Research

June 21 , 2023 by Ryan Ham

To Build or Not To Build?

Every application, SaaS, mobile, e-commerce, media property, website, enterprise data-lakes & beyond- will be powered by Large Language Models (LLMs) within the next 5 years. With the rate of change in today’s digital landscape, it may even be sooner. Generative AI capabilities have become essential for businesses to enhance customer interactions and streamline operations.

Many organizations, big and small, often find themselves at a crossroads, seeking to upgrade their potentially outdated technology into a more advanced generative AI system. Others are venturing into this space for the first time, aiming to implement a solution tailored to their specific needs. In either case, the choice between building and buying holds significant implications for the success of their application and often is one of the key decisions for ultimately shaping their ability to not only survive but also thrive in this competitive market. So, if you know you need an AI strategy, the first choice you’ll have to commit to is deciding if you will build or do you buy. As developing a foundational LLM is generally beyond the scope of most companies who will leverage an LLM-as-a-service provider such as OpenAI, for the sake of this argument, we’ll assume that “building” an LLM starts with an open source model, such as those found on HuggingFace.

How to Think Strategically About GenAI

In the landscape of today’s world, it becomes essential for companies to align with the needs and expectations of their customers and users while positioning defensively against the competition. When choosing between the momentous journey of developing an in-house Large Language Model or purchasing the use of one, there are three crucial factors that demand careful consideration: Manpower. Budget. Time.

Assessing your organization’s available resources and expertise is crucial. Do you have the manpower, and do they have the capability to build a generative AI model from scratch? This requires an immense amount of knowledge about the space and particularly high costs and can take several years, which leads to budget. Have you considered all related factors to building an LLM outside of just purchasing the necessary building blocks, and can you spare the time it takes without seeing any potential return on investment for the building duration? Building a generative AI platform from scratch can be time-consuming, requiring thorough research, data collection, and model development.

The timeline available to complete the project is equally important. Almost every potential customer with this use case ‘wants to have something in place yesterday.’ So, can you really afford to wait the months (or even longer) to build?

By carefully considering the manpower, budget constraints, and timeline requirements, you can strategically decide whether to build or buy a generative AI platform. Assessing these factors will help you align your approach with your organization’s capabilities, financial situation, and project timelines, ultimately leading to an informed decision that meets your strategic objectives. Like any other critical decision in business and life, building out a list of pros and cons is tried and true.

Pros of Building a Generative AI Platform

Customization and Control: Building a generative AI platform from scratch allows you to have complete control over its features, functionality, and design. This level of customization enables you to tailor the tool specifically to your organization’s unique needs, ensuring it aligns perfectly with your requirements and workflows.

Integration Flexibility: You can integrate it with your existing systems and databases, allowing for seamless collaboration and data utilization. This integration capability enables you to create a highly integrated and personalized user experience that will ensure a happy user experience.

Cons of Building a Generative AI Platform

Technical Expertise: Building a generative AI platform from scratch requires a team with expertise in natural language processing (NLP), machine learning (ML), dialog management, and Large Language Models (LLM). Integrating these components can be complex and challenging, requiring specialized knowledge and building a team of costly data science and ML engineer resources.

Maintenance and Upgrades: Once it is built, ongoing maintenance, training updates, and troubleshooting become your organization’s responsibility. This requires a dedicated technical team to handle the tool’s continuous development, support, and improvement.

High Costs: This regularly comes with significant costs, including hiring developers with specialized skills, acquiring necessary tools and technologies, ongoing maintenance, and resource allocation. Building a Generative AI Tool can be a substantial investment for an organization.

Diversion from Core Product/Service/Offering: This may divert resources away from an organization’s core competencies, potentially impacting the development and support of your primary products or services. Assessing whether building a tool aligns with your organization’s strategic goals is crucial.

Security and Data Privacy: It also requires implementing robust security measures and ensuring compliance with data protection regulations. Failure to address these aspects adequately could expose your organization to security risks and legal liabilities.

Pros of Buying a Generative AI Platform Service

Rapid Deployment and Out-of-Box Functionality: Purchasing a generative AI platform service enables quick implementation, usually via APIs. These ready-made solutions offer a significant portion of the desired functionality right out of the box, accelerating time-to-market and providing immediate value to your organization.

Reduced Development Effort: Buying a Generative AI platform service minimizes the need for extensive custom code and integrating multiple components. These pre-built tools offer intuitive interfaces and pre-configured features to simplify the deployment process and reduce the required development effort.

Support, Maintenance, and Reliability: By opting to buy, you gain access to ongoing support, updates, and improvements from the vendor. This includes technical assistance, bug fixes, and feature enhancements, providing peace of mind and ensuring both the tool’s reliability and longevity.

Data and Privacy Security: Reputable Generative AI platform vendors prioritize data and privacy security. They implement robust measures to protect sensitive information and comply with data protection regulations. Many platforms train their models with the data you input, which could be a major risk if you deal with sensitive data. Finding a solution that does not train on your data should be a must-have on your checklist. You need to choose a reliable vendor with proven security measures to ensure the safety and integrity of your data.

Better Accuracy and Relevancy: Purchasing a Generative AI platform service offers the advantage of pre-trained and fine-tuned models. These models provide higher accuracy and more reliable responses, leveraging the expertise and resources of the service provider to enhance the tool’s performance. There are generative AI offerings that have years of experience and effort put into making the best possible product for their consumers. LLMs are not your business, so leverage LLM experts so you can focus on applying LLMs to benefit your core business.

Faster Response Speeds: A platform can deliver fast response times, enabling efficient user interactions. This speed is vital in providing a seamless and satisfactory user experience. Customers demand speed in every aspect and expect the platform to help enhance customer satisfaction and engagement.

Lesser Need for Fine-Tuning: Fine-tuning Large Language Models for optimal performance is a complex task when building a tool from scratch. However, buying a Generative AI platform alleviates the need for extensive fine-tuning, saving time and effort while still delivering excellent results.

Scalability and Future-Proofing: Buying a Generative AI platform service allows your organization to scale effectively and handle increasing user demands. Most offerings have a pay-as-you-go approach allowing to only pay for what you use. Continuous updates also ensure your architecture remains up-to-date.

Cons of Buying a Generative AI Platform Service

Limited Customization: Pre-built generative AI platforms may have limitations in customizing your needs compared to building from scratch. While you can likely find a platform that caters to most requirements, certain unique or specialized needs may be challenging to fulfill with a pre-built solution.

Dependency on Vendor: It is crucial to select a reliable vendor with a track record of delivering quality and ongoing support because you rely on them far beyond the initial purchase.
You need to ensure the platform remains up-to-date, there are ongoing feature enhancements, and the technology stays aligned with evolving technological trends, which is essential to stay competitive and meet changing business needs. Issues with any platform are bound to arise, so prompt and efficient bug fixes are essential to maintain the platform’s stability and minimize any potential disruptions to your operations. Because there are many new companies entering the market, make sure to do your research and choose a reliable company that meets your needs and with whom you can develop a long-lasting relationship.

When Buying Is Better Than Building

Buying a Generative AI service is often a preferred choice in scenarios where time-to-market, cost-efficiency, or ease of implementation outweigh the benefits associated with building.

Many potential customers find themselves investing a significant amount of time and effort in attempting to build it internally, only to encounter difficulties and fall short of their expectations. As a result, their self-built endeavors prove less effective and more time-consuming than anticipated. In these situations, instead of taking the risk to build a solution to soon spend even more time and resources buying one after a failed experiment, purchasing a ready-made generative AI platform service offers a convenient and practical solution that allows organizations to leverage cutting-edge technology without the setbacks and risks of in-house development. Organizations can then focus on their core competencies while still harnessing the power of advanced generative AI capabilities.

Conclusion: So, What Path Should You Take?

In the build vs buy dilemma, strategic decision-making is essential. While building offers customization and control, it demands massive and generally underestimated expertise, ongoing maintenance, and high operational costs. On the other hand, buying provides rapid deployment, reduced development effort, ongoing support, and scalability. In most cases, buying a Generative AI tool service proves to be the preferred option as companies want to leave the building up to the experts but harness the fruits of their labor.

Next Step: A Safe, Trusted, and Affordable Entrypoint to GenAI

The time has come to make a choice, and it carries significant weight for both your company and customers. To explore the benefits of a prebuilt generative AI tool with little to no risk, try Vectara for free to get a taste of what more you can do by utilizing best-in-class retrieval, grounded generation, and generative summarization as a foundation upon which to build your GenAI program. The decision you make will have profound implications for the success of your team, remaining competitive, and the exhaustion of limited resources like time and money. Choose wisely; choose Vectara.