What you don’t see could cost you: Why open source matters in enterprise AI

In my previous article, I compared AI inference to the nervous system of an AI project — the critical, often unseen infrastructure that dictates the user experience. Whether it’s a chatbot or a complex application, the principle is the same: if the nervous system falters, everything else does too. 

As we know, however, a nervous system doesn't operate in isolation. It relies on the rest of the body, with countless other systems working in harmony. Enterprise AI is fundamentally the same. Individual models, isolated infrastructure components, fragmented orchestration, or disconnected applications cannot deliver meaningful value on their own. Real impact only comes when these elements connect to form a cohesive, high-performing whole. 

The risks of the black box

There are many paths to AI adoption. Some begin with closed, so-called “black-box” systems: pre-packaged platforms that make it simple to get up and running. They can be a great entry point, but they also come with trade-offs. When you cannot see how a model was trained, it can be difficult to explain its behavior, address bias, or verify accuracy in your context. In fact, a 2025 IBM survey found that 45% of business leaders cite data accuracy or bias as their biggest obstacle to adopting AI. If you cannot see inside the system, you cannot trust the results. Adaptation is often limited to surface-level fine-tuning or prompt tricks, making it difficult to truly shape the model to your business needs.

Even more critically, you rarely control where and how these models run. They’re tied to a single provider’s infrastructure, forcing you to concede digital sovereignty and accept someone else’s roadmap. The trade-offs show up quickly: costs you can’t optimize, “why” questions from end-users you can’t answer, and compliance risks when regulators ask where sensitive data is processed. 

Open source technology offers a different path built on transparency and flexibility. You can look under the hood, adapt models with your own data, and run them in the environment that makes the most sense for your business. Community-driven projects like vLLM and llm-d (for optimizing inference) and InstructLab (for adapting and fine-tuning models) are powerful examples of how open collaboration helps enable choice and control.That kind of control is the difference between steering your AI strategy and realizing too late that someone else has been steering it for you.

Red Hat AI

This is the philosophy behind Red Hat AI: a portfolio of products engineered not just to help you build AI, but to make sure the pieces work together. After all, it's about how we connect these pieces that ultimately defines the agility, trustworthiness, and sovereignty of your IT and AI strategy.

The Red Hat AI portfolio includes:

• Red Hat AI Inference Server: provides consistent, fast, and cost-effective inference. . Its runtime, vLLM, maximizes throughput and minimizes latency. An optimized model repository accelerates model serving, while an LLM compressor helps reduce compute utilization while preserving accuracy.
• Red Hat Enterprise Linux AI: offers a foundation model platform for running LLMs in individual server environments. The solution includes the Red Hat AI Inference Server, which provides an immutable, purpose-built appliance optimized for inference. Packaging the OS and application together, RHEL AI facilitates day-one operations to optimize model inference across the hybrid cloud.
• Red Hat OpenShift AI: provides an AI platform for building, training, tuning, deploying and monitoring AI-enabled applications and predictive and foundation models at scale across hybrid cloud environments. Red Hat OpenShift AI helps accelerate AI innovation, drive operational consistency, and optimize access to resources when implementing trusted AI solutions. 

Building on an open source foundation and backed by enterprise-grade support, Red Hat AI helps your AI components work together seamlessly across datacenter, cloud, and edge environments. This approach allows you to run any model, on any accelerator, on any cloud, without compromising current or future IT decisions.

With Red Hat AI, you can select your preferred cloud, use your desired accelerators, and extend the tools you already have. The platform adapts to your existing environment, all while preserving the flexibility you need for what comes next.

Value across the ecosystem

For partners, Red Hat AI provides the ability to deliver solutions that drop directly into the environments their customers already trust, without costly rework or disruption. The same openness that gives enterprises flexibility also helps partners accelerate adoption and deliver more consistent experiences. Openness creates a genuine win-win: enterprises gain control and agility, while partners expand their opportunities and margins without being forced into a single vendor’s playbook. The result is faster time to value across the entire ecosystem.

DenizBank shows what this looks like in practice. Before adopting OpenShift AI, their data scientists were stuck with a manual environment where setting up dependencies and managing infrastructure was slow and error-prone. By layering OpenShift AI on top of their existing platform, and using GitOps to manage the full AI lifecycle from experiments to production, they cut environment setup time from a week to about ten minutes. Deploying new microservices and models dropped from days to minutes. Over 120 data scientists now work with self-service environments and standardized tools, and are able to more efficiently use GPU resources. This speed, alignment, and scalability is what can happen when enterprise AI stacks are built to work together.

AI as a community effort

This is where the story gets bigger than any one product. AI at scale has never been something one company can do alone. It takes infrastructure, accelerators, orchestration layers, open source projects, enterprise platforms, and, most importantly, the communities that connect them.

Technologies that endure aren’t built in isolation. They’re built in the open, stress-tested by a community, and adapted across use cases no single vendor could anticipate, making it more resilient and usable in the real world. This is how Linux became the backbone of enterprise computing, and it’s how open source will shape the next era of AI.

Every organization’s AI journey looks different. Whether you are experimenting, scaling, or operationalizing, Red Hat AI can help you get there faster. Learn more about the portfolio or reach out to talk about what the right path looks like for you.