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 techniques, 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 dealing with enterprises for AI, with a number of choices for each kinds of fashions. On the proprietary closed-model entrance are among 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 vital 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 grasp 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 truly imply for enterprise customers?
A closed-source proprietary expertise, like OpenAI’s GPT 4o for instance, doesn’t have mannequin code, coaching information, or mannequin weights open or out there for anybody to see. The mannequin will not be 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, typically with out restrictions, together with fine-tuning and customizations.
Rohan Gupta, a principal with Deloitte, advised VentureBeat that the open vs. closed supply debate isn’t distinctive or native to AI, neither is it more likely 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 vital developer help. That features documentation in addition to hands-on recommendation and sometimes delivers tighter integrations with each infrastructure and functions. In trade, an enterprise pays a premium for these providers.
“Open-source fashions, alternatively, can present better management, flexibility and customization choices, and are supported by a vibrant, enthusiastic developer ecosystem,” Gupta mentioned. “These fashions are more and more accessible by way of 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 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, advised VentureBeat. ” Open vs closed is more and more a fluid design area, the place fashions are chosen, and even mechanically 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 usually prohibit fine-tuning, cost premium charges, or conceal the method in black containers. Whereas API-based instruments simplify integration, they summary away a lot of the management, making it tougher to construct extremely particular or interpretable techniques.
In distinction, open-source fashions permit for focused fine-tuning, guardrail design and optimization for particular use instances. This issues extra in an agentic future, the place fashions are now not monolithic general-purpose instruments, however interchangeable parts inside dynamic workflows. The flexibility to finely form mannequin conduct, at low value and with full transparency, turns into a significant aggressive benefit when deploying task-specific brokers or tightly regulated options.
“In apply, we foresee an agentic future the place mannequin choice is abstracted away,” Guarrera mentioned.
For instance, a consumer 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 domestically by means of an on-device LLM, all with out ever understanding which mannequin is doing what.
“The actual query turns into: what mixture of fashions most accurately fits your workflow’s particular calls for?” Guarrera mentioned.
Contemplating whole value of possession
With open fashions, the fundamental concept is that the mannequin is freely out there to be used. Whereas in distinction, enterprises all the time pay for closed fashions.
The truth 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 various layers. A number of key concerns 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 generally be a really complicated job. Closed frontier mannequin firms spend huge engineering effort to make sure efficiency throughout a number of duties. In his view, except enterprises deploy related engineering experience, they are going to face a posh 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 Information & Purposes at cloud native providers supplier Caylent advised VentureBeat that from his perspective, licensing phrases don’t matter, apart from in edge case situations. The biggest restrictions usually pertain to mannequin availability when information 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. 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 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. Nonetheless, for big enterprises, SaaS firms with very excessive demand on their LLMs, however easier use-cases requiring frontier efficiency, or AI-centric product firms, internet hosting distilled open fashions will be cheaper.
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 particular use case, safety necessities and strategic targets,” Bosquez advised 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 fast prototyping, open fashions assist his agency to iterate rapidly and profit from community-driven developments.
“Closed fashions, alternatively, are our alternative when information sovereignty, enterprise-grade help and safety ensures are important, notably for customer-facing functions or deployments involving delicate or regulated environments,” he mentioned. “These fashions usually come from trusted distributors, who supply sturdy 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 information dealing with insurance policies, integration necessities and long-term scalability.
Taking a look at 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. “In the end, 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 instances inside your group.
The fast motion objects are simple. First, audit your present AI workloads and map them towards the choice framework outlined by the consultants, 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 mechanically 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, reminiscent of EY’s Guarrera, predict, the place mannequin choice turns into invisible to end-users.
