Learn about how usage for generative AI assets is measured using resource unit (RU), hourly rates, or a flat rate.
Working with generative AI assets with watsonx.ai Runtime requires that you are using watsonx.ai. For more information on watsonx.ai, see Overview of IBM watsonx.ai.
Review the details for how resources are measured using:
- Resource units to measure inferencing activities for foundation models provided by watsonx.ai.
- Hourly rates for custom foundation models you import and deploy with watsonx.ai.
- Hourly rates for curated foundation models deployed on demand on dedicated hardware.
- Flat rates by page for document text extraction.
A resource unit is used to measure the following resources:
- Tokens used for inferencing a foundation model to generate text or text embeddings.
- Data points used by a time series foundation model for forecasting future values.
Resource unit metering for inferencing foundation models
For the list of supported foundation models for generating text and their prices, see Supported foundation models. For the list of supported encoder models for reranking and generating text embeddings and their prices, see Supported encoder models.
When measuring foundation model inferencing, a Resource Unit (RU) is equal to 1,000 tokens from the input and output of the foundation model. A token is a basic unit of text (typically 4 characters or 0.75 words) used in the input or output for a foundation model prompt or for input to an embeddings model.
Each foundation model provided by IBM watsonx.ai is assigned an inference price for input and output. The price is derived as a multiple of the base price for an RU ($0.0001). For example, a model with a price of $0.0006 has a multiplier of 6 times the base rate.
A prompt tuned foundation model is assigned the same price as the underlying foundation model. For information about tuned foundation models, see Tuning Studio. Tuning a model in the Tuning Studio consumes capacity unit hours (CUH). For more information, see Billing details for machine learning assets.
Resource unit metering for inferencing time series foundation models
When measuring foundation model forecasting, a Resource Unit (RU) is equal to 1,000 data points in the foundation model input and output. A data point is a unit of input and output content that is expressed as one or more numbers.
Billing classes by multiplier
If you are monitoring model usage with the watsonx.ai API, model prices are listed by pricing tier, as follows:
| Model pricing tier | Resource type | Price per RU in USD | Multiplier x base rate |
|---|---|---|---|
| Class 1 | Tokens | $0.0006 | 6 |
| Class 2 | Tokens | $0.0018 | 18 |
| Class 3 | Tokens | $0.0050 | 50 |
| Class C1 | Tokens | $0.0001 | 1 |
| Class 5 | Tokens | $0.00025 | 2.5 |
| Class 7 | Tokens | $0.016 | 160 |
| Class 8 | Tokens | $0.00015 | 1.5 |
| Class 9 | Tokens | $0.00035 | 3.5 |
| Class 10 | Tokens | $0.0020 | 20 |
| Class 11 | Tokens | $0.000005 | 0.05 |
| Class 12 | Tokens | $0.0002 | 2 |
| Class 13 | Tokens | $0.00071 | 7.1 |
| Class 14 | Data points | $0.00013 | 1.3 |
| Class 15 | Data points | $0.00038 | 3.8 |
Certain models, such as Mistral Large, have special pricing that is not assigned by a multiplier. The pricing is listed in Supported models.
Calculating the resource unit rate of tokens per model
To calculate charges for foundation model inference, divide the total number of tokens consumed during the month by 1000 and round up to the nearest 1000 to obtain the total number of RUs. Multiply the total number of RUs by the model price to obtain total usage charges. The model price varies by model and can also vary for input or output tokens for a given model.
The basic formula is as follows:
Total tokens used/1000 = Resource Units (RU) consumed
RU consumed x model price = Total usage charge
The base price for an RU is $0.0001. The price for each foundation model is a multiple of the base price.
Calculating the resource unit rate of data points per model
To calculate charges for forecasting with a time series foundation model, use the following equations:
- Input calculation:
context length x number of series x number of channels - Output calculation:
prediction length x number of series x number of channels
These equations use the following parameters:
- Context length refers to the number of historical data points that a time series foundation model uses as input to make predictions.
- A series is a collection of observations made sequentially over time. For example, when comparing stock prices for many companies, the observed stock price history for each company is a separate series.
- Channels are the specific features or variables that are measured within a time series dataset.
- Prediction length is the number of future data points for the model to predict.
For more information about these values, see Forecast future data values.
| Resource type | Model pricing tier | Price in USD per RU |
|---|---|---|
| Input data points | Class 14 | $0.00013 |
| Output data points | Class 15 | $0.00038 |
The following example shows how to calculate the cost for a time series forecasting request with the following parameters:
| Parameter | Example quantity |
|---|---|
| Context length (granite-ttm-1536-96-r2 model) | 1,536 |
| Channels | 10 |
| Series | 1,000 |
| Prediction length | 96 |
-
Total input data points: 15,360,000 (Context length of 1,536, 10 channels, for 1,000 series)
15,360,000 / 1,000 = 15,360 x 0.00013 = 1.9968 -
Total output data points: 960,000 (Forecast 96 time points, 10 channels, for 1,000 series)
960,000 / 1,000 = 960 x 0.00038 = 0.3648 -
Total price for the time series forecast request: $2.36 (Input cost $1.9968 + Output cost $0.3648)
1.9968 + 0.3648 = 2.3616
Hourly billing rates for custom foundation models
Deploying custom foundation models requires the Standard plan.
Billing rates are according to model hardware configuration and apply for hosting and inferencing the model. Charges begin when the model is successfully deployed and continue until the model is deleted.
| Configuration size | Billing rate per hour in USD |
|---|---|
| Small | $5.22 |
| Medium | $10.40 |
| Large | $20.85 |
For details on choosing a configuration for a custom foundation model, see Planning to deploy a custom foundation model.
Hourly billing rates for deploy on demand models
Deploy foundation models on demand when you want a hosted solution reserved for the exclusive use of your organization. Only colleagues to whom you grant access to the deployment can inference the foundation model. A dedicated deployment means faster and more responsive interactions, and allows for prompts with larger context window lengths. Billing rates are set per model and apply for hosting and inferencing the model. Charges begin when the model is deployed and continues until the model is deleted.
For details on deploying a foundation model on demand, including pricing, see Supported foundation models in watsonx.ai.
Rates per page for document text extraction
Use the document text extraction method of the watsonx.ai REST API to convert PDF files that are highly structured and use diagrams and tables to convey information, into an AI model-friendly JSON file format. For more information, see Extracting text from documents.
Billing is charged at a flat rate per page processed. A page can be a page of text (up to 1800 characters), an image, or a .tiff frame. The billing rate depends on your plan type.
| Plan type | Price per page in USD |
|---|---|
| Essential | $0.038 |
| Standard | $0.030 |
Learn more
- For details on pricing for machine learning assets, see Billing rates for machine learning assets.
- For details on tracking computing resource allocation and consumption, see Runtime usage.
- For more information about each model, see Supported foundation models.
- For details about regional support for each model, see Regional availability for foundation models.
Parent topic: watsonx.ai Runtime plans