error code and address the relevant account or limit issue. This is OpenAI-specific guidance, so use the corresponding provider’s documentation for other APIs. OpenAI: Error codes
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Rate limits are not always the only traffic constraint. OpenAI documents a slow_down condition associated with traffic increasing too quickly, which may occur even when requests-per-minute and tokens-per-minute limits have not been exceeded. Its guidance is to honor Retry-After, reduce request rate, and ramp traffic gradually. This is specific to OpenAI; do not assume other providers use the same condition or accounting rules. OpenAI: Error codes
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