Build vs. Buy Generative AI: Core Differences and Best Approaches

60% of companies prioritize integrating AI solutions as one of the top three categories over the next two years, according to Bain’s executive survey. This number proves the increasing trend of establishing the groundwork for AI-generative solutions to generate more value for businesses.

However, only 35% of businesses visualize clearly the promising outcome received after deploying generative AI solutions. Regarding approaching methods to implement artificial technology in the operation system, enterprises have two options: buying third-party solutions or building in-house applications.

In this article, we’ll outline the differences between build vs. buy generative AI in different attributions across use cases. Approaches to deciding whether to purchase or build a GenAI application for your projects will wrap up this post.

Key Takeaways:

• Businesses choose to buy a Generative AI solution for everyday tasks like programming code or writing emails.
• Businesses decide to build a Generative AI solution in-house for new products and services

Build vs. Buy Generative AI: Which Use Cases?

Bain surveyed 200 participants, and the percentage of respondents who chose to buy or build multiple models fluctuated depending on specific use cases. Generally, more than 50% of businesses prioritize do-it-yourself applications over off-the-shelf ones in most cases.

The survey results have experienced a surpassing number of businesses choosing do-it-yourself applications in product-focused cases. Over 60% of enterprises intend to build GenAI tools for new products and services, customer onboarding, non-software R&D, core product performance enhancements, natural language interfaces, operations, customer service, finance, and IT.

Regarding decisions for off-the-shelf applications, over 40% of businesses choose this approach for knowledge worker effectiveness, legal, sale, and marketing.

Advantages and Disadvantages of Building a Generative AI Solution

Building a generative AI solution offers businesses competitive advantages and a high concentration on core business needs. However, this approach requires much initial investment, intensive resources, and ongoing maintenance.

Pros of Building Generative AI

• Competitive advantages: Building in-house custom solutions differentiates businesses from competitors with a competitive differentiation to dominate the market. For instance, Bloomberg asserted its position in data analysis sectors by creating a 50-billion-parameter system called BloombergGPT. 
• Enhanced scaling capabilities: The decision to build its own generative model addresses the challenge of scaling capabilities to handle large amounts of data collection and more complex operational tasks. Moreover, businesses possess more control in tailoring AI features to meet their specific needs. For example, Alibaba integrated Alibaba Cloud services into its large language model, Tongyi Qianwen 2.0, to process complex tasks. They include reasoning, copywriting, memorizing, and understanding complex instructions. Developing Tongyi Qianwen supports this giant eCommerce business’s diverse ecosystem across various sectors’s needs.
• Diverse customization options: Building in-house applications provides flexibility to develop customized features based on businesses’ objectives. Rufus, an Amazon-developed GenAI-powered assistant, has improved customers’ online shopping experience by generating personalized product recommendations and specific services.     
• Ensuring data security and compliance: Besides offering a tailored application, developing in-house solutions helps businesses ensure regulatory compliance when protecting data privacy. Specific industries, such as FinTech and HealthTech, prioritize building GenAI solutions to enhance data management systems without sharing personal data with external APIs or closed models. Besides, do-it-yourself applications enable robust security measures and integral approaches to protect sensitive data.

Cons of Building Generative AI

Building generative AI applications is a significant investment, requiring substantial upfront costs and ongoing expenses. These costs include research and development, acquiring trusted datasets for training and testing the models, and the necessary hardware infrastructure to effectively train GenAI models. Additionally, organizations must pay to hire experts specialized in GenAI technology and implement robust security measures to enhance the protection of sensitive data. Furthermore, there are long-term maintenance costs associated with bug fixes, new feature development, and continuous updates, all of which contribute to the overall financial commitment for successful generative AI deployment.

Advantages and Disadvantages of Buying a Generative AI Solution

Buying generative AI solutions helps businesses mitigate complex development processes while optimizing costs and approaching high-quality AI capabilities.

Pros of Buying Generative AI

Buying existing foundational models allows businesses to incorporate an advanced AI landscape without efforts to recruit specialized talents and build from scratch. Additionally, this approach delivers faster deployment time while being updated with the latest AI development.

For instance, Panasonic’s B2B Connect purchased the Azure OpenAI Service to increase employee productivity with a powerful AI assistant. This enterprise solution gives more time to increase employee productivity while skipping the complex LLM creation process.

Businesses integrating third-party AI solutions can receive ongoing support, including training to maintain and update the AI system. The ease of implementation enables enterprises to integrate genAI solutions seamlessly into the existing systems with minimal disruption.

Cons of Buying Generative AI 

• Limited customization: Despite being more cost-effective than building Generative AI, buying off-the-shelf models is less flexible in customizing features tailored to businesses’ specific requirements.
• Data privacy: Sharing sensitive data with third-party services raises concerns about privacy and security. Besides, it’s challenging to monitor compliance with data protection regulations for GenAI applications. 
• Dependence on vendors: Companies will find it difficult to switch to other solutions while being dependent on a single vendor.
• Performance limitations: Off-the-shelf applications do not guarantee well-performed scalability and optimization for businesses’ specific use cases.

Considerations When Evaluating the Build and Buy Generative AI

Besides understanding the differences between the two approaches, the following considerations support businesses whether in building or buying GenAI applications:

Cost optimization: Calculating costs of investing in hardware infrastructure and ongoing maintenance enables businesses to allocate budget and optimize ROI performance. Implementing large language models in real-world applications does not always ensure efficiency due to the unmatched number between the cost structure and the production requirements. Consequently, businesses should map out resource selection correctly to manage costs effectively.       

Expertise required: Recruiting and retaining talented AI engineers, domain experts, data scientists, and product teams requires various costs and efforts. Now, buying GenAI solutions is perfect for businesses to reduce human resource costs while approaching top-notch experts in the industry.     

Centralized governance: Enterprises should control the data used for GenAI solutions under consistent rules and policies to ensure data privacy and security. The centralized governance approach covers the following points: 

Data risks: Using hosted or closed-source APIs can result in a high risk of data exposure. 

Access control: Control authority of involved parties across applications regarding accessing models, data prompts, and completions. 

Governance and guardrails: Businesses should establish necessary guardrails to manage security and compliance risks while implementing GenAI tools. 

Audit trails: Companies need to consider transparency and accountability whether buying or building Generative AI by auditing trails. 

Tailored to specific use cases:

• Businesses can decide to buy or build GenAI solutions based on their use cases. Each use case has different requirements in application scope, model customization, data sensitivity, risk tolerance, and scalability. 
• Businesses should choose to buy Generative AI models when the models use generic data without any further data projects expected in the short term and require no internal data IT team and hardware stack. Additionally, this approach is suitable for companies that intend to scale up data competency in the long term across use cases and teams. 
• Businesses should choose to build Generative AI models when the AI models are specific to the organization’s use cases, which require specialized skills to maintain and evolve data infrastructure. This method is also ideal when developing a solution that focuses on a limited team in the short term.

Innovation and future-proofing:

• Businesses must update with future innovations while implementing Generative AI applications. Essential knowledge to prepare for changes in Generative AI models includes:
• Strategies to adapt to constant changes in hardware, models, and frameworks
• Deployment of multi-component AI systems
• Managing long-running fine-tuning tasks while efficiently caching large models and Docker images
• GPU infrastructure distribution for hosting and model training

Vendor lock-in and dependency: Being tied to a single vendor causes businesses to struggle with the rapid changes in AI technology. To mitigate vendor lock-in risks and stay updated, businesses should stay flexible between building and buying GenAI tools.

Time-to-market needs: Building a Generative AI model meets limitations in adapting current business needs when plenty of time is required to reach the final phase. Indeed, companies must overcome a long process, including researching, collecting data, testing, integrating, and deploying the solution to finalize the entire platform. Meanwhile, the buying decision offers faster time to market, securing competitive advantages and optimized costs.      

Cultural fit and change management: Approaching GenAI tools should consider the current vendors’ contracts and seamless integration with the existing landscape to ensure effective performance.  

Scalability and global reach: If the goal is to target the international market, businesses should consider regional context requirements to choose appropriate GenAI applications with tailored features.

Strategies to Combine Build vs. Buy Generative AI: Hybrid Approaches

Businesses can combine building and buying Generative AI solutions depending on the complexity of the use case. Indeed, enterprises can apply the hybrid approach to customize AI models after buying with retrieval augmented generation (RAG) or fine-tuning. 

RAG enables businesses to modify pre-trained foundation models by changing the types of responses to various queries. Meanwhile, fine-tuning operates by adding data about businesses’ products and services to the pre-trained model to control its behavior.

Consequently, companies can leverage off-the-shelf models to serve their business needs by adjusting the output of the large language models through RAG and fine-tuning. RAG and fine-tuning processes require significant technology expertise despite taking less time and effort than building a model from scratch.      

Choose The Right Strategies to Implement Generative AI with Neurond AI Consulting Service

Building or buying generative AI decisions requires companies to partner with a trustworthy and quality service.

Neurond’s Generative AI consulting services provide the right strategies for businesses of different sizes based on specific use cases, budgets, and scaling objectives. Working with talented experts with experienced portfolio allows businesses to optimize performance, allocate costs, and stay updated with AI technologies.