Scribble Data vs Hugging Face

Hugging Face has a disclosed valuation of $4.5B, while Scribble Data'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 Scribble Data's $3.5M — a gap of $391.5M. Combined, the two companies have completed 5 known funding rounds.

Hugging Face leads with an Awaira Score of 82/100, while Scribble Data sits at 35/100. That 47-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Scribble Data was founded by Arun Iyengar in 2019. Hugging Face was founded by Clement Delangue in 2016. Visit each company's profile on Awaira for a full founder biography.

Scribble Data: Scribble Data builds a feature store and ML data pipeline platform that enables data science teams to define, compute, store, and serve machine learning features in a unified platform, addressing the significant engineering overhead of building and maintaining feature infrastructure for production ML systems. The platform reduces the time data teams spend on data wrangling and feature engineering by providing reusable feature definitions and automated pipeline orchestration.\n\nThe company is in early Seed stage with limited disclosed funding and targets data science teams at financial services companies, digital consumer businesses, and enterprise analytics organizations that run large-scale ML operations. Scribble Data positions its platform as infrastructure that shortens the path from model ideation to production deployment.\n\nFeature stores have emerged as a recognized production ML infrastructure category, with vendors including Tecton, Feast, and Hopsworks competing for enterprise adoption. Scribble Data's India-based team and pricing model target a segment of the market that cannot justify the cost of US-headquartered enterprise ML platform vendors, while still requiring production-grade feature management 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. Scribble Data didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Scribble Data has about 10-50 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 Scribble Data 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.