transformers
A platform for building, sharing, and deploying AI models with integrated collaboration and compute resources
- Vendor
- Huggingface
- Also known as
- transformers
Available worldwide
What is transformers?
Hugging Face is a hub for machine learning where users can host models, datasets, and interactive applications (Spaces), along with cloud storage and GPU compute infrastructure for model development and inference.
transformers pricing
Plans, per-tier features and add-ons, dated and linked to live pricing. Pricing changes often; always verify at source before you rely on it.
Pro at $9/month, Team at $20/user/month, Enterprise custom pricing. Free tier with CPU compute. Storage starts at $8/TB/mo with volume discounts.
Pro
Enhanced experience for individual users
- 2x public storage capacity
- $2 monthly inference credits
- 8x ZeroGPU quota with highest priority
- PRO badge
Team
Collaborative workspace for growing teams
Enterprise
Contact salesCustom features and dedicated support
Add-ons
- Cloud Storage (HF Hub)$12 per TB + $10 per TB + $8 per TB
- Cloud Storage (AWS S3)$23 per TB
- Cloud Storage (Backblaze Overdrive)$15 per TB
Platform & deployment
Independently observed- CLI
- Web
- Cloud / SaaS
Integrations (27)
Independently observed- Kaggle⇄•
- Papers with Code→•
- PyTorch
- DeepSpeed
- FSDP
- PyTorch-Lightning
- Axolotl
- Unsloth
- vLLM
- SGLang
- TGI
- llama.cpp
- mlx
- Amazon Bedrock
- AWS
- Google Cloud
- Microsoft Azure
- Diffusers
- Datasets
- Gradio
- Accelerate
- PEFT
- TRL
- Optimum
- Safetensors
- Bitsandbytes
- PyCharm•
Security & compliance
Known vulnerabilities: 24 (5 in the last 12 months), max severity CRITICAL sourcea count reflects scale & disclosure, not quality
transformers FAQ
Common questions about transformers, answered from independent, dated evidence.
What is transformers?
A central library for defining and using state-of-the-art machine learning models with compatibility across multiple training frameworks, inference engines, and deployment platforms. Works with PyTorch, FSDP, DeepSpeed, vLLM, and other tools for both inference and training workflows. It is indexed under AI Tools.
Source: https://huggingface.co/pricing
Is transformers free to use?
transformers is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.
Source: https://huggingface.co/pricing
What platforms does transformers support?
transformers supports the web and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Source: https://huggingface.co/pricing
Can transformers be self-hosted?
Yes. transformers can be deployed cloud / SaaS and self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://huggingface.co/pricing
What does transformers integrate with?
We have confirmed 23 integrations for transformers, including PyTorch, DeepSpeed, FSDP, Axolotl, Unsloth, vLLM, SGLang and TGI, plus 15 more. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Source: https://huggingface.co/pricing
What security certifications does transformers have?
We have independently confirmed GDPR for transformers. Certifications we do not list are ones we have not been able to verify from public sources, which is not the same as transformers not holding them. Always confirm compliance directly before you rely on it.
Source: https://huggingface.co/privacy
Is transformers open source?
Yes. transformers is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
transformers alternatives
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Compare transformers
Side by side against other ai tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for transformers, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Pricing transparency | 100 | 0.08 | 8.4 | ✓ |
| Development activity | 63 | 0.09 | 5.9 | ✓ |
| Release cadence | 96 | 0.05 | 5.0 | ✓ |
| Dependent projects | 75 | 0.06 | 4.7 | ✓ |
| Price level | 80 | 0.05 | 4.2 | ✓ |
| Integrations | 40 | 0.09 | 3.8 | ✓ |
| Reliability | 50 | 0.06 | 3.2 | ✓ |
| Security score | 65 | 0.04 | 2.7 | ✓ |
| Stars | 99 | 0.03 | 2.6 | ✓ |
| Capabilities | 0 | 0.09 | 0.0 | - |
| Security posture | 10 | 0.06 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 100 | mediumsource · 2026-10-15 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 7 items | mediumsource · 2026-09-10 · 66% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 24 | mediumsource · 2026-08-14 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-10-15 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-10-15 · 95% |
Market
| Attribute | Value | Evidence |
|---|---|---|
| Availability | PrimaryMarkets: … · AvailabilityScope: global · AvailableCountries: … · NotAvailableCountries: … | mediumsource · 2026-10-15 · 50% |
Pricing
Release
Reliability
| Attribute | Value | Evidence |
|---|---|---|
| Status page | Yes | mediumsource · 2026-08-05 · 60% |
Security
| Attribute | Value | Evidence |
|---|---|---|
| Disclosure policy | Yes | mediumsource · 2026-10-15 · 60% |
| Gdpr | Yes | highsource · 2026-08-05 · 75% |
| Scorecard | 6.5 | highsource · 2026-10-15 · 90% |
| Vulnerabilities | Count: 24 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=transformers&per_page=100 · Last 12m: 5 · Max severity: CRITICAL | highsource · 2026-10-15 · 90% |
| Trust center | https://huggingface.co/blog/security-incident-july-2026 | mediumsource · 2026-10-15 · 60% |
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