Predibase vs Scribble Data

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Predibase employs 50-200 people, while Scribble Data has 10-50 employees.

Predibase has raised $16M in disclosed funding across 0 known rounds. Scribble Data's funding history is not publicly available.

Predibase holds the higher Awaira Score at 55/100, compared to Scribble Data's 35/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 20-point gap that reflects meaningful differences in scale or traction.

Predibase was founded by Travis Addair in 2021. Scribble Data was founded by Arun Iyengar in 2019. Visit each company's profile on Awaira for a full founder biography.

Predibase: Predibase provides a fine-tuning and deployment platform purpose-built for large language models, enabling engineering teams to adapt open-source foundation models to their specific domains and production requirements without building the supporting infrastructure from scratch. The platform includes LoRA-based fine-tuning, evaluation pipelines, and a dedicated serving layer optimized for fine-tuned model latency.\n\nThe company raised approximately 16 million USD and serves teams at enterprises and growth-stage companies that have concluded that generic LLM APIs are insufficient for their accuracy requirements and want domain-adapted models they own rather than rent. Predibase is built on top of Ludwig, an open-source declarative ML framework developed at Uber and maintained by the same team.\n\nThe fine-tuning market is growing as production AI teams discover that base models underperform on specialized tasks and that instruction fine-tuning on domain-specific data meaningfully improves output quality. Predibase competes with Modal, Anyscale, and cloud-native fine-tuning services, but its focus on the complete workflow from training through serving rather than raw compute rental positions it closer to a managed ML platform than an infrastructure provider. 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.

Scribble Data was founded first in 2019, giving it 2 years of additional market experience. Predibase was founded later in 2021. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Predibase has approximately 50-200 employees, while Scribble Data has approximately 10-50. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Predibase and Scribble Data are direct competitors — both operate in the ML Platform space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.