Sold by: Prior Labs
Deployed on AWS
TabPFN-3.5 by Prior Labs is the world's leading Tabular Foundation Model, ranking first on TabArena for classification and regression in a single forward pass at 1858 Elo, beating every other tabular foundation model and AutoML systems tuned for 4 hours, on up to 1,000,000 training rows. Pretrained on synthetic tasks, it generalizes without retraining and needs minimal preprocessing; cached inference scores new rows in well under a second. It handles text, numerical and categorical features, missing values, tables up to 20,000 features, and many-class classification. Free under the non-commercial TabPFN-3.5 License.
Overview
TabPFN-3.5 by Prior Labs is the world's leading Tabular Foundation Model. It ranks first on the popular TabArena benchmark for classification and regression tasks in a single forward pass, reaching 1858 Elo and outperforming every other tabular foundation model as well as AutoML systems tuned and ensembled for 4 hours, on training sets of up to 1,000,000 rows. TabPFN is a pretrained transformer trained exclusively on synthetic prediction tasks, allowing it to generalize across thousands of use cases without retraining. This enables fast, accurate predictions with minimal preprocessing, while cached inference processes the training rows once and scores new rows in well under a second. The model handles text features, mixed feature types (numerical, categorical), high-cardinality categorical columns, missing values, and wide tables of up to 20,000 features, and natively supports many-class classification. It is an ideal default model for teams seeking reliable performance without costly tuning or retraining cycles. Beyond standard classification and regression, TabPFN-3.5 sets the state of the art on realistic non-i.i.d. data with grouped and temporal splits, on string-valued and multimodal tables, on time-series forecasting, and on relational data. This TabPFN-3.5 model package is free to use under the non-commercial conditions as specified in the TabPFN-3.5 License.
Highlights
- Tops the public TabArena benchmark in a single forward pass. 1858 Elo - ahead of every other tabular foundation model and of AutoML systems tuned and ensembled for 4 hours, with no model selection or hyperparameter tuning.
- Pretrained on synthetic tasks only, so it generalizes without retraining. Handles training sets up to 1,000,000 rows and tables up to 20,000 features; cached inference processes the training rows once and scores new rows in well under a second.
- Native handling of mixed feature types: numerical, categorical (including high-cardinality), and free-text columns, plus missing values and many-class targets. No bespoke preprocessing or upstream featurization required.
Details
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Dimensions summary
You run this model at no software cost, so all charges reflect the AWS instance hours you use. Pricing is organized by instance type and inference mode. You choose from two GPU instance families across many sizes, from xlarge up to 48xlarge. Batch mode covers scheduled or bulk predictions, while real-time mode covers on-demand serving. Batch mode uses the two main GPU families. Real-time mode adds a third GPU family and one high-memory instance option. Larger instance sizes carry more compute, so cost scales with the size and mode you pick.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a chosen instance runs the model. The software carries no charge, so you pay only for AWS instance hours used. A fully stopped instance stops accruing hours. Stopped instances may still incur underlying AWS storage fees separate from this listing.
Both meter running instance hours the same way. Batch mode fits scheduled or bulk prediction jobs, so you run instances only while a job processes. Real-time mode keeps an endpoint running to serve on-demand requests, so hours accrue for as long as the endpoint stays active.
Batch mode offers the two main GPU families across their sizes. Real-time mode adds a third GPU family plus one high-memory instance option. Your bill depends on the family, size, and mode you select. Larger sizes carry more compute, so cost rises with size.
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Vendor refund policy
Using TabPFN-3.5 on Amazon SageMaker is free of charge for non-commercial use. Because the model itself does not incur any licensing or usage fees, no refunds are provided for any costs incurred while using this product, including but not limited to charges for AWS compute instances, storage, networking, or any other AWS infrastructure used to run or host the model. If you have questions about this policy or need assistance, you can contact Prior Labs at: hello@priorlabs.ai
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release of TabPFN-3.5-Plus
Additional details
Inputs
- Summary
TabPFN-3 accepts inference requests in two content types:
- application/json -- inline JSON-encoded request body.
- multipart/form-data -- with X_train, y_train, X_test as separate Parquet or CSV file parts plus a JSON config part.
- Limitations for input type
- Sync (real-time) invocations are capped by SageMaker at 6 MB payload and 60 s processing. Async invocations support up to 1 GB payload and 60 min processing, which is the recommended deployment for any non-trivial tabular dataset.
- Input MIME type
- application/json
{"task_config": {"task": "classification", "tabpfn_config": {"n_estimators": 4, "fit_mode": "fit_preprocessors"}, "predict_params": {"output_type": "probas"}}, "X_train": [[1.2, 3.4], [5.6, 7.8], [2.1, 4.3], [6.0, 1.5]], "y_train": [0, 1, 0, 1], "X_test": [[1.0, 4.0], [5.0, 2.0], [3.0, 3.0]]}
{"task_config": {"task": "classification", "tabpfn_config": {"n_estimators": 4, "fit_mode": "fit_preprocessors"}, "predict_params": {"output_type": "probas"}}, "X_train": [[1.2, 3.4], [5.6, 7.8], [2.1, 4.3], [6.0, 1.5]], "y_train": [0, 1, 0, 1], "X_test": [[1.0, 4.0], [5.0, 2.0], [3.0, 3.0]]}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
task | Inference task type. Required; accepted at top level or nested under `task_config.task`. | AllowedValues: classification, regression. | Yes |
n_estimators | Number of TabPFN forward passes that are averaged for the final prediction. Default is the underlying TabPFN library default. | MinValue: 1. MaxValue: 8. | No |
categorical_features_indices | Indices of columns to treat as categorical. If omitted or null, the server infers categorical columns from the data. | - | No |
softmax_temperature | Temperature for the softmax over class logits (classification) or the predictive distribution (regression). | MinValue: greater than 0. Default: 1.0. | No |
average_before_softmax | Whether to average ensemble logits before applying softmax (instead of averaging post-softmax probabilities). Only meaningful when n_estimators > 1. | Boolean. Default: false. | No |
ignore_pretraining_limits | If true, the server does not reject inputs that exceed TabPFN-3.5's validated row, feature or cell-budget envelope. Outside-envelope inputs may run but with degraded accuracy. | - | No |
inference_precision | Numeric precision used for the forward pass. "auto" selects per-instance defaults. | Default: auto. | No |
fit_mode | Controls how the server caches between the fit-time forward pass and any subsequent predicts. `fit_preprocessors` (default) runs everything per request. `fit_with_cache` caches the fit and returns a model_id which can be reused on later requests via `context.model_id` to skip the fit step. | AllowedValues: fit_preprocessors, fit_with_cache. Default: fit_preprocessors. | No |
memory_saving_mode | If true, reduces GPU/CPU memory pressure during inference. Trades a small amount of throughput for the ability to handle larger contexts. | Boolean or null. Default: null. | No |
random_state | Seeds preprocessing-side randomness for reproducibility across runs. | Integer or null. Default: null. | No |
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Support can be reached via hello@priorlabs.ai
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