Understanding Azure OpenAI Pricing Models

Azure OpenAI pricing models can be complex, but understanding the basics can help you budget for your projects. The cost of using Azure OpenAI is calculated based on the number of tokens processed.

To give you a better idea, a token is a single character in a piece of text. This means that shorter texts cost less than longer ones. For example, a short sentence like "Hello world" would cost less than a longer paragraph of text.

The cost of Azure OpenAI also depends on the model you choose. The Model for variant determines the complexity and capabilities of the model, which in turn affects the cost. For instance, the Model for variant M is more expensive than the Model for variant S.

Azure OpenAI pricing models are designed to be flexible and scalable, so you only pay for what you use. This makes it a great option for projects that require a lot of text processing, but may not need it all the time.

Before diving into Azure OpenAI, it's essential to estimate costs to avoid any unexpected expenses.

You can use the Azure pricing calculator to get an accurate estimate of the costs involved. This will help you plan your budget and make informed decisions about your usage.

Estimating costs beforehand can save you a lot of headaches down the line.

Using the Azure pricing calculator is a straightforward process that takes just a few minutes.

Azure OpenAI Billing is a crucial aspect to understand, especially when it comes to managing costs. Azure OpenAI Service runs on Azure infrastructure that accrues costs when you deploy new resources.

You'll be charged for Azure OpenAI Service even if the status code is not successful, such as a 400 error due to a content filter or input limit, or a 408 error due to a timeout. This means you'll still incur costs for processing, even if the outcome isn't what you expected.

Model inference chat completions are charged per 1,000 tokens, with different rates depending on the model and deployment type. Each token is roughly four characters for typical English text, so be mindful of the length of your input and output.

Token costs apply to both input and output, so if you ask an Azure OpenAI model to convert a 1,000 token JavaScript code sample to Python, you'll be charged approximately 2,000 tokens. This includes the initial input request sent and the output received in response.

You can pay for Azure OpenAI Service charges with your Azure Prepayment credit, which can be a convenient option for managing costs. However, you can't use Azure Prepayment credit to pay for charges for third-party products and services, including those found in the Azure Marketplace.

Azure Cost Analysis is a useful tool for monitoring OpenAI usage and staying on top of costs. By creating an Azure budget and checking the 'Cost analysis' area, you can get a clear picture of your usage and identify areas where you can optimize costs.

Model pricing with Azure OpenAI is based on the number of tokens in your training file, with the latest prices available on the official pricing page.

Fine-tuned models are charged based on hosting hours, inference per 1,000 tokens, and input/output usage.

You're also charged for hosting hours, even if you're not actively using your fine-tuned model. This can add up quickly, so it's essential to monitor deployed model costs closely.

Here are the key model pricing factors to consider:

Note that a fine-tuned model will incur an hourly hosting cost, even if it's not being used, and will be deleted if it remains inactive for 15 days or more.

Fine-tuned models are charged based on the number of tokens in your training file. You can check the latest prices on the official pricing page.

The cost of fine-tuning a model also depends on the hosting hours and inference per 1,000 tokens, which are broken down into input usage and output usage.

Hosting hours are an important factor to consider, as your fine-tuned model will continue to incur an hourly cost even if you're not actively using it. Monitor your costs closely to avoid unexpected expenses.

After deploying a fine-tuned model, if it remains inactive for more than 15 days, it will be deleted. However, this doesn't delete the underlying customized model, and you can redeploy it at any time.

Each deployed fine-tuned model incurs an hourly hosting cost, regardless of whether you're making completions or chat completions calls to it.

GPT-4 Mini is a cost-efficient small model with vision capabilities, offering a more affordable option for those who need a smaller model. It has a 128K context and an October 2023 knowledge cutoff.

One of the key benefits of GPT-4 Mini is its ability to perform well with vision capabilities, making it a great choice for applications that require image understanding.

While pricing information for GPT-4 Mini is not explicitly stated in the article, we can look at the pricing structure for GPT-4o models to get an idea of what to expect.

Here's a rough idea of the pricing for GPT-4o models:

Keep in mind that these prices are placeholders and actual pricing may vary.

O1 Mini is a fast and cost-efficient reasoning model.

This model is specifically designed for use cases in coding, math, and science.

It has a context size of 128K, which is a notable feature for its intended applications.

The model's knowledge cutoff is October 2023, which is something to keep in mind when using it.

Azure OpenAI Service offers three pricing options: Standard (On-Demand), Provisioned (PTUs), and Batch API.

The Standard (On-Demand) option charges you pay-as-you-go for input and output tokens, with no upfront costs or long-term commitments.

Provisioned (PTUs) allows you to allocate throughput with predictable costs, making it easier to manage your expenses. You can also reserve capacity for a month or a year to reduce your overall spend.

The Batch API offers a 50% discount on Global Standard Pricing, making it a cost-effective option for global deployments.

You can estimate your expected monthly costs using the Pricing calculator, which allows you to input your specific usage scenarios and get an estimate of your costs.

There are three deployment options: Global Deployment, Data Zone Deployment, and Regional Deployment. Global Deployment is available with a Global SKU, while Data Zone Deployment is geographic-based (EU or US), and Regional Deployment is available in up to 27 local regions.

To manage costs, you can filter options in your Azure Budget using the following filters:

Cost Management is a crucial aspect of using Azure OpenAI. You can estimate costs before using Azure OpenAI by using the Azure pricing calculator.

To monitor costs, sign in to the Azure portal, select one of your Azure OpenAI resources, and navigate to the Cost analysis section. This will allow you to view Azure OpenAI costs in graphs and tables for different time intervals.

You can create budgets to manage costs and create alerts that notify stakeholders of spending anomalies and overspending risks. Alerts are based on spending compared to budget and cost thresholds.

To create a budget, head into the Cost Management in the Azure portal, and then into 'Budgets'. Once there, hit the 'Add' button to create a new one. The key step is to add a filter for one of the Azure OpenAI fields.

Here are some filter options to consider when creating a budget:

By using these filters, you can create budgets with more granularity and ensure that you don't accidentally create new resources that cost you more money.

You can also use Azure Cost Analysis to monitor OpenAI usage and identify spending trends. To do this, navigate to the Cost analysis area and select the Accumulated costs view. This will show you the accumulated costs that are analyzed depending on what you've specified for Scope.