AI, and generative AI (GenAI) in particular, is reshaping how businesses operate and scale.
AI adoption is expected to climb to 22.7% this year, translating to an additional 267,000 businesses leveraging AI solutions. 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 content generation and 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 case for off-the-shelf
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 might include chatbots, content generation and summarisation, or document classification tools.
These models are pre-trained, tested at scale, and designed for ease of implementation, while most also boast proven performance. 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 more important GenAI becomes to your operations, the more exposed you could be to licensing costs, dependency and widespread security risks.
The case for 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 $1mill – $2mill in the first year, with additional costs for maintenance, storage and updates making the overall cost close to the multi-million mark annually.
While a proprietary GenAI model might be a game-changer, it is inevitably a massive investment, not to mention the longer time to market and the risk of failure.
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.
Concluding thoughts
At AlphaSense, we have spent over a decade building an AI tech stack purpose-built for businesses that is extremely flexible and customisable – once GenAI was introduced to the masses, we could incorporate it easily into our platform, rather than build from scratch. Now, we leverage a dynamic suite of LLMs atop our own proprietary content to ensure the fastest, highest-quality experience across our platform. This gives us the scalability and customisation needed without compromising on trust or transparency.
However, other organisations – especially those building from the ground up amid the AI boom – might choose differently. Making the right choice requires decision makers to closely examine their desired use cases, evaluate the available off-the-shelf options and existing solutions, be strategic about their resources and move forward on the path that best suits their organisation for the long term.
About the Author
Chris Ackerson is SVP of Product at AlphaSense. AlphaSense is the technology behind the business world’s most important decisions – from Wall Street to boardrooms across every major industry. Trusted by over 6,500 leading companies – including 88% of the S&P 100, 80% of top global banks, and all 20 of the world’s largest pharmaceutical firms – our AI search and market intelligence platform empowers business leaders to move faster and with greater confidence by delivering the right insights at the right moment.
