Build vs Buy: How Generative AI Is Changing the Equation

Your feature backlog isn’t shrinking. Your stakeholders still want everything yesterday. And you’re still stuck answering the same question over and over again: Should we build this or buy it?

It’s a familiar debate — one that used to hinge on straightforward tradeoffs: speed vs control, cost vs customization. But generative AI has scrambled those assumptions. Now, both paths are faster, cheaper, and more flexible than ever. And that’s made the decision harder, not easier.

This isn’t just a tooling question anymore. It’s a strategic one. The real challenge isn’t picking a side — it’s knowing which side to pick when. GenAI hasn’t replaced the build vs buy decision. It’s made it more important than ever to get right — and it’s reshaping the way business leaders think about their software development strategies.

The Old Build vs Buy Playbook Doesn’t Hold Up Anymore

For years, the build vs buy decision followed a simple formula: buying was faster and cheaper; building gave you control and customization. You’d weigh cost, time-to-market, technical fit — and pick whichever side tipped the scale.

That model worked when software solutions were relatively stable and innovation moved at a manageable pace. But generative AI has changed the game entirely.

Tools like GitHub Copilot, Claude, and Vertex AI are collapsing development timelines, lowering skill barriers, and turning six-month builds into two-week proofs of concept. At the same time, SaaS vendors are shipping smarter tools powered by models that learn and improve over time.

For example, JPMorgan Chase reported a 10% to 20% increase in software engineers' efficiency after implementing an AI coding assistant, enabling engineers to focus on high-value projects. 

“AI has changed the parameters about how and why ‘buy v build decisions’ are made, not just to help make the traditional build or buy binary decisions,” says David Radin, creator of Time Management in the Age of A.I. and CEO of Confirmed.

The question isn’t just build or buy software anymore — it’s which parts of your stack can be accelerated by AI, and where do you need to stay in control?

Why Buying Software Still Works — and When It Doesn’t

If your goal is speed, predictability, or ease of implementation, buying existing solutions still makes a lot of sense. AI-powered SaaS products have become more affordable, more flexible, and far more intelligent. Pre-trained models and plug-and-play APIs give you instant access to cutting-edge AI solutions — without the overhead of hiring an AI team.

“AI-powered SaaS tools make buying more attractive by offering accessibility and scalability, allowing businesses of all sizes to leverage advanced AI capabilities without significant upfront investments,” says Nhi Nguyen, co-owner at Agilify, a technology consulting firm that puts agility at the center. “These tools automate tasks, reduce errors, and enhance data analysis — which leads to improved productivity and cost savings.”

But there’s a downside: SaaS sprawl. Many organizations are drowning in subscriptions they barely manage. Carhartt CIO Katrina Agusti shared that her company now juggles 121 SaaS products — more than double what they used five years ago.

“SaaS sprawl is a growing challenge that leads to increased costs, inefficiencies, and security risks—especially when it comes to AI adoption,” says Mike Zack, Chief Operating Officer of Acterys, a cloud-based platform to help finance and operations teams streamline data management, planning, and forecasting. “The biggest areas of waste include disconnected data silos, redundant BI & planning tools, and increased exposure to data breaches, compliance issues, and governance failures.”

The appeal of buying off the shelf is real. But the complexity it introduces is just as real — and needs to be factored into your build vs buy software analysis.

Why Building Is More Viable Than Ever

Building used to be the slower, more expensive option — especially if it involved AI or complex custom features. But GenAI has flipped that assumption on its head.

“As someone who has developed software before the emergence of AI, I can confidently say the usage of ChatGPT and GitHub Copilot within VS Code has drastically reduced development overhead,” says Gadi Kovler, founder and CEO of Radius, an AI-powered platform for generating custom lesson plans. “We need fewer developer positions, we need less domain-specific expertise, and debugging and refactoring is now instant.”

This increase in development speed and accessibility is reshaping how teams think about customization. “Buying software can offer immediate solutions and faster implementation,” says Andriy Senyk, Director of Partnerships & Customer Engagement at SPsoft, a custom healthcare software development company that uses AI at its core. “But building a custom solution with AI tools allows for more customization and control.”

And the barriers are lower than ever. “AI tools can help automate coding, testing, and debugging, lowering the barrier to building custom software,” says Fergal Glynn, AI Security Advocate and Chief Marketing Officer at Mindgard, a company focused on secure and trustworthy AI deployment. “Your team can create tailored tools using low-code platforms and AI co-pilots, reducing the development time from a few months to a few weeks.”

With the right focus and internal alignment, building in house is no longer a resource-heavy moonshot. It’s becoming a cost-effective path to differentiation — and for many teams, a faster way to deliver exactly what they need while maintaining full control and reducing technical debt over time.

GenAI Is Making Both Paths More Cost-Effective

AI is transforming the cost dynamics of software — not just for those who build, but also for those who buy.

On the build side, GenAI is dramatically reducing the time and resources needed to develop and deploy software. “Custom software that once required ten developers might now need three,” says Dev Nag, CEO of QueryPal, a customer support automation company. “With AI handling boilerplate code, integration layers, and even basic architecture decisions that previously consumed months of effort.”

At the same time, vendors are passing along efficiencies from large-scale AI development. “AI-powered software is getting cheaper to buy because vendors spread out their development costs across many customers,” says Adhiran Thirmal, Solutions Engineer at Cycode, an application security posture management (ASPM) platform. “But AI is also making in-house development more affordable by speeding up coding, testing, and automation.”

That said, cost savings don’t come without tradeoffs. “AI isn’t just about coding a model — it’s about data infrastructure, model training, continuous retraining, and compliance,” says Kristine Fossbakk, Data Services Director at Sharecat Data Services, which specializes in complex information management and digital transformation for industrial companies. 

In other words, GenAI can make both building and buying software more cost effective — but only if you consider the full picture. The most successful teams invest not just in the tools, but in the planning, architecture, and governance required to make them sustainable over the long run.

A New Set of Tradeoffs: How AI Has Rewritten the Rules

These shifting dynamics are why the build vs buy AI conversation now deserves its own playbook — one that accounts for rapid innovation, integration complexity, and the evolving role of software teams. Understanding the build vs buy GenAI pros and cons is critical for making decisions that balance speed, customization, and long-term sustainability.

Why Smart Teams Blend Build and Buy

Most teams don’t pick one path. They mix both.

“Buying is obviously faster — buy anything (if the budget allows it) supporting the development of your core competency, as long as it doesn’t clash with in-house needs and customization requirements,” Kovler says. 

That’s exactly what his company does. “We buy AI-powered services (GPT, Copilot, DALL-E, Sora), which has allowed us to bootstrap not just software development, but also marketing, finances, legal and more! We build custom logic around AI outputs, making the AI itself a hidden factor within our system.”

This composable approach gives companies speed where it matters — and control where it counts.

“If a person can describe a process, they can mock it and often execute it,” says Peter Swimm, founder of Toilville, a conversational AI agency. “This is a compelling story, especially for business leaders comfortable dictating and watching a business process come out fully formed on the other side of their organizational structure.”

These shifting dynamics are why the build vs buy AI conversation now deserves its own playbook — one that accounts for rapid innovation, integration complexity, and the evolving role of software teams.

The GenAI Tools Making Building and Buying Easier

Whether you're customizing off-the-shelf software or developing something from scratch, GenAI has expanded what's possible — and who can do it. Here are a few technologies reshaping the landscape:

• Pre-trained AI models and APIs – Solutions like OpenAI’s GPT, Google Vertex AI, and AWS AI services provide ready-to-use AI capabilities, eliminating the need for extensive in-house development.
• AI-powered low-code/no-code platforms – Tools such as Microsoft Power Apps and Bubble lower the barrier to customization, allowing companies to modify software without deep technical expertise.
• Smarter automation and adaptability – AI continuously improves through machine learning, making both custom and off-the-shelf software more efficient, predictive, and responsive to business needs.
  
  

How to Make the Right Call: A Step-by-Step Framework for Your Build vs Buy Analysis

AI hasn’t replaced the classic build vs buy analysis — but it’s fundamentally reshaped how teams approach it. With generative AI accelerating timelines, reducing manual work, and enhancing both off-the-shelf tools and custom development, it’s no longer just about cost or speed. It’s about making smarter, more adaptable choices.

Whether you're evaluating an internal tool, a customer support platform, or a new product feature, here’s how to rethink your decision-making process in this AI-accelerated landscape.

Start with the Problem, Not the Tools

Start by clearly defining the business problem. Are you solving for speed, scale, or standardization — or is this a strategic function that could offer competitive advantage?

If it’s a repeatable, commoditized process, buying a shelf product might be the fastest, smartest path. But if the capability is core to your value proposition, building in house could give you more control and long-term flexibility — especially now that AI can streamline development.

Take Inventory of What You Already Have

Before writing a single line of code or signing a new vendor contract, audit your current software stack. Many modern platforms — especially those enhanced with AI — offer far more configurability than they used to.

“If your existing SaaS tool can meet 80% of your needs and if you can handle long-term maintenance for custom builds, adapting may be more efficient than starting from scratch,” Glynn says.

Be Honest About Your Team’s Capacity

AI can speed up development, but it doesn’t eliminate the need for internal alignment, maintenance, and ownership. Even the best low-code tools still require oversight and clear processes.

Look Beyond the Short-Term Price Tag

AI may reduce the cost of writing code, but software — whether bought or built — comes with hidden complexity. Licensing fees, integration challenges, compliance requirements, and long-term support all factor into the real cost.

Don’t Force a Binary Decision

In many cases, the best answer isn’t build or buy — it’s both. Smart teams often start with a flexible foundation and build the layers that make their product unique.

The bottom line: AI has blurred the line between buying off the shelf and building from scratch. Informed decisions come from knowing when to do each — and having the team, tools, and thinking to do both well.

Keep Moving Forward: Make Smarter Build vs Buy Decisions

Your software strategy shouldn’t be fixed. It should evolve with your business — and with the technology reshaping it. Whether you’re building custom features, buying off-the-shelf tools, or doing both, the right decision at the right time is what keeps you fast, flexible, and focused on what matters most.

Whether you need to hire a software engineer or a dedicated team of specialists, X-Team provides rigorously vetted developers who integrate seamlessly into your workflows. Our developers don’t just write code — they actively contribute to your product goals and help you scale smarter, no matter which path you choose.

With X-Team, you can focus on delivering innovative software solutions while we handle the complexities of global hiring. Our human-first approach streamlines the process, accelerates time-to-hire, and ensures compliance — so you can move quickly without compromise.

Find out how X-Team can meet your on-demand software engineering needs.