This text is a part of VentureBeat’s particular challenge, “The Actual Price of AI: Efficiency, Effectivity and ROI at Scale.” Learn extra from this particular challenge.
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 methods, 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 sorts 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 aspect 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 crucial 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 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 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 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 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, informed 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 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 functions. In alternate, 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 mentioned. “These fashions are more and more accessible by way of 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 nevertheless 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 house, the place fashions are chosen, and even routinely 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 conduct. Proprietary mannequin distributors usually limit 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 methods.
In distinction, open-source fashions enable for focused fine-tuning, guardrail design and optimization for particular use instances. This issues extra in an agentic future, the place fashions are not monolithic general-purpose instruments, however interchangeable parts inside dynamic workflows. The power to finely form mannequin conduct, at low price 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 electronic mail with one AI software, 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 actual query turns into: what mixture of fashions most closely fits your workflow’s particular calls for?” Guarrera mentioned.
Contemplating complete price of possession
With open fashions, the essential thought is that the mannequin is freely out there to be used. Whereas in distinction, enterprises all the time pay for closed fashions.
The fact relating to contemplating complete price of possession (TCO) is extra nuanced.
Praveen Akkiraju, Managing Director at Insight Partners defined to VentureBeat that TCO has many various layers. Just a few 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 also can typically be a really advanced job. 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 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 & Functions 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 usually pertain to mannequin availability when information residency necessities are in place. On this case, deploying an open mannequin on infrastructure like Amazon SageMaker would be the solely option 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 change 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, 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 will be less expensive.
How one enterprise software program developer evaluated open vs closed fashions
Josh Bosquez, CTO at Second Front Systems 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 aims,” Bosquez informed VentureBeat.
Bosquez defined that open fashions enable his agency to combine cutting-edge capabilities with out the time or price of coaching fashions from scratch. For inner experimentation or fast 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 functions or deployments involving delicate or regulated environments,” he mentioned. “These fashions usually come from trusted distributors, who provide 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 in addition 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 method 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 danger and the flexibility 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 method that optimizes for various use instances inside your group.
The quick motion gadgets are simple. First, audit your present AI workloads and map them towards the choice framework outlined by the consultants, 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 straight impacts your true complete price of possession.
Third, start experimenting with mannequin orchestration platforms that may routinely 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, corresponding to EY’s Guarrera, predict, the place mannequin choice turns into invisible to end-users.
