Every founder will eventually face the critical decision of whether to build or buy when it comes to their startup’s AI product development.
Jeff Bezos famously illustrated this dilemma when speaking at a Y Combinator Startup School Keynote with an analogy from the turn of the last century.
Some beer factories began producing their own electricity, while others chose to purchase it externally and connect to the grid.
The latter spent more money on electricity but focused their efforts on producing better beer, which ultimately paid off.
This scenario inspired Bezos’s famous quote: “Focus on what makes your beer better.”
This analogy is particularly relevant in the age of AI product development.
Founders eager to make a mark in the rapidly evolving AI landscape may attempt to pioneer every aspect, potentially getting lost in the complexities of AI models.
Alternatively, they might design their solutions using existing market offerings. It is crucial to understand the depth of AI product development and align it with your business strategy.
In this article, I provide a concise guide on the key decision factors you should consider when making the build vs. buy decision.
Contents
AI Models: How Deep and Complex Are They?
Building an AI model ranges from relatively simple to extraordinarily complex—one of today’s most technically challenging endeavors. The effort required can be assessed along two main axes: Model Complexity and Model Resources.
Model Complexity
Model Complexity refers to how intricate the AI task is. Tasks like text and image generation use deep learning techniques with complex mathematical foundations.
As these mathematical abstractions become more sophisticated, they require more precise parameter fine-tuning, which makes development more challenging.
Model Resources
Model Resources exist independently from Model Complexity and refer to the computing power and data needed to train and operate a model.
AI models require extensive training data and processing capacity. Resource-heavy models, particularly transformer models, demand significant computing power not just during training but also in production, creating scalability issues as user numbers grow.
Complexity vs. Resource Matrix
Typically, models with simpler and well-defined mathematical formulations (e.g., regressions, decision trees, clustering) are highly customizable and their resource demands are more manageable.
The primary challenge for these models lies in data quality rather than complexity or resources.
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Define Your AI Product Development Strategy
To align model development with your business goals, start by examining the fundamental principles of your core product. The build vs. buy decision presents an excellent opportunity to reassess your business strategy.
Market Differentiator
Identify your market differentiator through your customers’ eyes—what unique value sets your product apart (your “beer”)? Focus on maximizing this key advantage. Keep in mind that your market differentiator often differs from your core product offering.
Consider a clothing company that emphasizes delivery speed or shopping experience rather than the clothes themselves.
Amazon illustrates this perfectly—while they began by selling books, they distinguished themselves through their superior buying experience, offering unmatched speed and selection. Your market differentiator becomes your identity and deserves top priority, as it should be difficult for competitors to replicate.
Product Vision
Beyond identifying your market differentiator, develop a forward-looking vision for your business in the short to long term.
Anticipate future needs around scalability, feature development, and potential integrations with tools and partners. This clear product vision will help you accurately assess the impact of your development decisions along the way.
Factors to Consider When Buying or Contracting a Third-Party Model
After understanding the implications of building an AI model, a clear product vision will guide you in deciding which parts of your core product can be effectively supplemented with an off-the-shelf model.
Buying a ready-made model allows you to focus on product development, but several factors must be considered in this decision.
Cost and Scalability
AI models typically use usage-based pricing structures, especially for remote operations. While initial costs may seem reasonable, they can grow exponentially as your user base expands—particularly depending on your product configuration.
This isn’t inherently negative, but it demands careful planning to maintain a sustainable business model.
Customization
When evaluating third-party tools, customization flexibility is paramount. Your specific use case may require model fine-tuning to achieve desired features.
While fine-tuning an existing model is less intensive than building one from scratch, you’ll need to weigh your customization needs against the third-party model’s inherent limitations.
Control and Maintenance
Third-party tools can significantly reduce your product’s maintenance overhead. However, this advantage comes with dependence on the provider’s update schedule and feature changes.
The initial time savings in development could become problematic if the provider proves unreliable or lacks a transparent release roadmap.
Privacy and Security
Routing your information flow through a third-party provider introduces potential security vulnerabilities. Before integrating any third-party models, it’s crucial to conduct a comprehensive security assessment to safeguard your data and users.
Market Examples of Build vs. Buy in AI Product Development
Let’s explore real-world examples that illustrate these concepts. Both startups and enterprises regularly face this build vs. buy decision in their AI product development journey.
Companies Buying Models
- Netflix utilizes AWS’s recommendation engine to personalize content suggestions.
- Airbnb optimizes pricing and improves search results with AWS machine learning services.
- Lyft employs AWS’s AI for route optimization and demand forecasting.
- Snapchat leverages Google Cloud’s AI and machine learning capabilities for augmented reality filters and features.
- Twitter (now X) uses Google Cloud’s AI for content moderation and personalized recommendations.
- Spotify enhances music recommendations and playlist generation with Google Cloud’s AI.
- Microsoft integrates OpenAI’s GPT models into products like GitHub Copilot for code assistance and Bing Chat for enhanced search experiences.
- Duolingo uses OpenAI’s GPT-4 to provide personalized language lessons.
- Shopify enhances its e-commerce platform with AI-powered product descriptions and customer support.
These companies strategically integrate third-party models into their core business operations, outsourcing both AI models and infrastructure.
This approach enables them to concentrate on their key strengths: partnerships, branding, user experience, and network building—creating lasting assets crucial for growth.
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Companies Building Their Models
Conversely, some companies develop their AI models from scratch to maintain a competitive advantage.
This approach is typical for companies like Salesforce, which, despite being primarily a CRM platform, has the scale and resources to build its own AI models independently.
Industries such as banking and retail, with their access to rich user datasets, frequently develop proprietary AI models for predictive analysis, customer support, and experience enhancement.
Energy companies also tend to build their own AI capabilities, driven by the need for highly customized solutions for their complex datasets.
These organizations often operate in heavily regulated industries where strict compliance standards make in-house development more attractive.
Conclusion
In terms of AI product development, the decision to build or buy a model is pivotal, impacting both technical infrastructure and business strategy.
By understanding AI models’ complexity and resource requirements, defining strategic priorities, and carefully evaluating third-party options, you can make informed decisions that support your long-term goals.
Whether you build your own models or opt for ready-made solutions, success lies in aligning your choice with business objectives and ensuring real value delivery.
The decision extends beyond initial costs and technical hurdles—it’s about choosing a solution that will grow and evolve alongside your business.
Thank you for reading!
