This guide provides a comprehensive set of best practices to help you transition from prototype to production. Whether you are a seasoned machine learning engineer or a recent enthusiast, this guide should provide you with the tools you need to successfully put the platform to work in a production setting: from securing access to our API to designing a robust architecture that can handle high traffic volumes. Use this guide to help develop a plan for deploying your application as smoothly and effectively as possible.
Once you log in to your OpenAI account, you can find your organization name and ID in your organization settings. The organization name is the label for your organization, shown in user interfaces. The organization ID is the unique identifier for your organization which can be used in API requests.
Users who belong to multiple organizations can pass a header to specify which organization is used for an API request. Usage from these API requests will count against the specified organization's quota. If no header is provided, the default organization will be billed. You can change your default organization in your user settings.
You can invite new members to your organization from the members settings page. Members can be readers or owners. Readers can make API requests and view basic organization information, while owners can modify billing information and manage members within an organization.
New free trial users receive an initial credit of $5 that expires after three months. Once the credit has been used or expires, you can choose to enter billing information to continue your use of the API. If no billing information is entered, you will still have login access but will be unable to make any further API requests.
Once you’ve entered your billing information, you will have an approved usage limit of $120 per month, which is set by OpenAI. To increase your quota beyond the $120 monthly billing limit, please submit a quota increase request.
If you’d like to be notified when your usage exceeds a certain amount, you can set a soft limit through the usage limits page. When the soft limit is reached, the owners of the organization will receive an email notification. You can also set a hard limit so that, once the hard limit is reached, any subsequent API requests will be rejected. Note that these limits are best effort, and there may be 5 to 10 minutes of delay between the usage and the limits being enforced.
The OpenAI API uses API keys for authentication. Visit your API keys page to retrieve the API key you'll use in your requests.
This is a relatively straightforward way to control access, but you must be vigilant about securing these keys. Avoid exposing the API keys in your code or in public repositories; instead, store them in a secure location. You should expose your keys to your application using environment variables or secret management service, so that you don't need to hard-code them in your codebase. Read more in our Best practices for API key safety.
As you scale, you may want to create separate organizations for your staging and production environments. Please note that you can sign up using two separate email addresses like bob+prod@widgetcorp.com and bob+dev@widgetcorp.com to create two organizations. This will allow you to isolate your development and testing work so you don't accidentally disrupt your live application. You can also limit access to your production organization this way.
When designing your application or service for production that uses our API, it's important to consider how you will scale to meet traffic demands. There are a few key areas you will need to consider regardless of the cloud service provider of your choice:
When using our API, it's important to understand and plan for rate limits.
Latency is the time it takes for a request to be processed and a response to be returned. In this section, we will discuss some factors that influence the latency of our text generation models and provide suggestions on how to reduce it.
The latency of a completion request is mostly influenced by two factors: the model and the number of tokens generated. The life cycle of a completion request looks like this:
The bulk of the latency typically arises from the token generation step.
Intuition: Prompt tokens add very little latency to completion calls. Time to generate completion tokens is much longer, as tokens are generated one at a time. Longer generation lengths will accumulate latency due to generation required for each token.
Now that we have looked at the basics of latency, let’s take a look at various factors that can affect latency, broadly ordered from most impactful to least impactful.
Our API offers different models with varying levels of complexity and generality. The most capable models, such as gpt-4, can generate more complex and diverse completions, but they also take longer to process your query.
Models such as gpt-3.5-turbo, can generate faster and cheaper chat completions, but they may generate results that are less accurate or relevant for your query. You can choose the model that best suits your use case and the trade-off between speed and quality.
Requesting a large amount of generated tokens completions can lead to increased latencies:
max_tokens parameter incur less latency.11. as a stop sequence, you can generate a list with only 10 items, since the completion will stop when 11. is reached. Read our help article on stop sequences for more context on how you can do this.n and best_of when possible where n refers to how many completions to generate for each prompt and best_of is used to represent the result with the highest log probability per token.If n and best_of both equal 1 (which is the default), the number of generated tokens will be at most, equal to max_tokens.
If n (the number of completions returned) or best_of (the number of completions generated for consideration) are set to > 1, each request will create multiple outputs. Here, you can consider the number of generated tokens as [ max_tokens * max (n, best_of) ]
Setting stream: true in a request makes the model start returning tokens as soon as they are available, instead of waiting for the full sequence of tokens to be generated. It does not change the time to get all the tokens, but it reduces the time for first token for an application where we want to show partial progress or are going to stop generations. This can be a better user experience and a UX improvement so it’s worth experimenting with streaming.
Our servers are currently located in the US. While we hope to have global redundancy in the future, in the meantime you could consider locating the relevant parts of your infrastructure in the US to minimize the roundtrip time between your servers and the OpenAI servers.
Depending on your use case, batching may help. If you are sending multiple requests to the same endpoint, you can batch the prompts to be sent in the same request. This will reduce the number of requests you need to make. The prompt parameter can hold up to 20 unique prompts. We advise you to test out this method and see if it helps. In some cases, you may end up increasing the number of generated tokens which will slow the response time.
To monitor your costs, you can set a soft limit in your account to receive an email alert once you pass a certain usage threshold. You can also set a hard limit. Please be mindful of the potential for a hard limit to cause disruptions to your application/users. Use the usage tracking dashboard to monitor your token usage during the current and past billing cycles.
One of the challenges of moving your prototype into production is budgeting for the costs associated with running your application. OpenAI offers a pay-as-you-go pricing model, with prices per 1,000 tokens (roughly equal to 750 words). To estimate your costs, you will need to project the token utilization. Consider factors such as traffic levels, the frequency with which users will interact with your application, and the amount of data you will be processing.
One useful framework for thinking about reducing costs is to consider costs as a function of the number of tokens and the cost per token. There are two potential avenues for reducing costs using this framework. First, you could work to reduce the cost per token by switching to smaller models for some tasks in order to reduce costs. Alternatively, you could try to reduce the number of tokens required. There are a few ways you could do this, such as by using shorter prompts, fine-tuning models, or caching common user queries so that they don't need to be processed repeatedly.
You can experiment with our interactive tokenizer tool to help you estimate costs. The API and playground also returns token counts as part of the response. Once you’ve got things working with our most capable model, you can see if the other models can produce the same results with lower latency and costs. Learn more in our token usage help article.
As you move your prototype into production, you may want to consider developing an MLOps strategy. MLOps (machine learning operations) refers to the process of managing the end-to-end life cycle of your machine learning models, including any models you may be fine-tuning using our API. There are a number of areas to consider when designing your MLOps strategy. These include
Thinking through these aspects of your application will help ensure your model stays relevant and performs well over time.
As you move your prototype into production, you will need to assess and address any security and compliance requirements that may apply to your application. This will involve examining the data you are handling, understanding how our API processes data, and determining what regulations you must adhere to. Our security practices and trust and compliance portal provide our most comprehensive and up-to-date documentation. For reference, here is our Privacy Policy and Terms of Use.
Some common areas you'll need to consider include data storage, data transmission, and data retention. You might also need to implement data privacy protections, such as encryption or anonymization where possible. In addition, you should follow best practices for secure coding, such as input sanitization and proper error handling.
When creating your application with our API, consider our safety best practices to ensure your application is safe and successful. These recommendations highlight the importance of testing the product extensively, being proactive about addressing potential issues, and limiting opportunities for misuse.