Nota AI vs Hugging Face

Hugging Face has a disclosed valuation of $4.5B, while Nota AI's valuation is not publicly available, making a direct size comparison difficult. Hugging Face employs 250 people.

Hugging Face has raised more in total funding at $395M, compared to Nota AI's $10M — a gap of $385M. Combined, the two companies have completed 6 known funding rounds.

Hugging Face leads with an Awaira Score of 82/100, while Nota AI sits at 38/100. That 44-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Nota AI was founded by Gwang-jin Cho in 2019. Hugging Face was founded by Clement Delangue in 2016. Visit each company's profile on Awaira for a full founder biography.

Nota AI: Nota AI develops neural network compression and optimisation technology for deploying AI models on edge devices and embedded systems with limited compute and power resources, providing tools that compress, quantise, and prune large neural network models into lightweight versions suitable for smartphones, surveillance cameras, automotive chips, and IoT devices. The Seoul company NetsPresso platform automates the model optimisation workflow, reducing the engineering effort required to adapt research models for production hardware deployment.\n\nThe company raised approximately $10 million in venture funding from Korean AI-focused investors. Nota AI targets computer vision and NLP model deployment scenarios where inference must run locally on resource-constrained hardware rather than in the cloud, a requirement driven by latency, privacy, and connectivity considerations. The company customers include semiconductor companies integrating AI features into SoC designs and device manufacturers seeking to add on-device AI capabilities without redesigning around more powerful processors.\n\nNota AI competes in the edge AI model optimisation market against Kneron, Hailo, and embedded AI optimisation tools from semiconductor companies including ARM, Qualcomm, and MediaTek. Its software-first approach, which optimises models for deployment across diverse existing hardware rather than requiring a specific custom chip, addresses the fragmented edge hardware ecosystem where AI model developers need to support many different target processors from different vendors. The Korean semiconductor industry concentration creates a proximate customer base among chipmakers and device manufacturers seeking edge AI capabilities. Hugging Face: Hugging Face is an AI platform company founded in 2016 that has become a central hub for the machine learning community. The company provides open-source libraries, pre-trained models, and collaborative tools for natural language processing and machine learning development. Its flagship offering, the Transformers library, enables developers to access and fine-tune state-of-the-art models for tasks including text classification, translation, summarization, and question-answering. The Hugging Face Model Hub hosts over 700,000 open-source models contributed by researchers and practitioners globally, making it a primary resource for model discovery and sharing. The company also offers inference APIs, AutoTrain for automated model training, and enterprise solutions for organizations deploying models in production. Hugging Face has achieved a $4.5 billion valuation with $395 million in total funding as of its Series D stage. The platform serves a diverse user base ranging from individual researchers to major technology companies. Its competitive positioning centers on democratizing access to advanced AI models through open-source initiatives and community-driven development. The company generates revenue through hosted inference services, enterprise features, and AutoTrain premium offerings. Growth has been driven by increasing demand for accessible AI tools and the model-sharing economy expansion. Hugging Face operates the largest open-source model repository, positioning it as infrastructure for the AI development ecosystem rather than a consumer application.

Hugging Face got there first, launching in 2016 — that's 3 years of extra runway. Nota AI didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Nota AI has about 1-50 employees; Hugging Face has about 250. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Nota AI and Hugging Face compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.