# Prior Labs

- [Overview](https://docs.priorlabs.ai/overview.md): A tabular foundation model that delivers strong predictions in seconds — no dataset-specific training required.
- [Quickstart](https://docs.priorlabs.ai/quickstart.md): Get started with TabPFN in minutes.
- [Models](https://docs.priorlabs.ai/models.md): Compare TabPFN model versions — capabilities, limits, and availability.
- [Accessing Model Weights](https://docs.priorlabs.ai/how-to-access-gated-models.md)
- [FAQ](https://docs.priorlabs.ai/faq.md)
- [Metering](https://docs.priorlabs.ai/api-reference/metering.md): How TabPFN API usage is metered — token pools, thinking fit quotas, and dataset limits.
- [Rate limits](https://docs.priorlabs.ai/api-reference/rate-limits.md): Default per-user request limits for TabPFN API uploads, fits, and predictions.
- [Thinking mode](https://docs.priorlabs.ai/capabilities/thinking-mode.md): Inference-time compute scaling for TabPFN — better predictions by spending more time at fit.
- [Classification](https://docs.priorlabs.ai/capabilities/classification.md): Learn about TabPFN's classification capabilities.
- [Regression](https://docs.priorlabs.ai/capabilities/regression.md): Learn about TabPFN's regression capabilities.
- [Predictive Distribution](https://docs.priorlabs.ai/capabilities/predictive-distribution.md): Access TabPFN's full predictive distribution for uncertainty quantification.
- [Time Series Forecasting](https://docs.priorlabs.ai/capabilities/forecasting.md): Learn about TabPFN's time series forecasting capabilities
- [Interpretability](https://docs.priorlabs.ai/capabilities/interpretability.md): Explain TabPFN predictions with Shapley values, feature interactions, and partial dependence plots.
- [KV cache](https://docs.priorlabs.ai/capabilities/kv-cache.md): Compute TabPFN's training-set attention state once and reuse it for faster repeated predictions.
- [Relational α](https://docs.priorlabs.ai/capabilities/relational.md): Learn about TabPFN's capabilities on multi-table relational data
- [Anomaly Detection](https://docs.priorlabs.ai/capabilities/anomaly-detection.md): Detect rare and anomalous samples using TabPFN’s unsupervised extension.
- [Data Generation](https://docs.priorlabs.ai/capabilities/data-generation.md): Generate realistic synthetic tabular data with TabPFN in seconds.
- [Fine-Tuning](https://docs.priorlabs.ai/capabilities/fine-tuning.md): Adapt TabPFN's pretrained foundation model to your data with gradient-based fine-tuning.
- [Embeddings](https://docs.priorlabs.ai/capabilities/embeddings.md): Extract latent feature representations from TabPFN models.
- [Many Class Classifier](https://docs.priorlabs.ai/capabilities/many-class.md): Learn about classification with TabPFN for large number of classes.
- [Benchmarking TabPFN](https://docs.priorlabs.ai/benchmarking.md): An end-to-end Python walkthrough for benchmarking TabPFN against other tabular models.
- [Improving Performance](https://docs.priorlabs.ai/improving-performance.md): Practical strategies to improve TabPFN performance beyond the default configuration.
- [Feature Engineering](https://docs.priorlabs.ai/improving-performance/feature-engineering.md): Encode domain knowledge into features that TabPFN cannot learn from raw columns alone.
- [Feature Selection](https://docs.priorlabs.ai/improving-performance/feature-selection.md): Reduce feature count to improve TabPFN's attention efficiency and predictive power.
- [Preprocessing](https://docs.priorlabs.ai/improving-performance/preprocessing.md): Understand TabPFN's default preprocessing and configure feature, category, target, and sampling transforms.
- [Model Parameters](https://docs.priorlabs.ai/improving-performance/model-parameters.md): Tune TabPFN's softmax temperature, metric optimization, and class imbalance handling.
- [Model Context Protocol](https://docs.priorlabs.ai/agentic/mcp.md): Connect AI tools to TabPFN using the Model Context Protocol (MCP) for natural language predictions on tabular data.
- [Setup Guide](https://docs.priorlabs.ai/agentic/setup-guide.md): Step-by-step instructions for connecting Claude, ChatGPT, Cursor, Codex CLI, and n8n to the TabPFN MCP server.
- [Databricks](https://docs.priorlabs.ai/agentic/tutorials/databricks.md): Build a customer churn prediction pipeline using an AI agent, TabPFN MCP, and Databricks Delta tables.
- [n8n](https://docs.priorlabs.ai/agentic/tutorials/n8n.md): Build an n8n chat workflow that accepts CSV uploads and runs TabPFN predictions through MCP.
- [Microsoft Foundry](https://docs.priorlabs.ai/integrations/foundry.md): Access TabPFN in your secure Azure environment.
- [Amazon SageMaker](https://docs.priorlabs.ai/integrations/sagemaker.md): Deploy TabPFN-3-Plus on Amazon SageMaker. Data stays inside your AWS account; the tabpfn-client Python SDK wraps the endpoint with a familiar scikit-learn surface.
- [OOM (Out of Memory) Error](https://docs.priorlabs.ai/troubleshooting/OOM-errors.md): How to handle CUDA and MPS out-of-memory errors when running TabPFN on large datasets.
- [Research Applications](https://docs.priorlabs.ai/tabpfn-in-action/research.md): Research using TabPFN across scientific domains.
- [Industrials](https://docs.priorlabs.ai/customer-references/industrial.md)
- [Healthcare](https://docs.priorlabs.ai/customer-references/healthcare.md)
- [Energy](https://docs.priorlabs.ai/customer-references/energy.md)
- [Finance](https://docs.priorlabs.ai/customer-references/finance.md)
- [Changelog](https://docs.priorlabs.ai/changelog.md): What's new in TabPFN.
- [RelArena-α, KV cache in the API, performance improvements, and more](https://docs.priorlabs.ai/changelog/august-2026.md): Explore RelArena-α, TabPFN-Rel, and RPI, plus KV-cached inference through the API, decoder attention readout, calibrated regression, and faster local fitting.
- [TabPFN-3](https://docs.priorlabs.ai/changelog/tabpfn-3.md): What's new in TabPFN-3 — scale, capabilities, API, client, and migration guidance.
- [Cookbook](https://docs.priorlabs.ai/cookbook/index.md): Step-by-step recipes for common TabPFN use-cases.
- [Experiment with Thinking Mode](https://docs.priorlabs.ai/cookbook/experiment_with_thinking_mode.md): Improve results of target metrics with TabPFN Thinking Mode
- [TabPFN vs XGBoost](https://docs.priorlabs.ai/cookbook/tabpfn_vs_xgboost.md): Compare TabPFN results with XGBoost using an OpenML Dataset
- [See Which Training Rows Drive a TabPFN Prediction](https://docs.priorlabs.ai/cookbook/decoder_readout.md): Interpret TabPFN predictions as weighted votes from training rows with the ManyClassDecoder readout
- [Bayesian Optimization with TabPFN Extensions](https://docs.priorlabs.ai/cookbook/bayesian_optimization.md): Use TabPFN as a Bayesian optimization surrogate via tabpfn-extensions and compare it against random search on Rosenbrock-4.
- [Generate Synthetic Data Using Real Data with TabPFN](https://docs.priorlabs.ai/cookbook/generate_synthetic_data.md): Sample new rows that mirror the statistics of an existing table
- [Insurance Claim Modeling: TabPFN vs. GLM](https://docs.priorlabs.ai/cookbook/insurance_claim_modeling.md): Predict insurance pure premiums on zero-inflated claims data; TabPFN on the raw dataframe vs. hand-engineered actuarial GLM, with per-policy uncertainty
- [TabPFN with MLflow](https://docs.priorlabs.ai/cookbook/mlflow.md): Learn how to wrap TabPFN as an MLflow PythonModel, register it to Unity Catalog, and deploy it to a Mosaic AI Model serving endpoint.
- [Interpreting Results with TabPFN](https://docs.priorlabs.ai/cookbook/interpret_results.md): Learn how to use the standard interpretability toolkit with TabPFN for explainable results
- [Interpreting Time-Series Forecasts with TabPFN-TS](https://docs.priorlabs.ai/cookbook/time_series_interpretability.md): Explain what drives a TabPFN-TS forecast with partial dependence, Window SHAP, and time-series decomposition
- [See Which Training Rows Drive a TabPFN Prediction](https://docs.priorlabs.ai/cookbook/decoder_readout.md): Interpret TabPFN predictions as weighted votes from training rows with the ManyClassDecoder readout
- [Bayesian Optimization with TabPFN Extensions](https://docs.priorlabs.ai/cookbook/bayesian_optimization.md): Use TabPFN as a Bayesian optimization surrogate via tabpfn-extensions and compare it against random search on Rosenbrock-4.
- [Faster Inference with KV Cache](https://docs.priorlabs.ai/cookbook/faster_performance_with_cache.md): Get a quick introduction into faster inference with fit_with_cache
- [Forecast Daily Spare-parts Demand with TabPFN](https://docs.priorlabs.ai/cookbook/forecast_spare_parts_demand.md): Compare local and pooled TabPFN regressors on synthetic demand from German manufacturing plants
- [Get Started with Predictive Distribution](https://docs.priorlabs.ai/cookbook/predictive_distribution.md): Learn how to extract predictive distributions with TabPFNRegressor
- [Insurance Claim Modeling: TabPFN vs. GLM](https://docs.priorlabs.ai/cookbook/insurance_claim_modeling.md): Predict insurance pure premiums on zero-inflated claims data; TabPFN on the raw dataframe vs. hand-engineered actuarial GLM, with per-policy uncertainty
- [Predictions over Relational Data with TabPFN-Rel](https://docs.priorlabs.ai/cookbook/relational_predictions_tabpfn_rel.md): Get started with RelArena & TabPFN-Rel, a TabPFN Harness
- [Experiment with Thinking Mode](https://docs.priorlabs.ai/cookbook/experiment_with_thinking_mode.md): Improve results of target metrics with TabPFN Thinking Mode
- [Forecast Daily Spare-parts Demand with TabPFN](https://docs.priorlabs.ai/cookbook/forecast_spare_parts_demand.md): Compare local and pooled TabPFN regressors on synthetic demand from German manufacturing plants
- [Interpreting Time-Series Forecasts with TabPFN-TS](https://docs.priorlabs.ai/cookbook/time_series_interpretability.md): Explain what drives a TabPFN-TS forecast with partial dependence, Window SHAP, and time-series decomposition
- [REST API quickstart](https://docs.priorlabs.ai/api-reference/getting-started.md): Authenticate, upload data, fit a model, and predict using the TabPFN REST API.
- [Metering](https://docs.priorlabs.ai/api-reference/metering.md): How TabPFN API usage is metered — token pools, thinking fit quotas, and dataset limits.
- [Rate limits](https://docs.priorlabs.ai/api-reference/rate-limits.md): Default per-user request limits for TabPFN API uploads, fits, and predictions.
- [Prepare train set upload](https://docs.priorlabs.ai/api-reference/training/prepare-train-set-upload.md): **Recommended:** Use [tabpfn-client](https://github.com/PriorLabs/tabpfn-client) (`TabPFNClassifier` / `TabPFNRegressor`). It calls these routes for you.
- [Get model limits](https://docs.priorlabs.ai/api-reference/training/get-model-limits.md): **Recommended:** Use [tabpfn-client](https://github.com/PriorLabs/tabpfn-client) (`TabPFNClassifier` / `TabPFNRegressor`). It calls these routes for you.
- [Fit (TabPFN JSON API)](https://docs.priorlabs.ai/api-reference/training/fit-tabpfn-json-api.md): **Recommended:** Use [tabpfn-client](https://github.com/PriorLabs/tabpfn-client) (`TabPFNClassifier` / `TabPFNRegressor`). It calls these routes for you.
- [Prepare test set upload](https://docs.priorlabs.ai/api-reference/prediction/prepare-test-set-upload.md): **Recommended:** Use [tabpfn-client](https://github.com/PriorLabs/tabpfn-client) (`TabPFNClassifier` / `TabPFNRegressor`). It calls these routes for you.
- [Predict (TabPFN JSON API)](https://docs.priorlabs.ai/api-reference/prediction/predict-tabpfn-json-api.md): **Recommended:** Use [tabpfn-client](https://github.com/PriorLabs/tabpfn-client) (`TabPFNClassifier` / `TabPFNRegressor`). It calls these routes for you.
- [Fit a Model](https://docs.priorlabs.ai/api-reference/training/fit-a-model.md): **Deprecated:** Prefer the TabPFN client (`tabpfn-client`) or `POST /tabpfn/fit` after preparing uploads. This multipart `/v1/fit` surface is legacy. See the [TabPFN-3 changelog](/changelog/tabpfn-3).
- [Run Predictions](https://docs.priorlabs.ai/api-reference/prediction/run-predictions.md): **Deprecated:** Prefer `tabpfn-client` or `POST /tabpfn/predict`. See the [TabPFN-3 changelog](/changelog/tabpfn-3).
- [Security](https://docs.priorlabs.ai/api-reference/security.md): How Prior Labs protects your data — encryption, isolation, and access controls.

## OpenAPI Specs

- [openapi](/api-reference/openapi.json)

## Optional

- [GitHub](https://github.com/priorlabs/tabpfn)
- [Discord](https://discord.com/invite/VJRuU3bSxt)
- [Colab Demo](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
- [Blog](https://priorlabs.ai/blog#articles)