- Release Notes
- Getting started
- Notifications
- Projects
- Datasets
- Data Labeling
- ML packages
- Out of the box packages
- Pipelines
- ML Skills
- ML Logs
- Document UnderstandingTM in AI Center
- AI Center API
- Licensing
- AI Solutions Templates
- How to
- Basic Troubleshooting Guide
AI Units
- Monitoring license allocation page from the Automation CloudTM Admin Guide for organization level consumption.
- AI units tenant level consumption overview page from the Insights Guide for tenant level consumption.
AI Units is the measure used to license AI products. AI Units are charged based on consumption when the models are bringing value to you.
For more general information on AI units consumption for our AI products, check out the Metering and charging logic and License tracking sections below.
Fore specific details on AI units consumption for Process Mining, check out the License page in the Process Mining guide.
You can also allocate and track AI units consumption at tenant level. See the tenant-level allocation pages for more details:
- Automation CloudTM - Automation Cloud - Allocating licenses to tenants
- Automation Suite - Automation Suite - Allocating robot and service licenses to tenants
This page contains specific information regarding AI Units depending on the used activity, covering the cost for every AI product.
To calculate the overall consumption cost, the following formula is used:
prediction cost
+ hardware cost
= consumption cost
For more information, check the following sections below:
- Prediction cost
- Hardware cost
To calculate the prediction cost, the following formula is used:
input size
x unit cost of the model
=
prediction cost
5000 characters
is as follows:
3 units
x 0.5
(unit cost) = 1.5 AI Units
Input size
Model | Input type | Input size | Computed input size |
---|---|---|---|
Document UnderstandingTM (UiPath and Customer-Managed third party) | Document | 1 page | Number of pages in the input document |
Communications Mining | JSON | 1 message | Number of messages per mailbox or ticketing system |
AI Computer Vision | Image | 1 image | Always 1 |
Task Mining | Dataset | 1 dataset | Always 1 |
GenAI Activities | String | String size limit is different for each model | |
Other models | JSON | 2000 characters = 1 unit | Ceil(length(input)/2000) |
File | 5 MB = 1 unit | Ceil(size/5MB) | |
Files | 5 MB = 1 unit | Ceil(sum(size(input))/5MB) |
Model used
Model | When we charge | Unit cost |
---|---|---|
Document UnderstandingTM (UiPath and Customer-Managed third party) | Per prediction | For a list of all Document Understanding models, check the Metering & Charging Logic page from the Document Understanding guide. |
AI Computer Vision | Per prediction | 0 |
Models in preview (like UiPath Image Classification) | Per prediction | 0 |
Task Mining | Per successful pipeline | 5000 |
Communications Mining | Per message uploaded, modified, or predicted | 1 - for more information on Communications Mining charging logic, check the official documentation. |
UiPath Light Text classifier | Per prediction | 0.2 |
UiPath Multilingual classifier | Per prediction | 0.5 |
UiPath Custom Named Entity Recognition | Per prediction | 0.5 |
Open Source packages |
Per prediction | 0.1 |
GenAI Activities | Per execution | 1 |
The hardware cost at the time of deploying ML Skills is calculated as follows:
replicas
x resource cost
The default replica count depends on the account type:
- Enterprise account: 2
- Other account types: 1
Use the following table to check the resource cost for ML Skills.
Hardware | Unit Cost |
---|---|
0.5 CPU 2 GB RAM (default) | 1 AI Unit / replica / hour |
1 CPU 4 GB RAM | 2 AI Units / replica / hour |
2 CPU 8 GB RAM | 4 AI Units / replica / hour |
4 CPU 16 GB RAM | 8 AI Units / replica / hour |
6 CPU 24 GB RAM | 12 AI Units / replica / hour |
GPU | 20 AI Units / replica / hour |
For hardware cost related to Pipelines, check the following table.
Hardware | Unit Cost |
---|---|
CPU | 6 AI Units / hour |
GPU | 20 AI Units / hour |
To automate a given process, you need to use the two following UiPath models:
The first step is to train the Multilingual Text Classification model on your dataset. The training takes 6 hours and 30 minutes using GPU.
After deploying both models as HA skills, they are running on CPU for three months. During this time, the Multilingual Text Classification model processed 20,000 texts, all around 3,000 characters, while the Invoices model processed 10,000 invoices containing 2 pages each.
- AI Units consumed for training Multilingual Text Classification:
7
(hours) x20
(AI units per hour for GPU) =140 AI Units
- AI Units consumed for hosting Multilingual Text Classification for three months:
24
(hours in day) x90
(number of days) x2
(AI units per hour) =4320 AI Units
- AI Units consumed for hosting Invoices for three months:
24
(hours in day) x90
(number of days) x2
(AI units per hour) =4320 AI Units
- AI Units consumed for predictions made using Multilingual Text Classification:
20000
(number of predictions) x2
(input size) x0.5
(unit cost) =20000 AI Units
- AI Units consumed for predictions made using Invoices:
10000
(number of predictions) x2
(input size) x1
(unit cost) =20000 AI Units
- AI Units consumed in total:
hardware cost
+predictions cost
= (140
+4320
+4320
) + (20000
+20000
) =48780 AI Units