Concurrency Limits: A Practical Walkthrough

A batch of study notes or a small coding assistant can start with one AI request and quickly become several running together. Before increasing that number, it helps to understand what concurrency measures. This walkthrough takes you from counting active requests to choosing a practical limit, without treating maximum speed as the only goal.

Start by counting unfinished requests

Concurrency is the number of requests in progress at the same time. If you send four requests before any finishes, your concurrency reaches four. When one finishes, that count falls to three. A concurrency limit restricts this active count rather than the total number you can send during a minute. ([learn.microsoft.com](https://learn.microsoft.com/en-us/aspnet/core/performance/rate-limit?view=aspnetcore-10.0&utm_source=openai))

Imagine summarizing twelve lecture transcripts. Instead of submitting all twelve immediately, choose an initial working limit of two. Keep the remaining ten waiting locally. These numbers are an example for your application, not a statement about MyRelay’s service limits.

Separate simultaneous work from request volume

Next, distinguish concurrency from requests per minute and tokens per minute. Those time-based limits measure traffic over an interval. Concurrency measures overlapping work. AI APIs can enforce request and token limits independently, so controlling simultaneous requests does not replace checking the other limits that apply. ([learn.microsoft.com](https://learn.microsoft.com/en-us/aspnet/core/performance/rate-limit?view=aspnetcore-10.0&utm_source=openai))

For a simple illustration, suppose two requests each take thirty seconds. If replacement requests start immediately, that setup completes roughly four requests per minute. If each instead takes five seconds, the same concurrency can complete roughly twenty-four. The active count stays at two, but the traffic changes substantially.

Understand what the limit protects

Limits exist because shared services have finite capacity. Controlling incoming work helps protect resources from overload. Concurrency controls specifically restrict how much unfinished work can occupy a service at once, while other controls address request frequency. They are capacity safeguards, not simply obstacles to faster processing. ([learn.microsoft.com](https://learn.microsoft.com/en-us/aspnet/core/performance/rate-limit?view=aspnetcore-7.0&utm_source=openai))

For your project, treat that safeguard as a design constraint. The useful question is not how to launch everything immediately. It is how to finish the work reliably, within the time and money you have available.

Build a small queue before increasing parallelism

Give your application a waiting list and a fixed number of active slots. Start with one or two slots for a small experiment. Start another item only after an active request finishes or is confirmed to have failed. Queueing separates accepting work from starting it immediately. ([learn.microsoft.com](https://learn.microsoft.com/en-us/aspnet/core/performance/rate-limit?view=aspnetcore-10.0&utm_source=openai))

For your transcript batch, record each item as waiting, running, completed, or failed. Save completed results as you go. Set a maximum queue length, too, so an accidental repeat submission does not create an afternoon of unwanted work.

If you use several tools, inspect their combined activity rather than only one window. Make your own scheduling policy explicit: decide whether background batches should pause while you are working interactively.

Handle refusals without creating a retry storm

When a request is refused, read the error details before changing anything. An HTTP 429 can reflect a traffic limit or a quota issue; the status alone does not identify concurrency as the cause. For a temporary traffic limit, honor the retry delay when supplied. Otherwise, use increasing delays with a little randomness and a maximum attempt count. Unsuccessful requests can themselves consume rate-limit allowance. ([help.openai.com](https://help.openai.com/en/articles/5955604-troubleshooting-api-rate-limits-and-429-errors?utm_source=openai))

Keep retries inside your scheduling policy. Do not let each failed item launch an independent retry loop. Check whether your SDK already retries eligible failures before adding another layer; OpenAI’s official SDKs include automatic retry behavior. ([help.openai.com](https://help.openai.com/en/articles/5955604-troubleshooting-api-rate-limits-and-429-errors?utm_source=openai))

Choose your working limit using time and cost

Run the same small batch first with one active request, then with two. Compare elapsed time, failures, tokens, and total cost. Increase again only if the improvement matters to your workflow.

MyRelay’s per-request token and cost visibility gives you a useful place to review those experiments across your personal AI budget. Create an account with MyRelay to track your own requests while testing a conservative queue.

Finally, set a batch budget separately from your concurrency setting. Before each experiment, decide how much you are willing to spend and when to stop submitting new work. Choose the smallest working limit that meets your deadline, rather than the largest number your application can launch.