Can I use Weights & Biases and Hugging Face together?

Yes, many teams use both tools together for different purposes. Use the Stack Builder section above to combine Weights & Biases and Hugging Face with other tools and save your AI toolkit.

Does Weights & Biases or Hugging Face have a free plan?

Weights & Biases is available for free with a free trial. Hugging Face is available for free. Always verify current pricing directly with the vendor.

Weights & Biases vs Hugging Face

Comprehensive side-by-side comparison — features, pricing, performance, and more.

Updated May 2026

Weights & Biases

Accelerate AI development with experiment tracking, model management, and LLM observability.

0.0(0)

FREEMIUM

95% data quality

Hugging Face

The AI community building the future of machine learning.

0.0(0)

FREEMIUM

96% data quality

Trust & Reliability

Weights & Biases

Users:1M-10M

Customers (per vendor):Canva, Microsoft, Toyota +1

SOC 2

HIPAA

Overall Winner: Hugging Face

7.7/10 vs Weights & Biases at 6.7/10

Leads in:

Customizability

Support

Integrations

AI Comparison Summary

Weights & Biases

6.7

avg score

Hugging Face

7.7

avg score

Hugging Face leads overall — particularly in Customizability and Integration Power.

Based on 6 dimensions

How scores are calculated

Overall Winner

Hugging Face

7.7 / 10

Weights & Biases

Best For

Tracking and visualizing machine learning experiments

Optimizing hyperparameters for AI models

Managing and versioning AI models and datasets

Debugging and evaluating LLM applications

Not Ideal For

Non-technical users without ML development experience

Simple data visualization tasks not related to ML experiments

Users looking for a no-code AI development platform

Hugging Face

Best For

Collaborating on machine learning models and datasets

Deploying and sharing AI applications

Researchers and developers building with open-source ML

Teams requiring enterprise-grade security for AI development

Not Ideal For

Non-technical users looking for a simple, no-code AI solution

Businesses requiring fully managed, black-box AI services without customization needs

Users who prefer proprietary, closed-source ML development environments

* Verdict is based on our algorithmic scoring of publicly available data. Learn about our methodology

Weights & Biases is best for

🎨Image & Design8.0📊Data & Analytics7.2💻Coding & Dev7.0

Hugging Face is best for

🤖Automation & Agents9.0🎧Customer Support9.0✍️Writing & Content7.0

Filter by your use case:

Pros & Cons

Weights & Biases

Pros

Comprehensive platform for MLOps lifecycle, from experimentation to production monitoring
Supports a wide range of AI models and frameworks for training and inference
Offers specialized tools for LLM observability, including traces, evaluations, and guardrails
Provides flexible deployment options including cloud-hosted and self-hosted
Includes a free tier for personal development and academic research

Cons

Corporate use is not allowed on the free Personal self-hosted plan
Cloud storage and tracked hours are limited on free and lower-tier plans
Pricing for inference is usage-based and can vary
Requires Docker and Python for self-hosting
No explicit mention of a simple GUI for non-technical users

Hugging Face

Pros

Vast collection of over 2 million models and 500k datasets
Strong open-source community and extensive libraries (Transformers, Diffusers)
Supports multiple modalities including text, image, video, audio, and 3D
Offers flexible compute options for deploying models and applications
Provides enterprise-grade security features like SSO, audit logs, and data residency options
Free tier available for personal use and open-source contributions

Cons

Can be complex for absolute beginners without ML background
Advanced compute and enterprise features can become costly
Requires technical knowledge for full utilization of the platform and libraries
No explicit mention of offline mode — requires active internet connection

Weighted Score — Adjust What Matters to You

Pick a profile or drag sliders — scores and radar update instantly on the right.

Quick profiles:

Ease of Use5

Output Quality5

Value for Money5

Customizability5

Support & Ecosystem5

Integration Power5

Dimension Comparison

Weights & Biases

6.7

/ 10

Hugging Face

7.7

/ 10

Clear winner

Dimension Breakdown

Ease of Use

How easy it is to get started and use daily

Weights & Biases

6.0

Hugging Face

6.0

Output Quality

Quality and accuracy of generated results

Weights & Biases

8.0▲ +1.0

Hugging Face

7.0

Value for Money

Cost vs. features and output quality

Weights & Biases

7.0

Hugging Face

7.0

Customizability

Calculated

Ability to tailor to specific needs

Weights & Biases

6.0

Hugging Face

9.0▲ +3.0

Support & Ecosystem

Documentation, community, and support quality

Weights & Biases

8.0

Hugging Face

9.0▲ +1.0

Integration Power

Calculated

Ability to connect with other tools and workflows

Weights & Biases

5.0

Hugging Face

8.0▲ +3.0

Calculated = derived from verified integration count and API/open-source signals. AI = LLM-assessed from public website content. Methodology

Data is sourced from public information and vendor websites. It may not always be up-to-date — verify pricing and features directly with the vendor before purchasing.Report inaccuracy

Frequently Asked Questions

Which is better, Weights & Biases or Hugging Face?

Both are strong options. Weights & Biases has a rating of 0.0/5 (0 reviews), while Hugging Face has a rating of 0.0/5 (0 reviews). The best choice depends on your specific use case, budget, and workflow requirements.

Is Weights & Biases cheaper than Hugging Face?

Weights & Biases starts at free (freemium model) with a free trial. Hugging Face starts at free (freemium model). Use the Cost Calculator above for a detailed estimate.

What are the main differences between Weights & Biases and Hugging Face?

Weights & Biases — "Accelerate AI development with experiment tracking, model management, and LLM observability.". Hugging Face — "The AI community building the future of machine learning.". Key differences include their pricing models, platform support, integrations, and technical capabilities. See the Feature Comparison table above for a complete side-by-side breakdown.

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