Decision comparison
ClearML vs Weights & Biases
ClearML and Weights & Biases represent two distinct philosophies in MLOps tooling. ClearML is the full-stack platform that gives ML teams complete control over every stage of the ML lifecycle, from experiment tracking and pipeline automation to data versioning, model serving, and GPU orchestration, all backed by an open-source core and dramatically lower pricing. Weights & Biases is the focused experiment tracking and collaboration platform that delivers the most polished visualization experience in the market, with expanding capabilities for LLM evaluation and model management. The choice between them ultimately depends on whether your team needs a broad MLOps platform with self-hosting flexibility or a specialized tracking tool with best-in-class collaboration and visualization.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
These are different kinds of product — ML Platform and Experiment Tracking.
Quick Comparison
| Decision factor | ClearML | Weights & Biases |
|---|---|---|
| Primary Focus | Full-stack MLOps platform covering experiment tracking, pipelines, data versioning, model serving, and GPU orchestration | Experiment tracking, visualization, and collaboration platform with expanding LLM evaluation capabilities |
| Deployment Model | Open-source self-hosted, managed hosted servers, or enterprise VPC and on-prem including air-gapped deployments | Managed SaaS cloud, single-tenant option in Enterprise, and self-hosted server via Docker for personal use |
| Experiment Tracking | Auto-magical logging with two lines of Python; captures hyperparameters, metrics, git diffs, and uncommitted code changes | Rich interactive dashboards for logging metrics, comparing runs, visualizing model performance, and sharing results |
| Pipeline Orchestration | Built-in pipeline automation with ClearML Agent for remote execution, queuing, and GPU cluster management | Launch for running hyperparameter sweeps and jobs; relies on external tools for full pipeline orchestration |
| Pricing Model | Open Source free, $15/unknown tier | Free (Free tier), $60/mo (Pro), CONTACT US (Enterprise) |
| Best For | Teams wanting a complete, self-hosted MLOps platform with full data sovereignty and lower per-seat costs | Teams that value polished visualization, seamless collaboration, and a managed cloud-first experience |
ClearML
- Primary Focus:
- Full-stack MLOps platform covering experiment tracking, pipelines, data versioning, model serving, and GPU orchestration
- Deployment Model:
- Open-source self-hosted, managed hosted servers, or enterprise VPC and on-prem including air-gapped deployments
- Experiment Tracking:
- Auto-magical logging with two lines of Python; captures hyperparameters, metrics, git diffs, and uncommitted code changes
- Pipeline Orchestration:
- Built-in pipeline automation with ClearML Agent for remote execution, queuing, and GPU cluster management
- Pricing Model:
- Open Source free, $15/unknown tier
- Best For:
- Teams wanting a complete, self-hosted MLOps platform with full data sovereignty and lower per-seat costs
Weights & Biases
- Primary Focus:
- Experiment tracking, visualization, and collaboration platform with expanding LLM evaluation capabilities
- Deployment Model:
- Managed SaaS cloud, single-tenant option in Enterprise, and self-hosted server via Docker for personal use
- Experiment Tracking:
- Rich interactive dashboards for logging metrics, comparing runs, visualizing model performance, and sharing results
- Pipeline Orchestration:
- Launch for running hyperparameter sweeps and jobs; relies on external tools for full pipeline orchestration
- Pricing Model:
- Free (Free tier), $60/mo (Pro), CONTACT US (Enterprise)
- Best For:
- Teams that value polished visualization, seamless collaboration, and a managed cloud-first experience
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | ClearML | Weights & Biases |
|---|---|---|
| Docker Hub pulls(Product adoption) | 89.0k | 4.1M |
| GitHub commits, 90d(Product adoption) | 67 | 477 |
| GitHub stars(Product adoption) | 6,500+ | 11,000+ |
| Search interest(Market interest) | 0 | 0 |
| PyPI weekly downloads(Product adoption) | 221.7k | 3.2M |
| Stack Overflow questions(Community interest) | 54 | 139 |
| Hacker News mentions, 90d(Community interest) | Not available | 0 |
| Hugging Face downloads(Product adoption) | Not available | 2.7k |
| Hugging Face likes(Product adoption) | Not available | 31 |
| npm weekly downloads(Developer adoption) | Not available | 8.2k |
| Product Hunt comments(Community interest) | Not available | 8 |
| Product Hunt rating(Community interest) | Not available | 5.0/5 |
| Product Hunt reviews(Community interest) | Not available | 3 |
| Product Hunt votes(Community interest) | Not available | 110 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
ClearML
September 21, 2026Package vulnerabilities
PyPI · clearml@2.1.12
0 vulnerabilities
across 1 package
Repository security score
Not available
Weights & Biases
September 21, 2026Package vulnerabilities
npm · @wandb/sdk@0.5.1 · PyPI · wandb@0.30.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
ClearML
Feature Comparison
| Feature | ClearML | Weights & Biases |
|---|---|---|
| Experiment Tracking & Visualization | ||
| Automatic Logging | Two-line integration with auto-capture of hyperparameters, metrics, console output, git diffs, and package versions | SDK-based logging with rich support for TensorFlow, PyTorch, Keras, JAX, and scikit-learn frameworks |
| Dashboard & Visualization | Project dashboard with 2D/3D metric plots, experiment comparisons, and data sample visualization | Industry-leading interactive dashboards with custom panels, parallel coordinates, and collaborative reports |
| Experiment Comparison | Side-by-side comparison of experiments, datasets, and models with diff views and search capabilities | Advanced run comparison with grouped tables, custom queries, and shareable comparison views |
| Pipeline & Orchestration | ||
| Pipeline Automation | Full pipeline orchestration with decorators, dependency injection, result caching, triggers, and automation rules | Launch for job orchestration and sweeps; full pipeline orchestration requires external tools like Airflow or Kubeflow |
| Remote Execution | ClearML Agent with queue-based execution across GPU clusters, cloud VMs, and on-prem infrastructure | Launch for queueing jobs to external compute; no built-in agent-based remote execution system |
| Hyperparameter Optimization | Built-in HPO controller with grid search, random search, and Bayesian optimization logged to the experiment system | Sweeps with grid, random, and Bayesian strategies directly integrated into the W&B experiment tracking interface |
| Data & Model Management | ||
| Dataset Versioning | Built-in dataset versioning tied to experiments with support for S3, GCS, Azure, and NAS object storage | Artifacts system for versioning datasets and models; data versioning available but not as deeply integrated |
| Model Registry | Model repository with versioning and direct deployment to ClearML Serving endpoints | Model registry with lineage tracking, aliases, lifecycle management, and CI/CD automation hooks |
| Model Serving | Built-in model serving with Nvidia-Triton backend, traffic routing, monitoring, and Kubernetes integration | No built-in model serving; integrates with external serving platforms for deployment |
| Infrastructure & Compute | ||
| GPU Management | Infrastructure Control Plane with fractional GPUs, multi-cluster support, and priority-based job scheduling | GPU usage monitoring and tracking within experiments; no built-in GPU cluster management |
| Cloud Autoscaling | Cloud autoscaling for AWS, GCP, and Azure available on Pro tier and above | No built-in autoscaling; relies on external cloud infrastructure management |
| Kubernetes Integration | Native Kubernetes integration for task scheduling, agent deployment, and cluster orchestration | Kubernetes support through Launch for job queueing; self-hosted server deployable on Kubernetes |
| Collaboration & Security | ||
| Team Collaboration | Project-based collaboration with dashboards and reports; team features available on Pro tier | Unlimited teams, shared workspaces, collaborative reports, and team-based access controls on Pro tier |
| Enterprise Security | Multi-tenancy with isolated networks and storage, SSO integration, and air-gapped deployment support | HIPAA compliance option, customer-managed encryption keys, SSO, SCIM provisioning, and audit logs |
| Access Control | Role-based access control with quota management and granular billing capabilities on Scale and Enterprise tiers | Team-based access controls on Pro, custom roles and automated user provisioning on Enterprise |
Experiment Tracking & Visualization
Automatic Logging
Dashboard & Visualization
Experiment Comparison
Pipeline & Orchestration
Pipeline Automation
Remote Execution
Hyperparameter Optimization
Data & Model Management
Dataset Versioning
Model Registry
Model Serving
Infrastructure & Compute
GPU Management
Cloud Autoscaling
Kubernetes Integration
Collaboration & Security
Team Collaboration
Enterprise Security
Access Control
Which approach fits
ClearML and Weights & Biases represent two distinct philosophies in MLOps tooling. ClearML is the full-stack platform that gives ML teams complete control over every stage of the ML lifecycle, from experiment tracking and pipeline automation to data versioning, model serving, and GPU orchestration, all backed by an open-source core and dramatically lower pricing. Weights & Biases is the focused experiment tracking and collaboration platform that delivers the most polished visualization experience in the market, with expanding capabilities for LLM evaluation and model management. The choice between them ultimately depends on whether your team needs a broad MLOps platform with self-hosting flexibility or a specialized tracking tool with best-in-class collaboration and visualization.
When each approach fits
Choose ClearML if:
Choose ClearML if your team needs a complete MLOps platform that goes beyond experiment tracking. ClearML delivers pipeline orchestration, dataset versioning, model serving, and GPU cluster management in a single tool, with an open-source core that provides full data sovereignty. At $15 per user per month for the Pro tier, it costs a quarter of what W&B charges, making it the practical choice for teams that want the full ML infrastructure stack without enterprise-level bills. Organizations running on-prem GPU clusters, teams in regulated industries requiring air-gapped deployment, and budget-conscious startups will get the most value from ClearML.
Choose Weights & Biases if:
Choose Weights & Biases if experiment tracking, visualization, and team collaboration are your top priorities. W&B offers the most polished dashboard experience in the MLOps space, with interactive panels, parallel coordinates, collaborative reports, and advanced run comparison that ClearML cannot match. The platform's expanding Weave framework for LLM evaluation and tracing positions it well for teams working on GenAI applications. Research teams publishing papers, organizations that already handle orchestration through Airflow or Kubeflow, and teams that value a managed cloud experience with HIPAA compliance and enterprise security will find W&B the better fit.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between ClearML and Weights & Biases?
ClearML is a full-stack, open-source MLOps platform that covers the entire ML lifecycle including experiment tracking, pipeline orchestration, data versioning, model serving, and GPU cluster management. Weights & Biases focuses primarily on experiment tracking, visualization, and collaboration, with an expanding set of capabilities for LLM evaluation and model registry. ClearML gives you the complete infrastructure to build and deploy ML systems, while W&B gives you the best-in-class experiment tracking and team collaboration experience.
How does ClearML pricing compare to Weights & Biases pricing?
The vendors publish different pricing bases, so a direct per-seat comparison is not supported. The open-source edition is free forever with unlimited experiments and full platform features. The Pro tier costs $15 per user per month. Weights & Biases offers a free tier limited to 5 seats and 5 GB of storage, with the Pro plan starting at $60/month, billed monthly. Because W&B publishes a plan starting price rather than a per-user price, that seat-by-seat team total is not supported. ClearML also offers a self-hosted option that eliminates recurring cloud fees entirely.
Can ClearML replace Weights & Biases for experiment tracking?
Yes. ClearML provides auto-magical experiment tracking that captures hyperparameters, metrics, console output, git diffs, and package versions with just two lines of code. It supports the same deep learning frameworks as W&B, including TensorFlow, PyTorch, Keras, and scikit-learn. However, W&B's interactive dashboards and visualization capabilities are more polished and feature-rich. Teams that rely heavily on custom visualization panels, collaborative reports, and advanced run comparison may find W&B's tracking experience superior.
Which platform is better for self-hosted deployments?
ClearML is the clear winner for self-hosted deployments. Its open-source edition provides full platform access with no feature restrictions, and the Enterprise tier supports VPC, on-prem, and air-gapped deployments. W&B offers a self-hosted server option through Docker, but the personal edition is limited to one user and restricted to non-corporate use. W&B's full self-hosted Enterprise deployment requires a custom contract with enterprise pricing.
Which tool has a larger community and ecosystem?
Weights & Biases has a sizable community footprint with over 11,000+ GitHub stars compared to ClearML's 6,600 stars. W&B has extensive adoption among ML researchers and is frequently cited in academic papers and benchmarks. ClearML has a strong and active open-source community but a focused user base overall. Both tools are written in Python and have active development, with ClearML's latest release at v2.1.5 and W&B at v0.28.1 as of July 16, 2026.