Open Source Weights & Biases Alternatives
A curated collection of the 2 best open source alternatives to Weights & Biases.
The best open source alternative to Weights & Biases is Arize Phoenix. If that doesn't suit you, we've compiled a ranked list of other open source Weights & Biases alternatives to help you find a suitable replacement. Other interesting open source alternative to Weights & Biases is mlop.
Weights & Biases alternatives are mainly Machine Learning Infrastructure. Browse these if you want a narrower list of alternatives or looking for a specific functionality of Weights & Biases.
An open source AI observability platform for engineers who need to trace, evaluate, and debug LLM applications.
Arize Phoenix is an open source AI observability platform built for experimentation, evaluation, and troubleshooting. It removes the guesswork from LLM applications, where a bad answer can come from a prompt, a retrieval step, or a model call, and nothing in an ordinary log tells you which.
Instrumentation is OpenTelemetry based through the OpenInference project, which keeps Phoenix vendor and language agnostic. It ships with support for frameworks such as LangGraph, CrewAI, LlamaIndex, DSPy and the OpenAI Agents SDK, and for providers including OpenAI, Anthropic, Google GenAI, AWS Bedrock and LiteLLM.
The platform is organized around a handful of workflows.
- Tracing: capture your application runtime and inspect every span sitting behind an answer.
- Evaluation: use LLMs to benchmark response and retrieval quality against criteria you define.
- Datasets and experiments: version examples, then track how prompt, model and retrieval changes score.
- Playground: compare models, adjust parameters, and replay traced calls while tuning a prompt.
- Prompt management: version, tag and test prompt changes instead of editing them in place.
A remote MCP server exposes traces, datasets and experiments to clients such as Claude Code and Cursor, and a setup command in the CLI wires instrumentation into an existing project by detecting its framework and provider.
Phoenix installs with pip or conda and runs locally, in a container, or on a cluster through published Docker images and a Helm chart. It is self hosted throughout, so traces stay inside your own infrastructure.
Self-hostable experiment tracking for machine learning engineers who want high logging throughput from about five lines of Python.
mlop is an MLOps framework for tracking training runs. It covers experiment tracking and lifecycle management for models, and the whole platform can be self-hosted, so runs and the data behind them stay on hardware you already have.
Instrumenting a project is short. Install the package, call init with a project name, log values as training proceeds and finish at the end. The README leads with a side by side recording of the mlop logger against a conventional one, because stable data throughput is the property the project optimizes for above everything else.
The work is split across a few pieces you can adopt separately.
- Python client: the mlop package installs from PyPI and carries the logging calls a run needs.
- Self-hosted server: a companion server repository comes up in three commands with docker compose and an environment file.
- Hosted platform: an account on the mlop service runs the same client code with no infrastructure of your own.
- Notebooks: an introductory Colab notebook and a PyTorch tutorial walk through a first run end to end.
- Design stance: the project follows a keep it simple philosophy and treats throughput as the top priority.
The team describes mlop as built by ML engineers who were tired of the state of the art in observability tooling, with the stated aim of informing you about model performance and saving compute time rather than charging for it. Documentation and a community chat sit alongside the repository.
