Luminary - 2026 Pricing, Features, Reviews & Alternatives

Luminary is a full-stack Physics AI platform designed to operationalize physics-driven simulations across the complete model lifecycle, from data unification through production deployment and governance. The platform serves engineering organizations in aerospace, automotive, defense, industrial, and electronics sectors that require validated, production-ready physics AI capabilities for mission-critical applications. It integrates GPU-native solvers, data pipeline management, model training infrastructure, and flexible deployment options ranging from software-as-a-service to air-gapped environments within a single unified system.

The platform encompasses four primary operational stages that form an integrated workflow. The data unification stage enables organizations to connect existing solvers, run simulations on the platform, or ingest test and operational data from disparate sources to construct unified training datasets while preserving physical context and data provenance. The build and validate stage includes support for fluids, structures, thermal, and electromagnetics disciplines, built-in evaluation criteria, and the ability to train and deploy proprietary, customer-developed, and third-party models within the same environment. The production deployment stage publishes trained models to production endpoints and integrates inference into engineering workflows through application programming interfaces, software development kits, and modular interfaces that embed inference within CAD and CAE pipelines. The governance stage tracks model performance across the product lifecycle, manages version control, triggers retraining as operational data accumulates, and maintains an audit trail documenting every prediction.

Luminary offers three deployment architectures to address security, compliance, and operational requirements. A software-as-a-service deployment provides a fully managed cloud environment where infrastructure management is handled by the platform. A hybrid deployment model combines platform services with customer-controlled infrastructure to meet specific security and compliance mandates without fragmenting workflows. A private cloud deployment runs the entire platform within customer-controlled environments to support strict data sovereignty, export controls, or air-gapped configurations while keeping data, model weights, and execution under organizational control.

Advanced features support production-scale physics AI operations. Out-of-distribution detection identifies inputs outside validated model envelopes and triggers fallback behavior, escalation protocols, or targeted data collection. Transfer learning adapts models to specific design spaces and operating envelopes with continuous evaluation and monitoring. The system maintains lineage from requirements to datasets, trained models, and deployed endpoints with reproducible runs and immutable records. GPU-native solvers accelerate computational performance while integrating simulation, test, and operational data with the physical context needed to develop reliable models that evolve as organizational assets.