Buy Vs. Build has been an age-old question for many firms looking to adopt new software to enhance their business offering, however when looking at Natural Language Processing (NLP) we have broken down some key considerations.  

With reports suggesting the NLP market to reach USD 44.96 Billion by 2028 it is clear that firms are maturing to its usage and eager to reap the rewards of early adoption. Yet, firms are faced with an extensive list of pros and cons which often makes selecting the right option a challenging and time-consuming process. 

So what are these key considerations firms must take into account? Firstly… 

1. What problem are you looking to address with NLP?  

It is vital to assess what is the core issue your business is trying to address by adopting NLP technology. Will NLP bolster your offering in some way, is it solving a problem which will enhance your value proposition, is it central to your core business?  

In understanding the specific reasoning in implementing an NLP solution the decision to buy or build can be rationalised with greater clarity. Knowing the benefits to be ascertained from the technology is fundamental in the decision-making chain. So once you have established the reasoning and assess the benefits where to next?… 

2. How much budget can you allocate? 

We all know that money doesn’t grow on trees and therefore cost consideration is paramount in the buy vs. build predicament. In terms of NLP, here are just some of the costs to assess.  

NLP Building Costs 

Although it is possible to develop in house a very basic NLP tool, building something that’s actually useful is more difficult. NLP is a complex system with many moving cogs, which when buying from a vendor are taken care of, removing the hassle of assessing and nurturing all elements.  

Building costs stack up quickly especially when developing a more intuitive tool which relies on machine learning and deep learning which, according to Forbes has “street cred and name recognition” however “it’s enormously and increasingly computationally expensive”. NLP engineers and data scientists are needed when opting to build in-house and therefore salaries, company benefits, and miscellaneous costs must be taken into consideration in addition to AWS storage and hardware.  

NLP Buying Costs 

When looking at both options, when it comes to NLP software buying costs licensing fees can vary depending on company size and use case. However, buying an NLP solution comes with many benefits including more analytical capabilities out of the box, the opportunity to tune and configure as you need, the maintenance of the core tech falls to the provider and additional support and services. 

3. How much time can you commit?  

Building in-house you own NLP software solution can be a time-consuming process, taking months if not years to perfect! With employee resources pumped into the development of such a solution, other pressing matters more central to the firm may be put on the backburner or focus split between many projects adding to the timeframe needed for completion.  

Buying on the other hand is a much quicker process with onboarding cut to a matter of days, leaving employees time to focus on other areas of the business.  

4. What is the competitive advantage to be achieved? 

Differentiating your offering in ever-more competitive markets is becoming increasingly difficult. Therefore, assessing the competitive edge to be gained by developing or purchasing an NLP software solution is essential. In capital markets having a tool which understands trading jargon, is enabling traders to never miss a trade opportunity with real-time data processing and analysis. This not only provides competitive edge but also a higher return on investment, something which would take considerable time to achieve should you develop in house.  

Furthermore, often firms are weary regarding the issue of data ownership which can tip the scale in favour of in-house development of an NLP solution. However, with VoxSmart our NLP solution is deployed on prem which ensures your firm has full control over training models and algorithms, keeping trading information private. This enables your firm to maintain their trading position and enhances competitive edge! 


When it comes to NLP, purchasing a solution provides the most ease and security for firms. The risks associated with in-house development are arguable too great unless the appropriate resources are available in abundance – money, labour and time.  

If you liked this, then you may also be interested in: 

New call-to-action