Optuna vs Weights & Biases
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python library that automatically searches for the best settings (hyperparameters) for machine learning mode
Open source · MIT
Python library to track, visualize, and manage machine learning experiments and models.
The short answer
Choose Optuna if you need…
- 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
- Teams that want to run the same search across multiple workers in parallel
- Users who want a web dashboard to inspect optimization history and parameter importance
Choose Weights & Biases if you need…
- Machine learning engineers who want to compare training runs over time
- Researchers tuning hyperparameters across many experiments
- Teams that need to share experiment results with colleagues
- Developers using PyTorch, TensorFlow, or Keras who want tracking with minimal code changes
Side-by-side
| Optuna | Weights & Biases | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 15k | 11.3k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 7 days ago | 4 days ago |
What each one is
Optuna
Optuna is a Python framework for hyperparameter optimization, meaning it automatically tries different settings for a program or machine learning model and keeps the combination that scores best. You write an objective function that suggests values and returns a score, and Optuna runs many trials to find good values. Its define-by-run API lets you build the search space dynamically in ordinary Python code.
Weights & Biases
Weights & Biases (W&B) is a Python library and hosted platform for keeping track of machine learning work. You add a few lines to a training script to record hyperparameters and metrics like accuracy and loss, then view and compare those runs in a web dashboard. The README also describes covering the wider pipeline from datasets to production models, and offers cloud or self-hosted deployment.
Best for
Optuna
- 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
- Teams that want to run the same search across multiple workers in parallel
- Users who want a web dashboard to inspect optimization history and parameter importance
Weights & Biases
- Machine learning engineers who want to compare training runs over time
- Researchers tuning hyperparameters across many experiments
- Teams that need to share experiment results with colleagues
- Developers using PyTorch, TensorFlow, or Keras who want tracking with minimal code changes
Strengths
Optuna
- Define-by-run API lets search spaces use normal Python conditionals and loops
- Lightweight install with few requirements, per the README
- Includes a web dashboard and visualization functions for inspecting results
Weights & Biases
- Simple API: a few lines like wandb.init() and run.log() add tracking to existing code
- Broad framework integrations listed in the README
- Open source under the MIT license with cloud and self-managed hosting options
Not ideal for
Optuna
- People who need a no-code tool, since the README describes a Python API that requires writing code
- Users whose objective function is very cheap and who need maximum sampling speed, where the README points to the experimental Rustuna project instead
- Non-Python codebases, since the README only shows Python usage
Weights & Biases
- People who only need to run a single script once with no need for records, since the setup adds overhead
- Teams that cannot send data to a third-party service and do not want to self-host, because the default workflow uses the hosted W&B account
- Users looking only for LLM app monitoring, since the README points to a separate product called Weave for that
Trade-offs
Optuna
- The README is truncated and does not fully document the feature set, so some details are unverified
- Requires Python 3.10 or newer and writing Python code to use
- Rustuna, the faster companion project, is labeled experimental and lacks some features
Weights & Biases
- Full value depends on the hosted W&B account and dashboard, which adds a service dependency
- The README is truncated, so details of offline use and data privacy controls are not covered here
- Some hosting options (Dedicated Cloud, Self-Managed) are described only briefly in the README
Getting started
Optuna
Install from PyPI
pip install optunaInstall dashboard (optional)
pip install optuna-dashboardWeights & Biases
Install the wandb library
pip install wandbOptionally configure your API key on this machine
wandb loginFrequently asked questions
What is the difference between Optuna and Weights & Biases?
Optuna: A Python library that automatically searches for the best settings (hyperparameters) for machine learning mode. Weights & Biases: Python library to track, visualize, and manage machine learning experiments and models. Optuna suits machine learning practitioners tuning model settings such as learning rate or tree depth, while Weights & Biases suits machine learning engineers who want to compare training runs over time.
Are Optuna and Weights & Biases free?
Optuna: Free and open source. Weights & Biases: Free and open source.
Which is easier to set up, Optuna or Weights & Biases?
Weights & Biases is easier to get started with (rated beginner); Optuna is rated intermediate and usually needs more setup.
Is Optuna or Weights & Biases more popular?
Optuna has more GitHub stars (15k) than Weights & Biases (11.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Optuna and Weights & Biases?
Other options people compare them with include LightGBM, ONNX Runtime, Stable Baselines3, pandas-profiling, Petals.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.