This article is part of the special issue of venturebeat, “The actual cost of AI: on performance, efficiency and ROI scale.” Read more From this special issue.

For the last two decades, enterprises have been an option between open-sources and closed proprietary technologies.

The original option for enterprises was mainly focused on the operating system, with a open-source option for Linux Microsoft Windows. In the developer region, open-source languages ​​such as Python and JavaScript dominate, as open-sources technology, including Kuberanets, are standard in clouds.

The same type of choice between open and closed is now facing enterprises for AI, with several options for both types of models. The ownership is some of the largest, most widely used models on the closed-model front, including people from Openai and Anthropic. There are models like Meta Lama towards Open-Sures, IBM Granite, Alibaba’s Caven And Deepsek,

Understanding when to use an open or closed model, 2025 and beyond enterprise is an important option for AI decision manufacturers. The option has either financial and adaptation implications for options that need to understand and consider enterprises.

Understanding the difference between open and closed license

There is no shortage of hyperbole around the decades -old rivalry between open and closed licenses. But all this is really mean to enterprise users?

For example, like Openai’s GPT 4O, a closed-source is not owned technology, model code, training data, or model weight, which is open or available to anyone. The model is not easily available to be fine and is generally speaking, it is available for real enterprise use only with a cost (of course, chat has a free tier, But it is not cutting it for a real venture charge,

The Meta Lama is openly available in an open technology such as IBM Granite or Dipsek. Enterprises can use models independently, usually without restrictions, including fine-tuning and adaptation.

Rohan Gupta, with a principal DelioetThe venture told that the Open vs. closed source debate is not unique or native to AI, nor is it likely to be resolved soon.

Gupta reported that closed source providers usually provide several wrappers around their model that enabling ease of use, simplified scaling, more spontaneous upgradation and a stable stream of downgrade and enhancement. They also provide important developers support. This includes documentation as well as hand advice and often provides strict integration with both infrastructure and applications. In turn, an enterprise pays a premium for these services.

“Open-source models, on the other hand, can provide more control, flexibility and adaptation options, and are supported by a vibrant, enthusiastic developer ecosystem,” Gupta said. “These models are rapidly accessible through APIs fully managed in cloud vendors, making their distribution wider.”

Creating an option between open and closed models for Enterprise AI

The question that many enterprise users can ask what is better: an open or a closed model? However, the answer is not necessarily one or the other.

“We don’t see it as a binary option,” in David Guarera, Generic AI leader EyesTold the venturebeat. “Open vs. closed is a fluid design space, where the model is selected, or even automatically orchestrated, depending on the trade between accuracy, delay, cost, interpretation and security at various points in a workflow.”

Guarera stated that closed models limit how organizations can customize or adapt to behavior. Own model vendors often restrict fine-tuning, charge premium rates, or hide the process in the black box. While API-based equipment simplifies integration, they are far from control, making it difficult to create highly specific or explanatory systems.

In contrast, open-source models allow for specific use cases targeted for finance, railing design and adaptation. This agent matters more in the future, where models are no longer a monolithic general-purpose device, but are interchangeable components within dynamic workflows. The ability to finely shape the model behavior at low cost and with complete transparency becomes a major competitive advantage when deploying working agents or tightly regulated solutions.

“In practice, we predict an agent future, where model selection is removed,” Gwarra said.

For example, a user can draft an email with an AI tool, briefly present the legal docs with the other, can find enterprise documents with a fine tuned open-source model and locally can interact with AI through an on-device LLM locally, all of them without knowing what the model is doing.

“The real question becomes: Which mixture of the model is in line with the specific demands of your workflow?” Guarera said.

Keeping in mind the total cost of ownership

With open models, the basic idea is that the model is available for use independently. Conversely, enterprises always pay for closed models.

It is more fine when it comes to considering the total cost (TCO) of ownership.

Praveen Akkiraju, Managing Director Insight partner The venturebeat explained that TCO has many different layers. Some major views include infrastructure hosting costs and engineering: Are there an open-source models self-host by enterprise or cloud provider? How many engineering, including fine-tuning, guard railings and security testing to operate the model safely?

Akkiraju said that Fixing an open weight model can also sometimes be a very complex task. Closed frontier model companies spend heavy engineering efforts to ensure performance in many tasks. In his view, until the enterprises deploy uniform engineering expertise, they will face a complex balance act when the fine-tuning open source model. This cost makes the implication when organizations choose their model perfection strategy. For example, enterprises can fix several model versions for various tasks or use an API for several tasks.

Ryan Gross, Head of Data and Applications on Cloud Domestic Services Provider Kalent From the venturebeat that from his perspective, the terms of aging do not matter, except the age case scenarios. The largest restrictions when data residence requirements are often related to the availability of models. In this case, deploying an open model on infrastructure such as Amazon Sayamkar can be the only way to get a state -of -the -art model that still complies. When it comes to TCO, Gross said that the tradeoffs are located between per-token costs and hosting and maintenance costs.

“There is a clear break-Even point where economics switchs to the open model to be cheaper,” Gross said.

In his view, for most organizations, closed models, with hosting and scaling solved by the organization will be a low TCO. However, for large enterprises, mother-in-law companies may be more cost-effective with the hosting of the distilled open model, the distilled open model, or simple uses, or simple uses requiring the frontier performance, or AI-focused product companies.

How an enterprise software developer evaluated open versus models

Josh Buscese, in CTO Second front systems Among the many firms that were to consider and evaluate the open versus closed models.

“We use both open and off AI models, depending on specific use cases, safety requirements and strategic objectives,” Buscese explained venturebeat.

Buscese reported that open models allow their firm to integrate state -of -the -art capabilities without a training model or cost. For internal experiment or rapid prototyping, open models help their firm recur quickly and benefit from community-managed progress.

“Closed models, on the other hand, are our choice when data sovereignty, enterprise-grade support and safety guarantee are necessary, especially for customer-affected applications or deployment associated with sensitive or regulated environment,” he said. “These models often come from reliable vendors, who provide strong performance, compliance support and self-hosting options.”

Buscese said that the model selection process is cross-functional and risk-infusing, not only technical fit but also data handling policies, integration requirements and long-term scalability.

Looking at TCO, he said that it is quite different between open and closed models, nor is the approach universally cheaper.

“It depends on the scope of deployment and organizational maturity,” Bosez said. “Ultimately, we evaluate not only on dollars spent, but also the speed of delivery, compliance risk, and the capacity of the scale safely.”

What does it mean for enterprise AI strategy

For smart tech decision manufacturers evaluating AI investment in 2025, there is no about choosing Open vs. closed arguments. It is about the creation of a strategic portfolio approach that adapt to various use cases within your organization.

Immediate action items are straight. First, audit your current AI workload and map them against the decision framework mentioned by experts, considering accuracy requirements, delaying needs, costs, security demands and compliance obligations for each use case. Second, honestly assess the engineering capabilities of your organization for model fine-tuning, hosting and maintenance, as it directly affects the actual total cost of your ownership.

Third, model start using with orchestration platforms that can automatically root the functions for the most suitable model, whether it is open or closed. It keeps your organization for the agent future that predicts industry leaders, such as EY guare, where model selection becomes invisible to end-users.