Why your enterprise AI technique wants each open and closed fashions: The TCO actuality verify

This text is a part of VentureBeat’s particular subject, “The Actual Value of AI: Efficiency, Effectivity and ROI at Scale.” Learn extra from this particular subject.

For the final twenty years, enterprises have had a alternative between open-source and closed proprietary applied sciences.

The unique alternative for enterprises was primarily centered on working programs, with Linux providing an open-source different to Microsoft Home windows. Within the developer realm, open-source languages like Python and JavaScript dominate, as open-source applied sciences, together with Kubernetes, are requirements within the cloud.

The identical sort of alternative between open and closed is now going through enterprises for AI, with a number of choices for each kinds of fashions. On the proprietary closed-model entrance are a few of the greatest, most generally used fashions on the planet, together with these from OpenAI and Anthropic. On the open-source facet are fashions like Meta’s Llama, IBM Granite, Alibaba’s Qwen and DeepSeek.

Understanding when to make use of an open or closed mannequin is a essential alternative for enterprise AI decision-makers in 2025 and past. The selection has each monetary and customization implications for both choices that enterprises want to know and contemplate.

Understanding the distinction between open and closed licenses

There isn’t a scarcity of hyperbole across the decades-old rivalry between open and closed licenses. However what does all of it really imply for enterprise customers?

A closed-source proprietary know-how, like OpenAI’s GPT 4o for instance, doesn’t have mannequin code, coaching knowledge, or mannequin weights open or obtainable for anybody to see. The mannequin will not be simply obtainable to be fine-tuned and usually talking, it’s only obtainable for actual enterprise utilization with a value (positive, ChatGPT has a free tier, however that’s not going to chop it for an actual enterprise workload).

An open know-how, like Meta Llama, IBM Granite, or DeepSeek, has brazenly obtainable code. Enterprises can use the fashions freely, typically with out restrictions, together with fine-tuning and customizations.

Rohan Gupta, a principal with Deloitte, informed VentureBeat that the open vs. closed supply debate isn’t distinctive or native to AI, neither is it prone to be resolved anytime quickly. 

Gupta defined that closed supply suppliers usually supply a number of wrappers round their mannequin that allow ease of use, simplified scaling, extra seamless upgrades and downgrades and a gradual stream of enhancements. Additionally they present important developer help. That features documentation in addition to hands-on recommendation and sometimes delivers tighter integrations with each infrastructure and purposes. In alternate, an enterprise pays a premium for these companies.

 “Open-source fashions, alternatively, can present larger management, flexibility and customization choices, and are supported by a vibrant, enthusiastic developer ecosystem,” Gupta mentioned. “These fashions are more and more accessible through totally managed APIs throughout cloud distributors, broadening their distribution.”

Making the selection between open and closed mannequin for enterprise AI

The query that many enterprise customers would possibly ask is what’s higher: an open or a closed mannequin? The reply nonetheless will not be essentially one or the opposite.

“We don’t view this as a binary alternative,” David Guarrera, Generative AI Chief at EY Americas, informed VentureBeat. ” Open vs closed is more and more a fluid design area, the place fashions are chosen, and even routinely orchestrated, primarily based on tradeoffs between accuracy, latency, value, interpretability and safety at completely different factors in a workflow.” 

Guarrera famous that closed fashions restrict how deeply organizations can optimize or adapt conduct. Proprietary mannequin distributors typically prohibit fine-tuning, cost premium charges, or cover the method in black containers. Whereas API-based instruments simplify integration, they summary away a lot of the management, making it more durable to construct extremely particular or interpretable programs.

In distinction, open-source fashions permit for focused fine-tuning, guardrail design and optimization for particular use circumstances. This issues extra in an agentic future, the place fashions are not monolithic general-purpose instruments, however interchangeable elements inside dynamic workflows. The power to finely form mannequin conduct, at low value and with full transparency, turns into a serious aggressive benefit when deploying task-specific brokers or tightly regulated options.

“In observe, we foresee an agentic future the place mannequin choice is abstracted away,” Guarrera mentioned.

For instance, a consumer could draft an e mail with one AI device, summarize authorized docs with one other, search enterprise paperwork with a fine-tuned open-source mannequin and work together with AI domestically by an on-device LLM, all with out ever realizing which mannequin is doing what. 

“The true query turns into: what mixture of fashions most closely fits your workflow’s particular calls for?” Guarrera mentioned.

Contemplating whole value of possession

With open fashions, the fundamental thought is that the mannequin is freely obtainable to be used. Whereas in distinction, enterprises all the time pay for closed fashions.

The fact in terms of contemplating whole value of possession (TCO) is extra nuanced.

Praveen Akkiraju, Managing Director at Perception Companions defined to VentureBeat that TCO has many alternative layers. Just a few key issues embrace infrastructure internet hosting prices and engineering: Are the open-source fashions self-hosted by the enterprise or the cloud supplier? How a lot engineering, together with fine-tuning, guard railing and safety testing, is required to operationalize the mannequin safely? 

Akkiraju famous that fine-tuning an open weights mannequin can even typically be a really advanced activity. Closed frontier mannequin corporations spend monumental engineering effort to make sure efficiency throughout a number of duties. In his view, until enterprises deploy comparable engineering experience, they may face a fancy balancing act when fine-tuning open supply fashions. This creates value implications when organizations select their mannequin deployment technique. For instance, enterprises can fine-tune a number of mannequin variations for various duties or use one API for a number of duties.

Ryan Gross, Head of Knowledge & Purposes at cloud native companies supplier Caylent informed VentureBeat that from his perspective, licensing phrases don’t matter, apart from in edge case situations. The most important restrictions typically pertain to mannequin availability when knowledge residency necessities are in place. On this case, deploying an open mannequin on infrastructure like Amazon SageMaker often is the solely strategy to get a state-of-the-art mannequin that also complies. With regards to TCO, Gross famous that the tradeoff lies between per-token prices and internet hosting and upkeep prices. 

“There’s a clear break-even level the place the economics swap from closed to open fashions being cheaper,” Gross mentioned. 

In his view, for many organizations, closed fashions, with the internet hosting and scaling solved on the group’s behalf, may have a decrease TCO. Nevertheless, for giant enterprises, SaaS corporations with very excessive demand on their LLMs, however less complicated use-cases requiring frontier efficiency, or AI-centric product corporations, internet hosting distilled open fashions may be less expensive.

How one enterprise software program developer evaluated open vs closed fashions

Josh Bosquez, CTO at Second Entrance Programs is among the many many corporations which have needed to contemplate and consider open vs closed fashions. 

“We use each open and closed AI fashions, relying on the particular use case, safety necessities and strategic goals,” Bosquez informed VentureBeat.

Bosquez defined that open fashions permit his agency to combine cutting-edge capabilities with out the time or value of coaching fashions from scratch. For inside experimentation or speedy prototyping, open fashions assist his agency to iterate rapidly and profit from community-driven developments.

“Closed fashions, alternatively, are our alternative when knowledge sovereignty, enterprise-grade help and safety ensures are important, notably for customer-facing purposes or deployments involving delicate or regulated environments,” he mentioned. “These fashions typically come from trusted distributors, who supply robust efficiency, compliance help, and self-hosting choices.”

Bosquez mentioned that the mannequin choice course of is cross-functional and risk-informed, evaluating not solely technical match but additionally knowledge dealing with insurance policies, integration necessities and long-term scalability.

TCO, he mentioned that it varies considerably between open and closed fashions and neither strategy is universally cheaper. 

“It is determined by the deployment scope and organizational maturity,” Bosquez mentioned. “Finally, we consider TCO not simply on {dollars} spent, however on supply velocity, compliance threat and the power to scale securely.”

What this implies for enterprise AI technique

For sensible tech decision-makers evaluating AI investments in 2025, the open vs. closed debate isn’t about selecting sides. It’s about constructing a strategic portfolio strategy that optimizes for various use circumstances inside your group.

The quick motion gadgets are easy. First, audit your present AI workloads and map them in opposition to the choice framework outlined by the specialists, contemplating accuracy necessities, latency wants, value constraints, safety calls for and compliance obligations for every use case. Second, truthfully assess your group’s engineering capabilities for mannequin fine-tuning, internet hosting and upkeep, as this straight impacts your true whole value of possession.

Third, start experimenting with mannequin orchestration platforms that may routinely route duties to essentially the most acceptable mannequin, whether or not open or closed. This positions your group for the agentic future that trade leaders, equivalent to EY’s Guarrera, predict, the place mannequin choice turns into invisible to end-users.