Predibase vs Iguazio

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Predibase employs 50-200 people, while Iguazio has 100-500 employees.

Iguazio has raised more in total funding at $72M, compared to Predibase's $16M — a gap of $56M.

Predibase holds the higher Awaira Score at 55/100, compared to Iguazio's 53/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 2-point gap that reflects meaningful differences in scale or traction.

Predibase was founded by Travis Addair in 2021. Iguazio was founded by Yaron Haviv in 2014. 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. Iguazio: Iguazio built an MLOps and real-time AI platform that provided a unified data and model serving infrastructure for machine learning applications requiring low-latency prediction serving and real-time feature computation, enabling data science teams to deploy models into production with integrated feature stores, model serving, and monitoring pipelines. The Tel Aviv company open-source Nuclio serverless framework became widely adopted for event-driven AI inference workloads.\n\nThe company raised approximately $72 million in venture funding before being acquired by McKinsey in 2023, with the acquisition integrating Iguazio MLOps platform into McKinsey QuantumBlack AI consulting and technology practice. Prior to the acquisition, Iguazio had clients including Deutsche Telekom, Moody Analytics, and financial services firms using its platform for real-time fraud detection, recommendation systems, and predictive maintenance applications.\n\nIguazio competed in the MLOps platform market against Databricks, MLflow, Kubeflow, and Weights and Biases, as well as managed MLOps offerings from AWS SageMaker, Google Vertex AI, and Azure ML. The acquisition by McKinsey represented a strategic move to acquire proprietary AI infrastructure that differentiates McKinsey technology consulting from pure advisory competitors. The Iguazio platform provides McKinsey QuantumBlack with an accelerated deployment capability for AI use cases it implements for clients, reducing time-to-production for ML models in regulated enterprise environments.

Iguazio was founded first in 2014, giving it 7 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 Iguazio has approximately 100-500. 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 Iguazio 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.