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. Combined, the two companies have completed 7 known funding rounds.

Predibase leads with an Awaira Score of 55/100, while Iguazio sits at 53/100. That 2-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

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 got there first, launching in 2014 — that's 7 years of extra runway. Predibase didn't arrive until 2021. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Predibase has about 50-200 employees; Iguazio has about 100-500. 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 Predibase and Iguazio compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.