Open-source models and inference for AI that fits you exactly.
Hugging Face is the central hub for open-source AI and hosts hundreds of thousands of models and datasets, along with the tools to use them. Through the Transformers library you load a pre-trained model in a few lines and run it locally or on your own infrastructure, without sending data to an outside service. Here you find tools for almost any task, from text recognition through translation to image analysis. We turn to it when custom or self-hosted models are called for, rather than relying solely on a closed interface.
More in the documentationWe reach for Hugging Face when data must not leave the house, or a specialised task calls for a model of its own that no large API offers. A self-hosted model for classification or embeddings runs on your infrastructure and costs nothing extra per call. That fits privacy-sensitive projects and features that run around the clock at high volume.
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("byteNative ships clean, dependable software")
print(result)Good to know
Always check a model's licence before you deploy it; not everything on the hub is cleared for commercial use. A technically perfect model is no use to you if its licence forbids using it in your product.
More tools we work with in the same area.
OpenAI
GPT models for text, analysis and intelligent features.
Anthropic
Claude models for secure, high-performance AI integrations.
RAG
Retrieval-augmented generation for AI answers based on your own data.
Vector Databases
Semantic search and knowledge bases for AI applications.
LangChain
A framework for orchestrating LLM workflows, agents and tools.
MCP
Model Context Protocol for a clean connection between AI and your tools.
You don't have to decide that, it's our job. Tell us about your plans.