Skip to main content
Version: 2.32.2

Managing OpenAI API Rate Limits

Understanding OpenAI rate limits​

OpenAI imposes fine-grained rate limits on both requests per minute and tokens per minute for each AI model they offer. For example:

ModelTokens per minuteRequests per minute
gpt-3.5-turbo900003500
gpt-3.5-turbo-16k1800003500
gpt-440000200

Dealing with these limits can be tricky, as API calls to OpenAI have high latency (several seconds). As the rate limits are quite aggressive, back-off and retry loops run for several minutes before a request can be successfully sent to OpenAI. When working with OpenAI APIs, request prioritization can be quite beneficial to ensure that the most important requests are sent to OpenAI first.

Managing OpenAI rate limits with Aperture​

Aperture can help manage OpenAI rate limits and improve user experience by queuing and prioritizing requests before sending them to OpenAI. Aperture offers a blueprint for managing quotas, consisting of two main components:

  • Rate limiter: OpenAI employs a token bucket algorithm to impose rate limits, and that is directly compatible with Aperture's rate limiter. For example, in the tokens per minute policy for gpt-4, we can allocate a burst capacity of 40000 tokens, and a refill rate of 40000 tokens per minute. The bucket begins to refill the moment the tokens are withdrawn, aligning with OpenAI's rate-limiting mechanism. This ensures our outbound request and token rate remains synchronized with OpenAI's enforced limits.
  • Scheduler: Aperture has a weighted fair queuing scheduler that prioritizes the requests based on multiple factors such as the number of tokens, priority levels and workload labels.

request prioritization

The graph above shows how requests are scheduled using Aperture, based on priorities and other critical business labels, which will be shown in the upcoming code snippets. Scheduled requests are either executed immediately if they are high priority, or queued if they are of lower priority, specially when nearing OpenAI's rate limits.

Configuration​

Pre-Requisites

Refer to the get started guide to pick the appropriate method of integration with Aperture.

Before creating a policy, a control point needs to be defined. Control Point specifies where the policy should apply the decisions. There are multiple ways to achieve this; for the scope of this guide, a JavaScript SDK is used to create a control point—check out the Control Point Concept & Integrations section for more details.

Control Point with JavaScript SDK​

info

💡 Check out other language SDKs supported by Aperture

The Aperture JavaScript SDK allows you to set a control point manually. How an Aperture Client instance is created is not discussed in this guide; detailed information around SDK integration can be found in Manually setting feature control points using JavaScript SDK.

The code below provides a general idea of control point creation and setting labels.

Integration with Aperture TypeScript SDK

Import and setup Aperture Client:


Wrap the OpenAI API call with Aperture Client's StartFlow and End methods:

const PRIORITIES: Record<string, number> = {
  paid_user: 10000,
  trial_user: 1000,
  free_user: 100,
};

let flow: Flow | undefined = undefined;

if (this.apertureClient) {
  const charCount = JSON.stringify(createCompletionParams).length;
  const labels: Record<string, string> = {
    api_key: CryptoES.SHA256(api.apiKey).toString(),
    estimated_tokens: (
      Math.ceil(charCount / 4) + Number(createCompletionParams.max_tokens)
    ).toString(),
    model_variant: baseModel,
    product_tier: this.settings.product_tier,
    product_reason: this.settings.product_reason,
    priority: String(PRIORITIES[this.settings.product_reason] + priorityBump),
    prompt_type: promptType,
    workload_name: this.settings.product_reason,
  };

  flow = await this.apertureClient.startFlow("openai", {
    labels: labels,
    grpcCallOptions: {
      deadline: Date.now() + 1200000,
    },
  });
}

// Regardless of whether the flow is rejected, send the message
try {
  const { data: chatCompletion, response: raw } = await api.chat.completions
    .create(createCompletionParams)
    .withResponse()
    .catch((err) => {
      this.#logger.error(`openai chat error: ${JSON.stringify(err)}`);
      throw err;
    });

  return chatCompletion;
} catch (e) {
  flow?.setStatus(FlowStatus.Error);
  throw e; // throw the error to be caught by the chat function
} finally {
  flow?.end();
}

Let's understand the code snippet above; we are creating a control point named openai and setting the labels, which will be used by the policy to identify and schedule the request. Before calling OpenAI, we rely on Aperture Agent to gate the request using the StartFlow method. To provide more context to Aperture, we also attach the following labels to each request:

  • model_variant: This specifies the model variant being used (gpt-4, gpt-3.5-turbo, or gpt-3.5-turbo-16k). Requests and tokens per minute rate limit policies are set individually for each model variant.
  • api_key - This is a cryptographic hash of the OpenAI API key, and rate limits are enforced on a per-key basis.
  • estimated_tokens: As the tokens per minute quota limit is enforced based on the estimated tokens for the completion request, we need to provide this number for each request to Aperture for metering. Following OpenAI's guidance, we calculate estimated_tokens as (character_count / 4) + max_tokens. Note that OpenAI's rate limiter doesn't tokenize the request using the model's specific tokenizer but relies on a character count-based heuristic.
  • priority: Requests are ranked according to a priority number provided in this label. For example, requests from paid_user can be given precedence over those from trial_user and free_user in example code.

In the startFlow call, it's important to define the gRPC deadline. This parameter sets limit for how long a request can remain queued. In our case, a deadline of 120000 milliseconds means the request might stay in the queue for up to 20 minutes. After this interval, the request will either be processed or discarded, depending on its position in the queue.

Policies​

You can generate a policy using quota scheduler blueprint, either via Aperture Cloud UI, or aperturectl command line interface.

Follow these steps to create a policy using the quota scheduler blueprint in Aperture Cloud UI:

  • Navigate to the Policies tab in the sidebar menu within your organization.
  • Click Create Policy in the top right corner.
  • Select the Request Prioritization tab and click on the dropdown menu.
  • Choose Quota Based, once there, complete the form to create your quota scheduling policy.

Following are the fields that need to be adjusted to match the application requirements -

  • Policy name: Name of the policy — This value should be unique and required.

  • Bucket Capacity: This value defines burst capacity. For example, in the case of gpt-4 tokens per minute policy, the bucket will have a capacity of 40000 tokens.

  • Fill amount: After the tokens are consumed, the bucket will be filled with this amount. For example, in the case of gpt-4 tokens per minute policy, the bucket will fill at 40000 tokens per minute.

  • Interval: It specifies the time duration in which fill_amount is applied.

  • Limit by label key: This field specifies the label that is used to determine the unique token bucket. It is set to api_key meaning that a token bucket would get initiated for each OpenAI key.

  • Priority label key: This field specifies the label that is used to determine the priority. It is set to priority in the policy and SDK code example.

  • Tokens label key: This field specifies the label that is used to determine tokens. It is set to estimated_tokens in the policy and SDK code example.

  • Workload label key: This field specifies the label that is used to determine the workload. It is set to product_reason in the policy and SDK code example.

Selector parameters allow filtering of the requests to ensure where the policy will act on.

  • Selectors:
    • Control Point: Control point name to match the request against. In this case, it will be openai.
    • Label matcher:
      • match_labels: Labels to match the request against. It is optional.

Below are examples of values file adjusted to match the SDK code snippet & control point labels.

Client-side quota management policies for gpt-4

# yaml-language-server: $schema=../../../../../blueprints/rate-limiting/base/gen/definitions.json
blueprint: quota-scheduling/base
uri: ../../../../../blueprints
policy:
  policy_name: gpt-4-tpm
  quota_scheduler:
    bucket_capacity: 40000
    fill_amount: 40000
    rate_limiter:
      interval: 60s
      limit_by_label_key: api_key
    scheduler:
      priority_label_key: priority
      tokens_label_key: estimated_tokens
    selectors:
      - control_point: openai
        label_matcher:
          match_labels:
            model_variant: gpt-4
            product_reason: paid_user
            prompt_type: chat

Apply Policy​

After entering all required values, click Continue followed by Apply Policy in the bottom right corner.

Policy in Action​

Once the policy is activated, it will begin to ensure that API requests conform to OpenAI's rate limits, prioritizing requests based on the workload types defined in the policy. These workloads are matched with the labels that the SDK passes to Aperture, where paid users are prioritized over trial users, and trial users over free users, thereby establishing a baseline experience for each tier.

Should rate limits be exhausted, Aperture will schedule the requests, placing them in a queue until either the bucket is reset or a token becomes available. Requests will remain in the queue up to the specified timeout value, provided in the StartFlow function. There is no minimum waiting time for requests; as soon as tokens are available, requests will be forwarded to OpenAI. A request can be transmitted to OpenAI as soon as it reaches the application, or it can wait until the timeout.

Monitoring the Policy and OpenAI Performance​

Aperture Cloud provides comprehensive observability of the policy and OpenAI performance, providing a granular view of each workload, such as paid, trial, and free.

The image below shows the incoming token rate and the accepted token rate for the gpt-4 tokens-per-minute policy. We can observe that the incoming token rate is spiky, while the accepted token rate remains smooth and hovers around 666 tokens per second. This roughly translates to 40,000 tokens per minute. Essentially, Aperture is smoothing out the fluctuating incoming token rate to align it with OpenAI's rate limits.

Token Rate in Light Mode

Token Rate in Dark Mode Incoming and Accepted Token Rate for gpt-4

The below image shows request prioritization metrics from the Aperture Cloud console during the same peak load period:

Prioritization Metrics in Light Mode

Prioritization Metrics in Dark Mode Prioritization Metrics for gpt-4

In the upper left panel of the metrics, noticeable peaks indicate that some requests got queued for several minutes in Aperture. We can verify that the trial and free-tier users tend to experience longer queue times compared to their paid counterparts and chat requests.

Queue wait times can fluctuate based on the volume of simultaneous requests in each workload. For example, wait times are significantly longer during peak hours as compared to off-peak hours. Aperture provides scheduler preemption metrics to offer further insight into the efficacy of prioritization. As observed in the lower panels, these metrics measure the relative impact of prioritization for each workload by comparing how many tokens a request gets preempted or delayed in the queue compared to a purely First-In, First-Out (FIFO) ordering.

In addition to effectively managing the OpenAI quotas, Aperture provides insights into OpenAI API performance and errors. The graphs below show the overall response times for various OpenAI models we use. We observe that the gpt-4 family of models is significantly slower compared to the gpt-3.5-turbo family of models.

Flow Analytics

Flow Analytics Performance Metrics for OpenAI Models