MLflow vs Weights & Biases: Observability, Prompts & Evals Comparison
Compare MLflow and Weights & Biases side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 27, 2026
The short answer
Choose MLflow if truly open source with Linux Foundation governance — no vendor lock-in, Apache 2.0 license.
Choose Weights & Biases if free tier for personal projects and academic research provides excellent value.
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Open Source | Freemium |
| Best For | ML engineers and AI teams, especially those in the Databricks ecosystem | ML engineers and researchers who need comprehensive experiment tracking |
| Website | mlflow.org | wandb.ai |
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Pros & Cons: MLflow vs Weights & Biases
- +Truly open source with Linux Foundation governance — no vendor lock-in, Apache 2.0 license
- +Massive ecosystem with 900+ contributors and integrations with 100+ AI frameworks across Python, TypeScript, Java, and R
- +Comprehensive GenAI platform with OpenTelemetry tracing, 50+ eval metrics, prompt management, and built-in AI Gateway
- +Unmatched adoption at 60M+ monthly downloads and 19,000+ companies globally
- +Unique combination of traditional MLOps and modern GenAI observability in a single platform
- −No built-in user management or RBAC in the open-source version — teams need Databricks or custom solutions for access control
- −Steep setup complexity for shared team deployments requiring proper storage backends, auth, and networking
- −Best features like Unity Catalog integration and serverless deployment require Databricks, creating soft vendor lock-in
- −GenAI-specific UI and developer experience less polished than LLM-native tools like Langfuse or LangSmith
- +Free tier for personal projects and academic research provides excellent value
- +Comprehensive experiment tracking and model versioning capabilities
- +Strong visualization tools help teams understand model performance
- +Acquisition by CoreWeave provides infrastructure synergies and stability
- −Pricing can become expensive for large teams and enterprises
- −Additional storage costs can add up quickly for data-intensive workflows
- −Learning curve for advanced features may slow initial adoption
- −Platform complexity may be overkill for simple ML projects
Pricing: MLflow vs Weights & Biases
- · Full platform access
- · Self-hosted
- · Apache 2.0 license
- · All GenAI features included
- · Managed MLflow
- · Cloud-hosted tracking server
- · Integrated with Databricks
- · Everything in Standard
- · Serverless compute
- · Unity Catalog integration
- · Everything in Premium
- · Advanced security
- · Compliance controls
- · Personal projects
- · Limited storage
- · Community support
- · Core features
- · Full product features
- · Enhanced storage
- · Email support
- · Team collaboration
- · For academic research
- · Unlimited tracked hours
- · 200GB cloud storage
- · Up to 100 seats
- · Custom pricing
- · Advanced features
- · Dedicated support
- · SLA guarantees
When to Choose MLflow vs Weights & Biases
- LLM observability & tracing
- Automated evaluation
- Prompt optimization
- ML experiment tracking and comparison
- Model training run management
- Team collaboration on ML projects
About MLflow
MLflow is the leading open-source platform for managing the end-to-end machine learning lifecycle, now expanded into a comprehensive GenAI engineering platform. Created by Matei Zaharia (also the creator of Apache Spark) at Databricks in 2018 and donated to the Linux Foundation in 2020, MLflow has grown to over 20,000 GitHub stars and 60 million monthly downloads, making it one of the most widely adopted ML tools in the world.
With the release of MLflow 3.0 in June 2025, the platform underwent a major pivot to become a unified AI engineering platform for agents, LLMs, and ML models. The GenAI capabilities include OpenTelemetry-compatible tracing for LLM observability, 50+ built-in evaluation metrics with LLM-as-judge support, prompt versioning and optimization, and a built-in AI Gateway providing unified API access to all major LLM providers with rate limiting and cost control. The platform auto-traces 50+ AI frameworks including OpenAI, Anthropic, LangChain, LlamaIndex, and DSPy.
MLflow is used by over 19,000 companies globally, including Fortune 500 organizations like Amazon, Microsoft, Google, and BNP Paribas. While it is 100% free and open source under the Apache 2.0 license, Databricks offers a fully managed MLflow experience integrated into their cloud data platform. MLflow's unique strength is combining traditional MLOps capabilities (experiment tracking, model registry, deployment) with modern GenAI observability — something no other tool in the category offers.
About Weights & Biases
Weights and Biases (W and B) is a machine learning operations platform founded in 2017 by Chris Van Pelt, Lukas Biewald, and Shawn Lewis in San Francisco, California. The platform offers performance visualization tools for machine learning, helping companies track models, visualize performance, and automate training and model improvement workflows. W and B provides comprehensive experiment tracking, model versioning, and collaborative tools for ML teams. In March 2025, Weights and Biases was acquired by CoreWeave, strengthening its position in the AI infrastructure ecosystem. The company raised a total of USD 250M from investors including CoreWeave, Coatue, Bloomberg Beta, and Insight Partners. W and B offers a free tier for personal projects and provides academic institutions with free Pro licenses for non-profit research, including unlimited tracked hours, 200GB cloud storage, up to 25GB/month of Weave data ingestion, and up to 100 seats. Paid plans start at USD 60/month with additional cloud storage available at USD 0.03 per GB.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
Browse all Observability, Prompts & Evals tools →