529 The Service Is Overloaded
Server-wide capacity exhaustion across all clients produces the 529 The Service Is Overloaded status code.
Usage
The 529 status code signals server-wide overload rather than per-client rate limiting. Unlike 429 Too Many Requests, which targets a single client exceeding request limits, a 529 means the total volume of requests across all clients has surpassed the server's capacity.
This status code is associated with Qualys SSL Labs and other services handling high-volume scanning or testing workloads. The condition is typically temporary, resolving once server load decreases or additional capacity becomes available.
Example
A client sends a request to an API endpoint during a period of heavy traffic. The server is overloaded and returns a 529 status with a Retry-After header suggesting when to try again.
Request
GET /api/v3/analyze HTTP/1.1
Host: www.example.re
Accept: application/json
Response
HTTP/1.1 529 The Service Is Overloaded
Date: Mon, 02 Mar 2026 11:00:00 GMT
Content-Type: application/json
Retry-After: 900
{
"error": "Service overloaded",
"message": "Too many concurrent requests"
}
How to fix
For API consumers (Qualys SSL Labs and similar services): The Qualys SSL Labs API documentation recommends sleeping for 30 minutes on a 529 and then trying again, randomizing the delay to avoid a thundering herd. Retry during off-peak hours when aggregate demand is lower. Honor the Retry-After header if the response includes one.
Unlike 429 Too Many Requests, a 529 is not caused by a single client exceeding limits. Reducing the request rate from one client does not fix the problem if the server is saturated from total demand.
For server operators: Scale resources (CPU, memory, connection pool limits) to handle peak traffic. Implement rate limiting with a Retry-After header in the 529 response so clients know when to retry. Add Caching layers (CDN, reverse proxy cache) to absorb repeated identical requests. Monitor server metrics and configure auto-scaling to add capacity before saturation occurs. Optimize per-request resource consumption through query tuning, connection pooling, and response Compression.