Azure API Management: A Guide to Load Balancing OpenAI Instances

2 min read

Azure API Management policy for load balancing Azure OpenAI instances can be a powerful tool for optimizing performance and ensuring reliability. By distributing requests across multiple instances, you can enhance the scalability and availability of your OpenAI services. This approach not only improves response times but also provides a robust framework for managing traffic efficiently.
<policies>
<!-- Throttle, authorize, validate, cache, or transform the requests -->
<inbound>
<!--<set-backend-service id="lb-backend" backend-id="openai-pool" />-->
<!-- Route requests based on the roundRobinIndex value -->
<base />
<!-- Try to get value for roundRobinIndex from cache and set it as a variable roundRobinIndex -->
<cache-lookup-value key="roundRobinIndex" default-value="0" variable-name="roundRobinIndex" />
<!-- Get a new index in round robin fasion and store it into cache -->
<cache-store-value key="roundRobinIndex" value="
@{
var index = Convert.ToInt32(context.Variables.GetValueOrDefault("roundRobinIndex","0"));
return ((index + 1) % 3).ToString(); // Assuming you have 3 backends
}
" duration="100" />
<!-- Get a new backend using the index generated above-->
<set-variable name="backendId" value="
@{
var backendsIds = new[] { "dev-openai-backend", "qa-openai-backend", "uat-openai-backend" };
var index = Convert.ToInt32(context.Variables["roundRobinIndex"]);
return backendsIds[index];
}" />
<!-- Set new backend for the current request -->
<set-backend-service backend-id="@((string)context.Variables["backendId"])" />
</inbound>
<!-- Control if and how the requests are forwarded to services -->
<backend>
<base />
</backend>
<!-- Customize the responses -->
<outbound>
<base />
</outbound>
<!-- Handle exceptions and customize error responses -->
<on-error>
<base />
</on-error>
</policies>
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