This text is a part of VentureBeat’s particular situation, “The Actual Price of AI: Efficiency, Effectivity and ROI at Scale.” Learn extra from this particular situation.
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 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 kind of alternative between open and closed is now dealing with enterprises for AI, with a number of choices for each forms 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 any 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 information, or mannequin weights open or accessible for anybody to see. The mannequin is just not simply accessible to be fine-tuned and customarily talking, it is just accessible 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 expertise, like Meta Llama, IBM Granite, or DeepSeek, has overtly accessible 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 more likely 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. 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 trade, an enterprise pays a premium for these providers.
“Open-source fashions, then again, can present larger 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 is just not 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 mechanically orchestrated, based mostly 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 packing containers. Whereas API-based instruments simplify integration, they summary away a lot of the management, making it tougher to construct extremely particular or interpretable programs.
In distinction, open-source fashions enable for focused fine-tuning, guardrail design and optimization for particular use circumstances. This issues extra in an agentic future, the place fashions are now not monolithic general-purpose instruments, however interchangeable parts 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 stated.
For instance, a consumer could draft an e-mail with one AI instrument, 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 understanding which mannequin is doing what.
“The true query turns into: what mixture of fashions most closely fits your workflow’s particular calls for?” Guarrera stated.
Contemplating complete value of possession
With open fashions, the essential thought is that the mannequin is freely accessible to be used. Whereas in distinction, enterprises all the time pay for closed fashions.
The truth in the case of contemplating complete value of possession (TCO) is extra nuanced.
Praveen Akkiraju, Managing Director at Perception Companions defined to VentureBeat that TCO has many various layers. Just a few key issues embody 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 also generally 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, except enterprises deploy comparable engineering experience, they’ll 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 Information & Purposes at cloud native providers supplier Caylent instructed VentureBeat that from his perspective, licensing phrases don’t matter, apart from in edge case eventualities. The most important restrictions typically pertain to mannequin availability when information residency necessities are in place. On this case, deploying an open mannequin on infrastructure like Amazon SageMaker stands out as the solely option 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, can have a decrease TCO. Nonetheless, 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 could be more cost effective.
How one enterprise software program developer evaluated open vs closed fashions
Josh Bosquez, CTO at Second Entrance Techniques 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 targets,” Bosquez instructed VentureBeat.
Bosquez defined that open fashions enable 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 shortly and profit from community-driven developments.
“Closed fashions, then again, are our alternative when information sovereignty, enterprise-grade help 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 help, 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 information dealing with insurance policies, integration necessities and long-term scalability.
TCO, he stated that it varies considerably between open and closed fashions and neither strategy is universally cheaper.
“It relies on the deployment scope and organizational maturity,” Bosquez stated. “In the end, 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 selecting sides. It’s about constructing a strategic portfolio strategy that optimizes for various use circumstances inside your group.
The rapid motion objects are simple. First, audit your present AI workloads and map them towards 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 complete value of possession.
Third, start experimenting with mannequin orchestration platforms that may mechanically route duties to probably the most applicable mannequin, whether or not open or closed. This positions your group for the agentic future that trade leaders, corresponding to EY’s Guarrera, predict, the place mannequin choice turns into invisible to end-users.
