Page 39 - Red Hat PR REPORT - OCTOBER 2025

Article

For organizations wondering where to begin, open source AI models provide a powerful and
practical foundation. There has been an "explosion of capability" from open-source
technologies in the three two years - models like Llama, Mistral, and Phi-2, Qwen, DeepSeek,
Granite and many others are outperforming proprietary models in enterprise use cases.
Unlike proprietary platforms, open source models offer transparency, flexibility, and a lower
barrier to entry. This ecosystem offers a rich foundation of tools, models, and frameworks
that empower organizations to build AI solutions tailored to their specific needs - without
the constraints of vendor lock-in or proprietary architectures. The flexibility of open source
enables enterprises to experiment, iterate, and scale AI solutions in ways that are cost-
effective,and aligned with internal governance and compliance mandates.

Private AI refers to the development and deployment of AI services within an organization’s
controlled environment — whether on-premises, in a private cloud, or across a hybrid
infrastructure using open source models.

• Small, optimized, purpose-built models that reduce cost and improve
performance.
• Model alignment with enterprise data using techniques like Retrieval-Augmented
Generation (RAG), fine-tuning.
• Enterprise-grade deployment at scale across hybrid cloud environments.

By treating AI as a service just like, compute, storage and other enterprise platforms —
internal teams consume AI securely and efficiently while IT retains full control. Running AI
privately isn’t just about control — it’s a smarter business move:

• Predictable Economics: GPU-as-a-Service models allow teams to share resources,
manage quotas, and prevent idle time, all while avoiding the unpredictability of per-
token cloud pricing.

• Elimination of Shadow IT: By delivering centralized AI services internally, Red Hat
helps IT teams reduce fragmented tooling and unauthorized use of external APIs.
• Faster Innovation, Lower Risk: With Private AI, developers and data scientists can
prototype, fine-tune, and deploy AI applications with enterprise data — while
staying compliant with internal policies and regulatory requirements.
• Unified AI Service Delivery: Through Models-as-a-Service, Red Hat empowers
organizations to distribute AI capabilities to all departments — from customer
support to engineering — without sacrificing oversight or governance.