Many companies waste millions of dollars and critical time-to-market because they make the wrong decision on a seemingly simple question: should you buy an off-the-shelf AI solution or build your own?

If you're an AI project owner or a decision-maker tasked with implementing AI in your business, you're likely facing this dilemma. The choice between building a custom AI solution and using a pre-built one is crucial.

Drawing from our extensive experience and numerous client interactions, we aim to provide you with insights into this decision-making process, helping you understand the factors involved.

Comparative analysis: buying vs. building

The choice between buying pre-built AI solutions and building custom ones is complex, influenced by factors unique to each business. Pre-built solutions can swiftly manage simple tasks or urgent needs, whereas custom solutions are crucial for core functions requiring scalability, customization, and strategic advantage.

Let's delve into a comparative analysis of both approaches to help you understand their distinct advantages and make an informed decision.

Advantages of buying AI solutions

• Access to advanced solutions: Purchasing off-the-shelf AI allows companies to quickly deploy cutting-edge technology, which is particularly beneficial for businesses without deep AI expertise.
• Rapid implementation: Off-the-shelf solutions can be deployed much faster than building a custom system, which is critical when time-to-market is a primary concern.
• Cost-effectiveness: Initially, buying an AI solution can be more affordable since it avoids the high upfront costs associated with custom development, such as hiring expert staff or extended development timelines.
• Support and maintenance: With purchased AI solutions, ongoing support and maintenance are often included in the service agreement, ensuring the system remains operational and updated without additional internal resources.
• Reduced development risk: Pre-built solutions have been tested and proven, reducing the risk of errors and the trial-and-error associated with custom development.

Advantages of building AI solutions

• Customization and independence: Custom AI solutions offer precise alignment with specific business needs and the flexibility to adapt as those needs evolve, providing a perfect fit for strategic business functions.
• Competitive advantage: A custom solution can serve as a key differentiator by providing unique capabilities not available to competitors, which can be a significant strategic advantage.
• Scalability and long-term cost savings: Although more expensive initially, custom-built solutions can be more cost-efficient at scale. They are also better aligned with the company’s long-term growth, often resulting in lower total cost of ownership.
• Knowledge and capability building: Developing AI solutions in-house can enhance the organization's technical capabilities and knowledge base, which are valuable assets for future projects.
• Data integration and control: Building in-house can prevent data fragmentation and offer better control over data, which is crucial for making informed business decisions.

It’s not a binary decision

Remember, deciding whether to buy or build your AI application isn't a final choice. Various factors might lead you to change your approach later or even consider a hybrid strategy from the start. We'll explore this further in the blog post.

The following sections will delve deeper into the advantages of buying and building AI solutions, providing detailed explanations of the points outlined above. We'll also offer practical considerations to help guide your decision-making process.

Align AI with your business strategy

Before diving into the technical aspects of AI implementation, it's crucial to assess the role AI will play in your organization. Is AI a tool to enhance operational efficiency, or is it a transformative element that sets your company apart? The decision to buy or build should align with your overall strategy.

Consider the following aspects to clarify your objectives:

Core vs. complementary

Is the AI application a core component of your business, or a supplementary tool to enhance existing processes? This distinction influences the type of solution you need and how you should integrate it.

• Core applications: If AI is central to your business model—perhaps as a driver of product offerings or critical operations—building a custom solution may be more advantageous. This approach not only allows for tailored customization and scalability but also provides significant competitive leverage through unique capabilities not available in the market.
• In-house expertise development: By choosing to develop your AI solution internally, you cultivate technical expertise and intellectual property that remains within your company, providing long-term strategic benefits.

Time to market requirements

How urgent is your need to deploy an AI solution? The urgency can guide your decision:

• Immediate needs: If rapid implementation is critical, due to market pressure or the need to quickly improve efficiency, buying a pre-existing platform allows you to get up and running quickly without the complexities of development.
• Leveraging external expertise: Utilizing off-the-shelf solutions can be a smart move to capitalize on the advancements already achieved by others in the AI field, thus saving time and potentially reducing costs associated with lengthy development cycles.

Evaluate your capabilities before building AI

Building AI solutions internally is a strategic commitment that goes beyond technical development. It requires a solid foundation, ongoing investment in specialized talent, and scalable infrastructure. Here’s how you can assess whether building AI is the right path for your organization:

Assess in-house AI expertise

Evaluate the current level of AI expertise within your team. If your team lacks the necessary skills for custom development, purchasing pre-built solutions may be the better choice, as it avoids compromising on the quality or delaying project timelines.

Resource allocation

Consider the financial and human resources you can dedicate to an AI project. Be honest about your ability to attract and retain talent in this competitive landscape, or your capacity to leverage partners to help achieve your goals.

Strategic investment

Embarking on the creation of complex AI systems requires substantial resources and expertise, yet it offers significant rewards. Take a moment to consider whether these rewards justify the effort involved.

Proof of Concept (PoC) as a first step

Developing a PoC can be relatively straightforward these days with Generative AI. However, developing robust AI capabilities and scaling applications to production remains challenging.

Focus on value creation

To derive the most benefit from custom AI, concentrate on developing tools that deliver distinctive and profound value. Aim to create solutions that elevate your offerings and distinguish your company in the marketplace.

Case study: transitioning to a custom build

A leading online service company faced limitations with a third-party dynamic pricing platform. The platform couldn't keep pace with their sophisticated pricing needs and rapidly evolving business model. Seeking a tailored approach, they turned to us for validation and to find the best way to move forward.

Our experts recommended an AI discovery phase that extended beyond validation, involving strategic planning and exploring technological feasibility to improve the company's pricing strategy.

An online service provider was constrained with a third-party dynamic pricing platform that couldn't keep pace with their sophisticated pricing needs and rapidly evolving business model.	We recommended an AI discovery phase to thoroughly check if the company's skills were ready for a shift to a custom, in-house pricing system—a move that would better match their market dynamics.	Opting for a third-party solution initially doesn't eliminate the need for customization later. Transitioning to an in-house, custom built system may become necessary to better align with dynamic market conditions and business models.

Asses your fit for off-the-shelf AI solutions

When considering an off-the-shelf AI solution, it's important to take a strategic approach. The right product should align with your business objectives and operational needs. Here's how to effectively evaluate available AI solutions:

Understand market availability

Start by surveying the landscape of available AI solutions that meet your specific business requirements. Identify products that not only fit your current needs but also have the potential to support your future growth. Consider the following:

• Variety of solutions: Explore different types of AI tools, from data analytics platforms to customer interaction systems, and determine which ones align with your business goals.
• Provider reputation: Assess the market reputation of the vendors. Established vendors typically offer more reliable and continuously updated solutions.
• Establish a benchmarking process: To select the most suitable AI product, establish a rigorous benchmarking process that evaluates performance, scalability, and integration capabilities.

By assessing these factors, you can make an informed decision that minimizes risk and maximizes the benefits to your business.

The cost of failure in critical applications

In mission-critical applications, the stakes are too high to risk failure. AI solutions play a pivotal role in day-to-day operations, making reliability essential.

For instance, one of our clients required highly reliable business intelligence dashboards. After thorough evaluation, they chose Google's Looker due to its proven reliability and accuracy. Looker's robust performance capabilities were vital for their operations.

In such cases, opting for a well-tested and reliable off-the-shelf solution isn't just a convenience, it's essential. The dependability of these tools can significantly impact your operational success and financial health.

When buying AND building makes sense

Deciding whether to buy or build your application doesn’t have to be a binary choice. A hybrid AI approach leverages both off-the-shelf products and custom-built elements to create a tailored system that meets specific business needs.

Implementing a hybrid AI strategy

• Foundation with off-the-shelf products: Start by selecting an AI product that closely matches your business requirements. This product forms the foundation of your AI solution, offering a reliable base from which to build additional, custom features.
• PoC and small-scale deployment: Before fully committing to a custom development path, you can build a PoC or deploy the foundational product on a small scale. This step allows you to test the solution's practical value and make necessary adjustments based on initial results.
• Gradual integration of custom features: As you validate the solution and understand its impacts, you can begin to integrate custom features. This gradual enhancement helps mitigate risks and ensures that the solution evolves in line with your business needs.

This method offers several advantages depending on your specific context:

• Rapid implementation: In industries where time is of the essence, companies can quickly gain AI capabilities through off-the-shelf solutions while custom features are developed in parallel.
• Cost efficiency: For businesses with limited budgets, this approach allows for the integration of cost-effective standard solutions with tailored developments. This not only helps manage initial costs but also ensures that the AI system can scale and evolve as the business grows.
• Risk mitigation and ROI validation: By starting with a proven product and incrementally adding custom elements, businesses can better manage risk and validate the return on investment before further resource commitment.

Strategic considerations for long-term success

Scale smartly

Scaling your business presents significant challenges. Initially, off-the-shelf AI solutions may seem appealing due to their quick deployment capabilities. However, as your business grows, these decisions need to be reassessed to ensure they still align with your evolving needs.

• Data processing needs: For example, if your data processing needs jump from 1,000 images to 100,000 or more, the vendor’s pricing model might become unsustainable. This misalignment can significantly increase operational costs, as vendor prices often don't scale linearly with usage.
• Cost-effective solutions: If the cost-scaling model of an off-the-shelf product diverges from your business growth, developing a custom solution might become more cost-effective over time. Conversely, if the product’s cost structure scales well with your needs, continuing with the purchased solution could be advantageous.
• Proper data engineering: Implementing proper Data Engineering practices is essential as you scale up. Efficient data management and backend architecture can significantly impact costs. For instance, without well-partitioned tables, you might end up querying your entire data warehouse, leading to prohibitively high costs.

The decision to buy or build when scaling hinges mainly on a detailed analysis of cost structures and an alignment of business models.

Keep your tech stack flexible

As your business evolves, so will your technological needs. You may find yourself switching vendors or transitioning from using vendor solutions to building in-house. Anticipating these changes and planning for future migrations is essential in the long run.

• Lean integration: When integrating vendor solutions, many methods will suffice and cover your current needs. However, we recommend you deliberately integrate these solutions as leanly as possible.
• Avoid vendor lock-in: If your system is deeply entangled with a vendor’s proprietary technology, unwinding this can become a significant headache. To avoid such complications, ensure that your system allows for easy disengagement from vendor-specific technologies.

Integrating a vendor's solution? Maintain an abstraction layer to ensure flexibility and ease of transition in the future.

Case study: navigating migration challenges

We've experienced this firsthand with one of our clients when the experiment tracking tool became inadequate as the business scaled.

At the time, there weren’t many available tools for this and the best one available met our needs. So, believing the tool would remain suitable longterm, our internal library became heavily reliant on it.

However, as time passed and our client’s business grew, we found ourselves in the need to migrate to Vertex AI Experiments. But the library was so dependent on the other solution that breaking away from it was a significant challenge.

A legacy system was severely limited by its dependence on a single experiment tracking tool, which could not scale with it's growing needs.	We migrated to a solution capable of handling the increasing scale. Migrating without breaking any of the functionalities in the production code was hard work.	Always maintain a layer of abstraction between your proprietary code and the tools you integrate. Starting with a scalable plan ensures that your technology can grow alongside your business without becoming a bottleneck.

Conclusion

The choice of whether to buy or build your AI solution is more than just a technical decision, it's a strategic one that must align with your overall business's objectives. This decision should be guided by a thorough assessment of your business needs, capabilities, and the specific phase of your project.

You need to weigh in multiple factors: core business requirements, time constraints, in-house expertise, cost considerations, scalability needs, and the desired level of technological independence. Each element plays a crucial role in determining the best path forward for integrating AI into your operations.

At Tryolabs, we offer extensive AI expertise and a client-centered approach to navigate these complex decisions. We are dedicated to aligning AI solutions with your strategic goals to ensure long-term success and a competitive edge in your industry.

Ready to explore the potential of AI for your business? Partner with us to harness the transformative power of AI, tailored to your unique needs and challenges.