Hugging Face
Open-source models, model hub and inference. Access 500k+ models for LLMs, vision, audio and embeddings with fine-tuning and deployment support.
Why choose Hugging Face
Hugging Face provides unmatched model choice, fine-tuning freedom and cost control for organisations building with open-source AI.
Model choice and flexibility
Access to 500k+ models vs single vendor alternatives. LLMs, vision, audio, embeddings and specialised models all in one hub.
Fine-tuning freedom
Customise any model on proprietary data. LoRA and QLoRA available for efficient fine-tuning without full retraining costs.
Cost control
Free self-hosting, pay-per-request serverless, or predictable endpoint costs. Choose the deployment model that fits your budget.
Transformers library
Python library for working with transformer models. Load any model from Hub with three lines of code. The most popular ML library with 100M+ downloads per month.
Inference API and endpoints
Serverless inference for quick starts at roughly one dollar per million tokens. Dedicated endpoints for production with predictable costs.
Open-source community
5M+ users with extensive documentation and community support. AutoTrain provides no-code fine-tuning for teams without deep ML expertise.
How we use Hugging Face
Self-hosted open models
Deploy Llama 3 70B, Mistral 7B and other leading open models on your own infrastructure for complete control and data privacy.
Fine-tuning on proprietary data
Customise models with LoRA for domain-specific tasks, achieving specialist performance without full retraining costs.
Embeddings and retrieval
Build semantic search with SentenceTransformers for high-quality embeddings that power retrieval-augmented generation systems.
Vision and multimodal
Deploy CLIP, SAM and other vision models for image understanding, document processing and multimodal applications.
Rapid experimentation
Test and compare multiple models quickly to find the right fit before committing to production deployment.
Audio processing
Leverage Whisper and other audio models for transcription, voice processing and speech-to-text workflows.
Deployment options
Hugging Face models can be deployed through multiple channels depending on your volume, cost and control requirements.
Serverless Inference API
Roughly one dollar per million tokens. Instant deployment, good for low to medium volume. Minutes to get started.
Inference Endpoints
Dedicated GPUs for predictable costs and production-grade performance. One to two weeks deployment timeline.
Self-hosted
Full control with your own GPU infrastructure. Lowest cost at scale with complete data sovereignty. Three to four weeks deployment.
Frequently Asked Questions
How does Hugging Face compare to OpenAI?
OpenAI offers proprietary models with a simple API but no fine-tuning access. Hugging Face provides 500k+ open models with full fine-tuning, self-hosting, and lower costs at scale.
Can we fine-tune models on Hugging Face?
Yes, using AutoTrain for no-code fine-tuning or the Transformers library for custom approaches. LoRA and QLoRA are available for efficiency. Deploy via Endpoints or self-hosted.
What does Hugging Face cost?
Models are free. Serverless API runs roughly one dollar per million tokens. Endpoints cost between three hundred and two thousand pounds per month. Self-hosting covers infrastructure only.
Can we self-host Hugging Face models?
Yes. Download any open-weight model and deploy using Transformers, TGI, vLLM, or Ollama. Complete control with no API fees.
Do Hugging Face models match GPT-4 quality?
Llama 3 405B is competitive with GPT-4. Llama 3 70B and Mistral perform similarly to GPT-3.5 Turbo. Smaller models trade capability for speed and cost.
Build with Hugging Face
Whether you need open-source models, fine-tuning on proprietary data or self-hosted deployment, we can help you build with Hugging Face.