Navigate through the Mendix Cloud GenAI Portal

Last modified: November 28, 2025

Introduction

The Mendix Cloud GenAI portal is the part of the Mendix portal that provides access to Mendix Cloud GenAI Resource Packs. After logging in, you can navigate to the overview of all resources. You can see all resources, that you are a team member of and access their details.

Resource Details

After clicking on a specific resource, you land on its details page, offering shortcut to consumption insights, key generation, team management, and helpful documentation.

Settings

The Settings tab contains the details of a GenAI resource. It shows the following:

Display Name: indicates the name of the resource.
ID: indicates the resource ID.
Region(s): the region where the resource is hosted.
Cross Region Inference (CRI): shows if the model supports cross-region inference ¹.
Cloud Provider: indicates the cloud provider, for example, AWS.
Type: this is the type of resource, for example, Text Generation, Embedding, Knowledge Base, etc.
Model: indicates which model is used, for example, Anthropic Claude Sonnet 3.5.
Plan: indicates the subscription plan used for compute resources (for example, embedding or text generation resources).
Environment: shows which environment is used, for example, test, acceptance, or production.

¹ Cross-region inference (CRI) allows a model to redirect requests to another region, helping to distribute the load across multiple regions within the same area. So, EU requests always stay within EU regions. Connecting to a cross-region inference profile does not change how the request is sent; the redirection happens on the server side, determining the region to handle the request to get the fastest response. For more information, see Increase throughput with cross-Region inference. If applicable, CRI profiles are selected during provisioning of a model resource. New models are available under the CRI inferencing type by default.

Additional Details for Knowledge Base Resources

For knowledge base resources, you can also see details of the associated embeddings resource and vice versa. To learn more about embeddings, see the Embedding vector section of RAG in a Mendix App.

Team

The Team page allows you to manage access to the Mendix Cloud GenAI resource. All users listed in this overview have access to the resource in the GenAI resource portal and can create new keys or invite new users. You can add new users via the Add Member button and remove them using the Remove Member button next to their name in the overview.

Currently, you can only invite people within the same organization.

Keys

The Keys tab allows you to manage configuration keys for the resources. These keys provide programmatic access to the GenAI resources. From the Keys tab, you can create new keys and revoke existing ones.

To create a new key, click Create Key, add a description, and save the changes. A pop-up message will display the key.

Make sure to store it securely, as it will only be shown once.

Once created, the key can be used in the Mendix application via the Mendix Cloud GenAI Connector.

Additional Information for Knowledge Base Resource Keys

When you create a key for a knowledge base, an embeddings resource key is automatically generated for the selected embeddings model and marked accordingly in the keys overview. To configure a knowledge base connection from a Mendix application, you only need to import the knowledge base resource key. The connection details for the embeddings model are created automatically.

Content (Only for Knowledge Bases)

The Content tab is available only for Knowledge Bases.

On the Content page, you can find information on adding knowledge to your Knowledge Base resource and managing its content.

Currently, you have the following options for adding data to a Knowledge Base:

Add files (for example, TXT or PDF)
Add data from a Mendix application.

Add Files

When you select the Add Files Like .TXT or .PDF option, you can upload documents directly to the GenAI portal. Before uploading, you also have the option to add metadata. For more information, see the metadata section below.

Only TXT and PDF files are supported.

Before uploading, you can choose to upload the data to a new collection, the default collection, or another existing collection within the resource. A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on its shared domain, purpose, or thematic focus. Below is a diagram showing how resources are organized into separate collections. This approach allows multiple use cases to share a common resource while the option to only add the required collections to the conversation context is preserved.

While collections provide a mechanism for data separation, it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of Metadata.

Metadata

Metadata is additional information that can be attached to data in a GenAI knowledge base. Unlike the actual content, metadata provides structured details that help in organizing, searching, and filtering information more efficiently. It helps manage large datasets by allowing the retrieval of relevant data based on specific attributes rather than relying solely on similarity-based searches.

Metadata consists of key-value pairs and serves as additional information connected to the data, though it is not part of the vectorization itself.

In the employee onboarding and IT ticket support example, instead of having two different collections, such as IT setup, and equipment and historical support tickets, there could be one named 'Company IT'. To retrieve tickets only and no other information from this collection, add the metadata below during insertion.

key: `Category`, value: `Ticket`

The model then generates its response using the specified metadata instead of solely the input text.

Using metadata, even more fine-grained filtering becomes feasible. Each ticket may have associated metadata, such as

key: Ticket Type, value: Bug
key: Status, value: Solved
key: Priority, value: High

Instead of relying solely on similarity-based searches of ticket descriptions, users can then filter for specific tickets, such as 'Bug' tickets with the status set to 'Solved'.

Add Data from a Mendix Application

You can upload data directly from Mendix to the Knowledge Base. To do so, several operations of the Mendix Cloud GenAI Connector are required. For a detailed guide on this process, see the Add Data Chunks to Your Knowledge Base section of Mendix Cloud GenAI Connector.

Consumption (Only for Text and Embeddings Generation Resources)

The Consumption tab is available for Model resources only.

The Consumption section provides outcomes of token consumption monitoring for each GenAI resource in a graphical way. Use this overview to see the current usage, insights on the usage per day, and to compare the current month with previous months. Note that months represent bundle months here, which is the period during which token consumption is tracked, beginning on the date of your last GenAI Resource plan entitlement reset and ending on the next reset date. This creates a recurring monthly cycle based on your plan activation date, not the calendar month.

What Are Tokens?

Tokens are what you pay for when consuming large language model services.

In order for a large language model to understand text input, the text is first ‘tokenized’: broken down into smaller pieces where each piece represents a token with its unique ID. A good rule of thumb is that 100 tokens are around 75 English words, however there are always differences depending on the model or the language used. After tokenization, each token will be assigned an embeddings vector. The tokens required to feed the input prompt to the model are called ‘input tokens’. The tokens required to transform the model output vectors into, for example, text or images are called ‘output tokens’.

When Are Tokens Consumed?

Text generation resources consume both input and output tokens (text sent to the model and generated by the model).

Embeddings resources only consume input tokens. This is because only the generated embedding vectors are returned and the generated output is not tokenized.

Knowledge base resources do not consume tokens as they only store embedding vectors. Uploading a document to a knowledge base connected to an Embeddings resource will consume tokens in the embeddings resource.

Exporting Token Consumption Data

Click Export to export consumption data in CSV format. The export contains basic information about input tokens, output tokens, and dates. Days with no consumption are not exported.