Generative AI (GenAI) is reshaping how businesses operate and scale. By the end of 2025, 88% of businesses reported regular AI use in at least one business function. With investments growing exponentially, the next question for enterprises becomes how to adopt and integrate.
With GenAI at the core of many enterprise workflows, from customer service to financial analysis, executives are left with a choice – do they build their own GenAI solutions, or do they buy off-the-shelf models?
With clear advantages and disadvantages to both, the right path depends on budgets, long-term ambitions, and an organisation’s appetite for complexity.
The benefits of off–the-shelf models
For many, the appeal of buying GenAI tools is obvious. Time to market, cost efficiency, and lower complexity are all advantages with vendors now offering highly capable, ready-made models built for common use cases.
These models are pre-trained, tested at scale, and designed for ease of implementation. Buying also offers access to continuous innovation as vendors can push out updates and improvements faster than most in-house teams can manage.
Yet, there are limitations which are becoming apparent as use cases mature. For example, pre-trained models are designed for the average user, not the edge cases or proprietary needs of particular vertical segments or companies in highly regulated environments. If data or workflows deviate from the conditions under which the model was optimised, off-the-shelf tools may not yet be capable of conducting end-to-end operations smoothly.
There is also the issue of data privacy and vendor lock-in. Many GenAI models operate as black boxes, requiring data to be sent off-premises, which introduces concerns around security and compliance.
The upside of building in-house
By contrast, building your own GenAI solution helps guarantee it will have the nuanced, custom functionalities and features your company needs. A custom-built model can be tailored to your data, domain and workflows, integrating with existing systems and giving engineering teams the ability to fine-tune performance and behaviour over time. This can offer a competitive advantage while ensuring the privacy of your data.
But the costs are steep. Building an LLM model in-house ranges from $1-$2ml in the first year, with additional costs for maintenance, storage and updates, making the overall cost close to the multi-million mark annually.
Part of the reason it’s so expensive is the high cost of the necessary hardware and required talent. The high-end GPUs needed to train these models are scarce and expensive, and the AI engineers and researchers capable of delivering production-grade models command premium salaries. According to data from LinkedIn, the average number of days it takes to hire an engineer is 49 – longer than roles in many other professions, including finance, IT, and healthcare.
Long-term business strategy has to centre on AI
As generative AI cements itself within business strategy, the build-versus-buy dilemma becomes less about the technology and more about prioritisation. Neither method is universally correct, but the wrong choice can slow progress. The businesses that will succeed are those that assess their data maturity and risk tolerance alongside clear ROI goals and long-term value.
Chris Ackerson is an SVP of Product at AlphaSense
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