Framework for Onboarding Open-Source AI Models into Production by Comparative Insights from Four Models

Abstract

The runaway advancement in open-source AI models has enabled organizations to leverage cutting-edge capabilities with less cost and development time. However, deploying such models into enterprise production environments requires thorough evaluation for performance, compliance, and operation reliability. This project examines the real-world deployment of four top open-source AI models—LLaMA 2 (language generation), BLOOM (multilingual NLP), Stable Diffusion (text-to-image generation), and Whisper (speech-to-text transcription). All models were also implemented in a production-ready environment and tested against an extensive evaluation system for functional performance (e.g., accuracy, latency, and resilience), governance requirements (e.g., license adherence and explainability), and operational performance (e.g., integration readiness and security issues). Restrictions that were witnessed encompassed inference speed volatility, consistency behavior in domain-specific data, and the requirement for high compute resources in large models. The comparative study isolated one model as optimum for business-scale implementation, founded on its overall performance, flexible licensing, and system compatibility. This research gives a duplicable method to onboard open-source AI responsibly with real-world recommendations to guide businesses to implement these models securely and according to law.