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The best Weights & Biases alternatives

8 tools that do a similar job to Weights & Biases, 8 of them open source. Each entry says what it does and who it suits, so you can pick the one that fits instead of the most famous.

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Weights & Biases

Python library to track, visualize, and manage machine learning experiments and models.

  1. 1

    ONNX Runtime

    Open source · MIT · ★ 22.2k

    A cross-platform library for running and training machine learning models quickly.

    Best for: Developers who want to deploy a trained model inside an application or service; Teams running the same model across Windows, Linux, macOS, or ARM devices

    Full review Weights & Biases vs ONNX Runtime
  2. 2

    Stable Baselines3

    Open source · MIT · ★ 14k

    A Python library of reliable reinforcement learning algorithms built on PyTorch.

    Best for: Researchers who want a reliable baseline to compare a new method against; Engineers prototyping an RL agent for a task with a Gymnasium-style environment

    Full review Weights & Biases vs Stable Baselines3
  3. 3

    Optuna

    Open source · MIT · ★ 15k

    A Python library that automatically searches for the best settings (hyperparameters) for machine learning mode

    Best for: Machine learning practitioners tuning model settings such as learning rate or tree depth; Python developers who want to automate trial-and-error on any objective function

    Full review Weights & Biases vs Optuna
  4. 4

    pandas-profiling

    Open source · MIT · ★ 13.7k

    Generates a one-line data quality and exploratory analysis report for pandas and Spark DataFrames.

    Best for: Analysts who want a quick first look at an unfamiliar dataset; Data scientists checking data quality before modeling

    Full review Weights & Biases vs pandas-profiling
  5. 5

    Petals

    Open source · MIT · ★ 10.7k

    Run big language models by sharing the work across volunteers' GPUs, BitTorrent-style.

    Best for: Developers who want to experiment with large open models like Llama 3.1 or BLOOM without owning the hardware to run them alone; Researchers prompt-tuning large models on their own tasks

    Full review
  6. 6

    PyG

    Open source · MIT · ★ 24.1k

    A PyTorch library for building and training graph neural networks on structured data.

    Best for: Researchers who want ready-made implementations of published graph neural network methods; Python developers already comfortable with PyTorch who want to work on graph data

    Full review Weights & Biases vs PyG
  7. 7

    imgaug

    Open source · MIT · ★ 14.7k

    Python library that creates many slightly altered copies of training images for machine learning.

    Best for: Machine learning engineers training image models on a limited dataset; Researchers who need augmentations that keep labels such as bounding boxes or keypoints aligned

    Full review Weights & Biases vs imgaug
  8. 8

    spaCy

    Open source · MIT · ★ 34k

    Python library for processing and analyzing text, with pretrained models for 70+ languages.

    Best for: Python developers building text-processing features into an application; Data scientists who need fast, production-oriented NLP pipelines

    Full review

How we pick: tools in the same category that solve the same problem, projects that name each other as alternatives, and open-source projects that describe themselves as an alternative to Weights & Biases. We don't take payment for placement.