Can I use Google ADK and HuggingFace Agent together?

Yes, many teams use both tools together for different purposes. Use the Stack Builder section above to combine Google ADK and HuggingFace Agent with other tools and save your AI toolkit.

Does Google ADK or HuggingFace Agent have a free plan?

Google ADK is a paid tool. HuggingFace Agent is a paid tool. Always verify current pricing directly with the vendor.

Google ADK vs HuggingFace Agent

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

Updated June 2026

Google ADK

Google

An open-source, code-first Python toolkit for building sophisticated AI agents.

0.0(0)

OPEN_SOURCE

81% data quality

HuggingFace Agent

Hugging Face

Collaborate on state-of-the-art AI models, datasets, and applications with the ML community.

0.0(0)

OPEN_SOURCE

94% data quality

Trust & Reliability

Google ADK

Trust data not available yet

HuggingFace Agent

Uptime:99.980%Status page

Users:10K-100K

Customers (per vendor):Ai2, AI at Meta, Amazon +5

GDPR

Overall Winner: HuggingFace Agent

7.5/10 vs Google ADK at 5.2/10

Leads in:

Output Quality

Customizability

Support

AI Comparison Summary

Google ADK

5.2

avg score

HuggingFace Agent

7.5

avg score

HuggingFace Agent leads overall — particularly in Ease of Use and Customizability and 2 more.

Google ADK has an edge in Value for Money.

Based on 6 dimensions

How scores are calculated

Overall Winner

HuggingFace Agent

7.5 / 10

Google ADK

Best For

Developing custom AI agents for specific tasks

Building multi-agent orchestration systems

Experimenting with agentic AI workflows

Deploying AI agents with fine-grained control

Not Ideal For

Non-technical users seeking no-code agent builders

Businesses needing a fully managed, hosted agent platform

Users looking for pre-built, off-the-shelf AI agents

HuggingFace Agent

Best For

Building custom AI agents for complex tasks for developers

Collaborating on machine learning projects for researchers and engineers

Deploying and scaling machine learning applications and agents

Sharing and discovering pre-trained AI models and datasets

Not Ideal For

Non-technical users seeking a no-code autonomous agent solution

Users looking for a single, pre-built, fully autonomous agent for specific tasks without development

Organizations requiring a fully managed, black-box AI agent solution without customization

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

Google ADK is best for

🤖Automation & Agents7.7

HuggingFace Agent is best for

🤖Automation & Agents6.7

Filter by your use case:

Pros & Cons

Google ADK

Pros

Open-source and free to use, fostering community contributions
Code-first approach provides maximum flexibility and control for developers
Designed for building sophisticated single and multi-agent systems
Backed by Google, implying robust engineering and potential for future development

Cons

Requires strong Python programming skills, not suitable for non-developers
No visual interface or low-code options for rapid prototyping
Lacks built-in cloud hosting or managed services, requiring self-deployment
Limited explicit integrations mentioned, relying on developer implementation

HuggingFace Agent

Pros

Extensive collection of 2M+ models and 500k+ datasets on the Hub
Comprehensive open-source libraries (Transformers, Diffusers, Datasets) for various ML tasks
Supports multiple modalities including text, image, video, audio, and 3D
Offers flexible compute options with GPU access for Spaces and Inference Endpoints
Strong community collaboration features with Git-based versioning and evaluation leaderboards
Enterprise-grade security features like SSO, audit logs, and granular access controls

Cons

Requires technical knowledge and coding skills for agent development
Advanced features and dedicated compute require paid subscriptions
Not a ready-to-use autonomous agent, but a framework for building them
Free tier has limited storage and inference credits

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

Google ADK

5.2

/ 10

HuggingFace Agent

7.5

/ 10

Clear winner

Dimension Breakdown

Ease of Use

How easy it is to get started and use daily

Google ADK

3.0

HuggingFace Agent

6.0▲ +3.0

Output Quality

Quality and accuracy of generated results

Google ADK

7.0

HuggingFace Agent

8.0▲ +1.0

Value for Money

Cost vs. features and output quality

Google ADK

9.0▲ +2.0

HuggingFace Agent

7.0

Customizability

Calculated

Ability to tailor to specific needs

Google ADK

5.0

HuggingFace Agent

8.0▲ +3.0

Support & Ecosystem

Documentation, community, and support quality

Google ADK

6.0

HuggingFace Agent

8.0▲ +2.0

Integration Power

Calculated

Ability to connect with other tools and workflows

Google ADK

1.0

HuggingFace Agent

8.0▲ +7.0

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, Google ADK or HuggingFace Agent?

Both are strong options. Google ADK has a rating of 0.0/5 (0 reviews), while HuggingFace Agent has a rating of 0.0/5 (0 reviews). The best choice depends on your specific use case, budget, and workflow requirements.

Is Google ADK cheaper than HuggingFace Agent?

Google ADK starts at free (open_source model). HuggingFace Agent starts at free (open_source model). Use the Cost Calculator above for a detailed estimate.

What are the main differences between Google ADK and HuggingFace Agent?

Google ADK — "An open-source, code-first Python toolkit for building sophisticated AI agents.". HuggingFace Agent — "Collaborate on state-of-the-art AI models, datasets, and applications with the ML community.". 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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