Understanding Azure OpenAI Rate Limit and Quotas

Azure OpenAI has a rate limit to prevent abuse and ensure fair usage. The rate limit is 10 requests per second (RPS) for the free tier.

This limit is in place to prevent a single user from overwhelming the system and impacting others.

To avoid hitting this limit, you can use Azure OpenAI's asynchronous API, which allows you to send multiple requests at once and receive responses in batches.

To view available quota, you'll need to use the Cognitive Services Usages Reader role, which provides minimal access to view quota usage across an Azure subscription.

This role can be found in the Azure portal under Subscriptions > Access control (IAM) > Add role assignment > search for Cognitive Services Usages Reader.

The role must be applied at the subscription level, it doesn't exist at the resource level.

The subscription Reader role will also provide equivalent access, but it will grant read access beyond what's needed for viewing quota and model deployment.

Curious to learn more? Check out: Azure Subscription Limits

Rate limits are quantified in several ways, including RPM (Requests Per Minute), RPD (Requests Per Day), TPM (Tokens Per Minute), TPD (Tokens Per Day), and IPM (Images Per Minute).

Each of these limits can be reached based on any of the metrics, depending on which limit is hit first. For example, if your RPM is set to 20, sending 20 requests with only 100 tokens would exhaust your RPM limit, even if you haven't reached your TPM limit.

Here are the different types of rate limits and their acronyms:

Managing quotas is a crucial aspect of Azure OpenAI, and understanding how it works will help you avoid hitting rate limits. You can assign Tokens-Per-Minute (TPM) to each model deployment, which will directly map to the tokens-per-minute rate limit enforced on its inferencing requests.

The default quota for most available models is assigned to your subscription on a per-region, per-model basis in units of TPM. You can create a single deployment of 240K TPM, 2 deployments of 120K TPM each, or any number of deployments as long as their TPM adds up to less than 240K total in that region.

To manage quotas effectively, you can adjust your TPM allocation by selecting and editing your model from the Deployments page in Azure AI Studio. You can also modify this setting from the Management > Model quota page.

Here are some key quota-related facts to keep in mind:

You can also set user-specific limits to mitigate the risk of exceeding rate limits. This can include daily, weekly, or monthly caps on the number of requests each user can make. For users who frequently exceed their limits, consider implementing a manual review process to assess their usage and adjust their limits as necessary.

Discover more: Azure Storage Limits

To minimize issues related to rate limits, it's a good idea to use techniques like setting max_tokens and best_of to the minimum values that serve the needs of your scenario. For example, don’t set a large max-tokens value if you expect your responses to be small.

Implementing retry logic in your application is crucial, as it ensures your LLM application can handle retries. This helps to prevent repeated failures and maintain a smooth operation.

Quota management is also essential, as it allows you to increase TPM on deployments with high traffic and reduce it for limited needs. This helps to optimize resource usage and prevent hitting rate limits.

Avoid sharp changes in workload, as they can lead to hitting rate limits. Instead, increase the workload gradually, and test different load increase patterns to find the most efficient setup.

Here are some strategies to manage rate limits:

Tokens are the building blocks of text processed by the OpenAI model, with 1 Token approximately equal to 4 characters in English and 3/4 of a word. This affects usage and billing.

A Token is approximately 75 words, making it a crucial unit of measurement for input and output text.

Consider reading: Azure Access Token

Rate limiting is a mechanism that restricts the number of API requests a user can make within a given period to ensure fair usage and prevent service overload.

Rate limits are evaluated over periods of 1 or 10 seconds to ensure requests are evenly distributed over a minute.

PAYG (Pay-As-You-Go) and PTU (Provisioned Throughput Unit) are two models used to manage rate limits. PAYG charges users based on actual usage, while PTU allocates a certain level of capacity to users.

Quotas are the maximum number of requests, tokens, or computational resources an OpenAI API user can consume within a specific timeframe.

TPM (Tokens per Minute) is a rate limit based on the estimated number of tokens processed by a request at the time it is received.

TPM Estimation Factors include Prompt Text, Max_Tokens, and Best_of, which are used to estimate the number of tokens processed by a request.

RPM (Requests per Minute) is dependent on TPM, with a conversion metric of 6 RPM for every 1000 TPM.

Here's a summary of the rate limit models:

A high token usage scenario can result in rate limiting, with a TPM quota of 100,000 allowing for up to 100 requests per minute. However, if requests are made too quickly, the server will throttle them, resulting in HTTP 429 errors.

To effectively manage rate limits and avoid errors, refer to the OpenAI Cookbook, which provides a Python notebook outlining best practices for avoiding rate limit errors.

The OpenAI Cookbook is a valuable resource for learning how to implement throttling in Azure API Management to optimize cost control in AI projects effectively. This can be done by using the strategies outlined in the cookbook.

For more information on cost control, explore the following resources:

Azure OpenAI errors can be frustrating, especially when you're in the middle of a project. Azure OpenAI has a limit of 5 concurrent requests per API key to prevent abuse.

Azure OpenAI requires a valid API key to function, and if the key is invalid or expired, the service will return an error. This is a common issue that can be resolved by checking the API key and updating it if necessary.

Azure OpenAI also experiences errors due to network connectivity issues. If the connection to the Azure OpenAI service is lost or interrupted, the service will return an error.

Check this out: Azure Openai Api Key

Azure OpenAI has a retry mechanism that can be configured to handle transient errors. By setting the retry policy, you can instruct the service to retry the request after a certain amount of time, reducing the likelihood of errors.

Azure OpenAI errors can be caused by invalid or malformed requests. This can happen if the request is missing required parameters or has incorrect formatting.

Here's an interesting read: How to Get Access to Azure Openai Service

If you're dealing with rate limits, a great resource to check out is the OpenAI Cookbook. It has a Python notebook that outlines best practices for avoiding rate limit errors.

The OpenAI Cookbook is a valuable tool for managing your usage of the Azure OpenAI API and ensuring a smooth experience for all users.

To effectively manage rate limits, you can explore how to implement throttling in Azure API Management. This can help optimize cost control in AI projects.

Implementing API throttling in Azure can also help you optimize cost control in AI projects effectively.

If you're interested in learning more about rate limit management, you can check out the following resources:

Managing rate limits in Azure OpenAI API is crucial for smooth operation. Implementing strategies like exponential backoff can reduce repeated failures.

To stay within rate limits, consider batch processing, which groups multiple requests into a single API call. This approach can be achieved using the OpenAI Cookbook's Python script.

Regular monitoring of API usage is essential to identify patterns and adjust your application's behavior accordingly. By doing so, you can predict when you might hit the limits and make proactive adjustments.

Here are some strategies for managing rate limits:

Migrating existing deployments requires some attention to detail. As part of the transition to the new quota system and TPM based allocation, all existing Azure OpenAI model deployments have been automatically migrated to use quota.

This migration process is crucial for maintaining consistency and avoiding disruptions. Existing deployments were automatically migrated to use quota.

The migration process also ensured that deployments with previous custom rate-limit increases were not left behind. Equivalent TPM were assigned to the impacted deployments.

This proactive approach helps to minimize the impact of changes on existing deployments. It's a great example of how careful planning can make a big difference in the transition process.

Managing rate limits is crucial for maintaining the performance and reliability of your applications. It's essential to understand the mechanisms in place and implement strategies that ensure smooth operation without exceeding the allowed thresholds.

Implementing exponential backoff is a great approach to managing rate limits. By waiting for a progressively longer period before retrying, you can reduce the likelihood of repeated failures.

Batch processing is another effective strategy for managing rate limits. Grouping multiple requests into a single API call can help you stay within rate limits while still achieving the desired throughput.

Monitoring API usage is crucial for predicting when you might hit the limits and allowing for proactive adjustments. Regularly monitoring API usage can help you identify patterns and adjust your application's behavior accordingly.

To help you manage rate limits, consider implementing load balancing at different levels. Load balancing can help mitigate the impact of rate limiting, ensuring a smoother and more efficient deployment of your Azure OpenAI models.

Here are some strategies for managing rate limits:

By implementing these strategies, you can ensure that your applications operate smoothly and efficiently, even when dealing with rate limits.