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Access TabPFN directly from Azure AI Foundry with Azure-native endpoints and authentication. Usage is billed through your Azure subscription and you are charged by Azure only for the compute resources needed to host TabPFN models.

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Prerequisites

  • An active Azure subscription with access to Azure AI Foundry
  • Azure quota for VM SKUs with GPU
  • TabPFN deployed as an endpoint in your Foundry project
For a full list of supported VM SKUs please visit the TabPFN Microsoft Foundry Model Card.

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Getting Started

  1. Navigate to the Azure AI Foundry Model Catalog
  2. Search for TabPFN and select TabPFN-3-Plus
  3. Click Use this model and follow the guided setup
  4. Once deployed, note your endpoint URL and API key from the deployment details page
Microsoft Foundry hosts each TabPFN version as a separate model. When a new TabPFN version is released, it will appear as a distinct model in the catalog and must be deployed independently - existing deployments will not be updated automatically.

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Azure AI Foundry

If you’ve deployed TabPFN to an Azure AI Foundry managed online endpoint, you can invoke it through tabpfn_client.foundry using the same scikit-learn surface. There is no PriorLabs API token in this path — you authenticate against your own Foundry endpoint with its bearer key, and predict calls are billed by Azure rather than against your TabPFN usage allowance. Install the client library: 
pip install tabpfn-client
Point the estimator at your endpoint URL and pass the bearer key: 
from tabpfn_client.foundry import TabPFNClassifier, TabPFNRegressor
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

clf = TabPFNClassifier(
    endpoint_url="https://<your-endpoint>.<region>.inference.ml.azure.com/predict",
    api_key="<your-foundry-bearer-token>",
)
clf.fit(X_train, y_train)
clf.predict(X_test)
clf.predict_proba(X_test)
Notes:
  • endpoint_url is the full Foundry scoring URL, including the /predict path. The bearer key is sent as Authorization: Bearer <api_key>.
  • Requests are sent as application/json; the Foundry path does not use multipart, so all data travels JSON-encoded.
Set use_kv_cache=True if you will call predict* more than once on the same training data. The first call ships X_train / y_train to the endpoint, runs the fit there, and gets back a model_id. The client caches that id, and every subsequent call sends only X_test plus the id — the server skips the fit and runs inference only. That makes follow-up calls dramatically faster on non-trivial training sets, and shrinks the wire payload from O(n_train + n_test) down to O(n_test): 
clf = TabPFNClassifier(
    endpoint_url="https://<your-endpoint>.<region>.inference.ml.azure.com/predict",
    api_key="<your-foundry-bearer-token>",
    use_kv_cache=True,
)
clf.fit(X_train, y_train)
clf.predict(X_test_a)          # first call: fit + predict on the endpoint
clf.predict_proba(X_test_b)    # cache hit: predict only — much faster
Leave use_kv_cache=False (the default) when each call uses a different training set; otherwise the cache is dead weight on the endpoint.

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Finding your endpoint and key

Authenticate using the Primary key from your deployment’s page in Azure AI Foundry. To access the model settings, navigate to your TabPFN deployment in Azure AI Foundry:
  1. Go to Azure AI Foundry and select the Foundry project where TabPFN was deployed.
  2. In the left-hand menu, select My assets → Models + Endpoints.
  3. Open the Model deployments tab and click on TabPFN.
TabPFN Model Deployment

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Thinking mode

Thinking mode on Azure AI Foundry is available through a separate enterprise listing. Thinking mode applies additional inference-time computation on top of TabPFN-3-Plus to push prediction quality further — on the public TabArena benchmark it beats every non-TabPFN model by over 200 Elo overall and by 420 Elo on the largest data subset.
To request access to Thinking mode on your Azure subscription, reach out to sales@priorlabs.ai.
Once you’ve been granted access, the Thinking listing surfaces the same tabpfn-client SDK; the only change is an optional thinking_effort constructor kwarg on TabPFNClassifier / TabPFNRegressor that engages Thinking mode per request. Omitting it falls back to the standard TabPFN-3-Plus single-forward-pass behavior, so a single endpoint serves both modes. See the Thinking mode page for capability details, benchmark numbers, and parameter reference.