Hugging Face development for open, self-hosted AI models
Hugging Face hosts a large public hub of open models and datasets, along with widely used libraries such as Transformers. It is the main route to open-source AI models that can be fine-tuned and run on your own infrastructure.
Using open models gives you control over where data is processed, protection from provider price changes and the ability to fine-tune on your own examples. The Transformers library provides a consistent way to load and run thousands of models for text, vision and audio. The trade-off is operational: you need GPUs or optimised hosting, monitoring and updates. When those demands outweigh the benefits, a hosted API from OpenAI, Anthropic or Google is usually simpler.
What we build with Hugging Face
- Self-hosted language models
- Fine-tuning models on company data
- Text classification and entity extraction
- Embedding models for search
- Speech and vision models
Hugging Face questions
Are open models good enough for business use?
For many focused tasks, such as classification, extraction and search, well-chosen open models perform very well. For complex reasoning, leading hosted models are often stronger.
Are all models on Hugging Face free for commercial use?
No. Each model has its own licence, and some restrict commercial use. We check licences as part of model selection.
How do you choose the right open model?
We shortlist models by task, size, language and licence, then test them on your own examples to compare accuracy, speed and hardware needs.
What does it cost to self-host an open model?
The main cost is GPU or CPU infrastructure, which depends on model size and traffic. For steady, high volumes this can be more predictable than paying per token.
Can we fine-tune an open model on our data?
Yes. Hugging Face libraries support efficient fine-tuning methods that adapt a model with modest hardware, and the resulting weights belong to you, subject to the base model's licence.
Where would a self-hosted model run?
It can run in your own cloud account in the region of your choice, or on your own servers, so prompts and data never leave infrastructure you control.
What are Hugging Face Inference Endpoints?
They are a managed service that deploys a chosen model on dedicated infrastructure with a choice of cloud provider and region, avoiding the need to run servers yourself.
How do you make open models run faster?
We use optimised serving tools, quantisation and batching, and size hardware based on load tests, so response times meet your requirements.
Do open models hallucinate less than hosted ones?
Not inherently. Accuracy depends on the model and the task, so we ground answers in your data where possible and measure outputs against an evaluation set.
Can we use Hugging Face datasets for training?
Public datasets can help, but each has its own licence and quality varies. We review licensing and check the data carefully before using it alongside yours.
More ai & machine learning technologies
Have a project in mind? Let’s talk.
Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.