7 Best Open Source Weights and Biases Alternatives
A curated collection of the 7 best open source and self-hosted alternatives to Weights and Biases in 2026.
Looking for a free, privacy-friendly, or self-hosted replacement for Weights and Biases in 2026? The top open-source alternative is Langfuse — Open source AI agent observability platform.
Other highly rated open-source Weights and Biases alternatives include Arize Phoenix, Latitude and LangWatch.
Open source AI agent observability platform
Langfuse is an open-source platform designed to help teams trace, evaluate, and improve AI agents. By leveraging production data, it provides deep insights into agent behavior, enabling teams to collaborate on fixes and optimize performance for better quality, reduced costs, and lower latency. - LLM Observability: Monitor and debug AI agent interactions in real-time with detailed traces and logs. - Prompt Management: Centralize and version control prompts for consistent AI outputs. - Evaluation & Metrics: Assess agent performance using built-in evaluation tools and custom metrics. - Collaboration Tools: Share insights and collaborate on fixes with your team. - Self-Hosting Support: Deploy Langfuse on your infrastructure using Docker, Kubernetes, or cloud providers (AWS, GCP, Azure). - Scalability: Handle millions of observations per month with high-performance data processing. - Integration Ready: Works seamlessly with popular AI frameworks like LangChain and OpenAI. - Open Source: Fully open-source with a vibrant community and enterprise-grade features. Langfuse is built with modern technologies and supports multiple deployment options: - Docker Compose: Quick local or cloud deployment. - Kubernetes (Helm): Scalable production deployments. - Cloud Providers: AWS, GCP, and Azure via Terraform. - Database: Powered by ClickHouse for high-performance analytics. Langfuse is trusted by over 100,000 engineers and used by 21 of the Fortune 50 companies.
Open-source AI agent development platform
Phoenix is an open-source platform designed for AI agent development and evaluation. It provides comprehensive observability, tracing, and evaluation tools to help AI engineering teams build, monitor, and improve their AI applications. - AI Agent Development: Build and test AI agents with integrated tracing and debugging tools. - Observability & Monitoring: Gain deep insights into AI agent performance and behavior. - Evaluation & Benchmarking: Measure and compare AI agent capabilities with built-in evaluation frameworks. - Self-Hosting: Deploy Phoenix locally or in your own infrastructure for full control. - Integration with Coding Agents: Works seamlessly with popular coding agents like Claude, Codex, and Cursor. - Open-Source: Fully open-source under Apache 2.0 license. - Enterprise-Grade: Used by Fortune 500 companies for production AI deployments. Phoenix is open-source and can be self-hosted using Docker, Kubernetes, or directly via CLI. It supports Python and TypeScript SDKs for easy integration with existing AI workflows. Documentation and quick-start guides are available for local setup and production deployment.
Open-source AI agent observability platform
Latitude is an open-source AI agent observability and monitoring platform designed to help teams debug, monitor, and improve their AI agents in production. - Self-Healing Agents: Automatically detects failures in production AI agents and dispatches coding agents (like Claude Code or Cursor) to fix issues with minimal intervention. - Conversation Intelligence: Analyzes completed sessions to extract behavior patterns, escalations, resolutions, and failure modes. - Automatic Issue Discovery: Groups recurring failures, prioritizes them, and provides trend analysis with failing traces attached. - Real-Time Alerts: Sends notifications via Slack, email, or webhooks when new issues are detected or existing ones escalate. - OTEL Compatibility: Integrates seamlessly with OpenTelemetry pipelines without proprietary formats or vendor lock-in. - Custom Signals: Allows defining custom signals for tracking specific failure modes, policies, or quality checks. - Dataset Management: Automatically builds golden datasets from validated production traces for evaluation and regression testing. - Human Annotations: Enables inline feedback on traces, spans, or outputs to improve structured signal collection. - Advanced Filters: Supports semantic search, exact text, and metadata filters for cohort analysis (e.g., "frustrated users on GPT-5.5 in prod after the May release"). - Failure Mode Clustering: Groups similar failing traces into issues with examples, trends, and affected user metrics for efficient triaging. Latitude is open-source and can be self-hosted. It supports integration with existing OpenTelemetry pipelines and provides an MCP server for running the entire monitoring loop within your editor. The platform is designed to scale with production workloads and offers comprehensive documentation for setup and customization.
AI agent testing and evaluation platform
AI agent testing and evaluation platform that transforms unpredictable agents into reliable production systems. LangWatch provides simulations, evaluations, observability, and governance to ensure your AI agents perform consistently before and after deployment. - Agentic AI Testing: Run realistic user scenarios against your agent to catch issues before production - LLM Evaluation: Measure response quality and accuracy to ship agents that hold up in production - LLM Observability: Trace every agent step and monitor cost and latency with full production visibility - AI Governance: Govern every model, key, and tool with virtual keys, budgets, routing policies, and audit trails - Prompt Management: Version, deploy, and A/B test prompts as code with full history and GitHub sync - Voice AI Testing: Simulate and test voice AI agents at scale before customer interactions - LLM Red-teaming: Uncover safety and security gaps through simulated attacks - Spec-driven Testing: Automatically turn requirements into agent tests for continuous improvement - Self-improving Loops: Set up automated agent improvement cycles based on production traces - CI/CD Integration: Run scenarios locally and in CI pipelines with no separate setup LangWatch is open source and can be self-hosted in 15 minutes. The platform supports local development, CI/CD integration, and enterprise-grade deployment options. Documentation is available at docs.langwatch.ai. Technologies include: - TypeScript/JavaScript for core platform - PostgreSQL for data storage - Redis for caching and real-time features - Docker for containerization - Kubernetes for orchestration (enterprise deployments) Self-hosting is supported via Docker Compose and Kubernetes Helm charts. Enterprise features include advanced security, SSO, and dedicated support.
Open source AI observability and harness platform
OpenLIT is an open-source platform designed to streamline AI-native development workflows by providing comprehensive observability, evaluation, and management capabilities for LLMs and AI agents. - AI Observability: Trace LLMs and AI agents using OpenTelemetry for real-time monitoring and debugging. - LLM Evaluations: Run automated evaluations to assess model performance and reliability. - Prompt Management: Centralize and version-control prompts for consistent AI outputs. - Model Comparison: Compare different AI models to optimize cost, performance, and accuracy. - API Key Vault: Securely store and manage API keys for AI services. - Self-Hosting: Deploy OpenLIT on-premises or in your cloud for full data control. - Apache 2.0 License: Free and open-source under a permissive license. OpenLIT is built with modern technologies and supports self-hosting for complete data privacy. The platform is designed to integrate seamlessly with existing AI workflows and infrastructure, making it ideal for enterprises and developers prioritizing control and compliance.
Open source MLOps experiment tracking platform
The open source platform for tracking, optimizing, and collaborating on machine learning experiments. Built for modern ML teams, mlop provides experiment tracking, parameter and gradient monitoring, real-time alerts, and seamless integration with your development workflow. Compatible with Weights & Biases API for easy migration. - Experiment Tracking: Monitor model accuracy, performance metrics, and training progress over time - Parameter & Gradient Tracking: Track model parameters and gradients in real-time with visualizations - Reproducibility: Track experiments, model versions, and uncommitted files in real-time - Real-time Alerts: Receive email notifications and critical issue alerts about model performance - Multi-media Tracking: Log images, videos, and other media alongside metrics - Git Integration: Automatic tracking of Git status and uncommitted changes - Weights & Biases Compatibility: 100% API compatible for easy migration - Open Source: Community-driven development with enterprise features mlop is open source and can be self-hosted. The platform is built for scalability and integrates seamlessly with existing ML pipelines and development workflows. For more information, visit the GitHub repository.
Privacy-focused modern web browser
Flow Browser is a modern, privacy-focused web browser designed for speed, security, and seamless browsing. With a minimalist interface and powerful features, it enhances productivity while prioritizing user privacy. - Privacy-First: Built-in adblocker and tracker protection for a cleaner browsing experience. - Multiple Profiles: Separate settings and extensions for different user profiles. - Spaces & Pinned Tabs: Organize tabs into spaces and pin frequently used ones for quick access. - Command Palette: Quick access to browser features via keyboard shortcuts. - Offline Games: Built-in games for entertainment without an internet connection. - Chrome Extensions Support: Full compatibility with Chrome Web Store extensions. - Widevine Support: Stream DRM-protected content from services like Netflix. - Sleep Tabs: Reduce resource usage by putting inactive tabs to sleep. - Customizable UI: Personalize app icons and interface to fit your workflow. Flow Browser is a desktop application available for Windows, macOS, and Linux. It does not require self-hosting as it is a client-side application. The source code is open-source and available on GitHub.
About Weights and Biases
Weights & Biases (W&B) is a developer-first platform for tracking machine learning experiments, visualizing results, and collaborating across teams. Whether you're working on a single model or managing a large-scale ML project, W&B provides the tools to log, compare, and share your work efficiently.
Key Features of Weights and Biases
- Experiment Tracking: Log hyperparameters, metrics, and artifacts automatically
- Visualization: Interactive dashboards for comparing runs and analyzing results
- Collaboration: Share projects and reports with your team
- Integration: Works with PyTorch, TensorFlow, scikit-learn, and more
- Reproducibility: Version control for datasets and models
- Reports: Create shareable documents with live visualizations
- CLI & SDK: Easy integration with existing workflows
- Enterprise Features: Role-based access control and advanced security
Tech / Self-Hosting
W&B offers a cloud-hosted solution at wandb.ai with enterprise plans for self-hosting. The platform is built for scalability and supports both small teams and large organizations.