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 20 years, enterprises have had a alternative between open-source and closed proprietary applied sciences.
The unique alternative for enterprises was primarily centered on working techniques, with Linux providing an open-source various 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 forms of fashions. On the proprietary closed-model entrance are a number of the largest, 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 important 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 is no such thing as 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 expertise, like OpenAI’s GPT 4o for instance, doesn’t have mannequin code, coaching knowledge, or mannequin weights open or out there for anybody to see. The mannequin just isn’t simply out there to be fine-tuned and customarily talking, it’s only out there for actual enterprise utilization with a price (certain, ChatGPT has a free tier, however that’s not going to chop it for an actual enterprise workload).
An open expertise, like Meta Llama, IBM Granite, or DeepSeek, has brazenly out there code. Enterprises can use the fashions freely, usually with out restrictions, together with fine-tuning and customizations.
Rohan Gupta, a principal with Deloitte, instructed 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 provide a number of wrappers round their mannequin that allow ease of use, simplified scaling, extra seamless upgrades and downgrades and a gentle stream of enhancements. In addition they present vital developer assist. That features documentation in addition to hands-on recommendation and sometimes delivers tighter integrations with each infrastructure and purposes. In change, an enterprise pays a premium for these companies.
“Open-source fashions, then again, can present better management, flexibility and customization choices, and are supported by a vibrant, enthusiastic developer ecosystem,” Gupta stated. “These fashions are more and more accessible through absolutely 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 may ask is what’s higher: an open or a closed mannequin? The reply nonetheless just isn’t essentially one or the opposite.
“We don’t view this as a binary alternative,” David Guarrera, Generative AI Chief at EY Americas, instructed VentureBeat. ” Open vs closed is more and more a fluid design house, the place fashions are chosen, and even robotically orchestrated, based mostly on tradeoffs between accuracy, latency, price, interpretability and safety at completely different factors in a workflow.”
Guarrera famous that closed fashions restrict how deeply organizations can optimize or adapt habits. Proprietary mannequin distributors typically prohibit fine-tuning, cost premium charges, or conceal the method in black bins. Whereas API-based instruments simplify integration, they summary away a lot of the management, making it more durable to construct extremely particular or interpretable techniques.
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 parts inside dynamic workflows. The flexibility to finely form mannequin habits, at low price and with full transparency, turns into a significant 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 stated.
For instance, a person might draft an electronic 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 regionally by an on-device LLM, all with out ever figuring out which mannequin is doing what.
“The true query turns into: what mixture of fashions most accurately fits your workflow’s particular calls for?” Guarrera stated.
Contemplating complete price of possession
With open fashions, the essential concept is that the mannequin is freely out there to be used. Whereas in distinction, enterprises at all times pay for closed fashions.
The fact in relation to contemplating complete price of possession (TCO) is extra nuanced.
Praveen Akkiraju, Managing Director at Perception Companions defined to VentureBeat that TCO has many alternative layers. A number of 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 may typically be a really advanced job. Closed frontier mannequin corporations spend huge engineering effort to make sure efficiency throughout a number of duties. In his view, except enterprises deploy comparable engineering experience, they’ll face a fancy balancing act when fine-tuning open supply fashions. This creates price 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 Information & Purposes at cloud native companies supplier Caylent instructed VentureBeat that from his perspective, licensing phrases don’t matter, apart from in edge case eventualities. The biggest 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 would be the solely solution to get a state-of-the-art mannequin that also complies. Relating 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 stated.
In his view, for many organizations, closed fashions, with the internet hosting and scaling solved on the group’s behalf, could have a decrease TCO. Nevertheless, for giant enterprises, SaaS corporations with very excessive demand on their LLMs, however easier use-cases requiring frontier efficiency, or AI-centric product corporations, internet hosting distilled open fashions may be more cost effective.
How one enterprise software program developer evaluated open vs closed fashions
Josh Bosquez, CTO at Second Entrance Methods is among the many many companies which have needed to contemplate and consider open vs closed fashions.
“We use each open and closed AI fashions, relying on the precise use case, safety necessities and strategic goals,” Bosquez instructed VentureBeat.
Bosquez defined that open fashions permit his agency to combine cutting-edge capabilities with out the time or price of coaching fashions from scratch. For inner experimentation or speedy prototyping, open fashions assist his agency to iterate rapidly and profit from community-driven developments.
“Closed fashions, then again, are our alternative when knowledge sovereignty, enterprise-grade assist and safety ensures are important, significantly for customer-facing purposes or deployments involving delicate or regulated environments,” he stated. “These fashions typically come from trusted distributors, who provide robust efficiency, compliance assist, and self-hosting choices.”
Bosquez stated 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.
Taking a look at TCO, he stated that it varies considerably between open and closed fashions and neither strategy is universally cheaper.
“It is dependent upon the deployment scope and organizational maturity,” Bosquez stated. “Finally, we consider TCO not simply on {dollars} spent, however on supply pace, compliance danger and the power to scale securely.”
What this implies for enterprise AI technique
For good tech decision-makers evaluating AI investments in 2025, the open vs. closed debate isn’t about choosing sides. It’s about constructing a strategic portfolio strategy that optimizes for various use circumstances inside your group.
The fast 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, price 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 instantly impacts your true complete price of possession.
Third, start experimenting with mannequin orchestration platforms that may robotically route duties to probably the most acceptable mannequin, whether or not open or closed. This positions your group for the agentic future that trade leaders, resembling EY’s Guarrera, predict, the place mannequin choice turns into invisible to end-users.
