Federated computing platform for privacy-preserving AI across siloed data
Rhino Federated Computing builds infrastructure for organizations to train AI models on distributed data without moving it—a pattern gaining urgency in healthcare, pharma, and financial services. The stack is engineering-heavy (7 of 10 active hires are senior/principal engineers) and production-focused: Python backend (Django/FastAPI), Kubernetes orchestration, GCP/AWS multi-cloud, and observability across Prometheus/Grafana. The project pipeline shows they're balancing platform scaling with customer deployment support and compliance (SOC 2), suggesting a move from early-stage product toward operational maturity.
Rhino Federated Computing provides a data collaboration platform that enables organizations to build AI models on sensitive, siloed data without centralizing it. The platform spans data preparation, model development, privacy enforcement (differential privacy), and secure custom-code deployment across on-premises and multi-cloud infrastructure. Founded in 2021, the company operates R&D centers in Boston and Tel Aviv and works with over 60 organizations, with notable traction in healthcare (14 of 20 hospitals in Newsweek's 'Best Smart Hospitals' ranking) and top-tier biopharma companies, expanding into financial services and ecommerce.
Python (Django/FastAPI), React/TypeScript frontend, Kubernetes + Docker orchestration, Terraform/Terragrunt IaC, GCP/AWS cloud, and observability via Prometheus/Grafana/VictoriaMetrics. Also uses Model Context Protocol, ArgoCD, and NGINX.
Boston, Massachusetts, with an R&D center in Tel Aviv. Founded in 2021, the company employs 51–200 people and is privately held.
Rhino Federated Computing's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.