The Economics of AI: Build, Buy, or Partner for Deep Learning Success
Previously, we explored When Deep Learning Makes Sense, and When It Doesn’t, now, we tackle a factor that often determines success: cost clarity. Deep learning can unlock business value, but it’s a serious investment. Knowing the true costs upfront, in time, talent, and infrastructure, helps maximize ROI and avoid surprises.
To succeed, leaders must budget for more than data scientists: think data wrangling, infrastructure, and ongoing operations. In this installment, we’ll break down what it really takes to implement deep learning and how to choose whether to build, buy, or partner.
Costs You Should Expect (and Budget For)
Data Work: The Long Pole in the Tent
Integration, cleaning, labeling, and governance often dominate project timelines and budgets. Plan for this explicitly. It’s not uncommon for data preparation to consume ~80% of a machine learning project’s time. This means you should budget for data engineering effort up front and possibly data labeling services if your use case requires labeled examples. For a typical project, expect that one or more team members (or an external service) will be dedicated to wrangling data before any model gets built. The takeaway: don’t underestimate the time and cost of getting your data house in order — it’s usually the most time-consuming part of the journey.
MLOps from Day One
Treat models like software — invest in tests, pipelines, deployment infrastructure, and monitoring early. This prevents the common scenario of something that works in a lab notebook but fails in production. In practice, you should budget for the tools or platforms to automate training and deployment (for example, using cloud ML services or setting up continuous integration for models). Deep learning also often requires specialized hardware like GPUs or scalable cloud computing, which can be costly, so include cloud computing fees or hardware investments in your cost planning. It’s wise to have at least one engineer with DevOps/MLOps skills in the team to set up things like model serving, automated rollback, and performance tracking. Investing in this infrastructure from the start adds to upfront costs, but it pays off by reducing failures and maintenance headaches down the line.
Program Management Is Strategic
This isn’t a skunkworks project; treat it as a cross-functional program. Coordinate product, data science, IT, compliance, and operations from the outset — that coordination is often the difference between a “cool demo” and real business impact. Plan for a dedicated project/product manager to drive timelines, manage stakeholders, and keep efforts aligned with business goals. Many machine learning initiatives take a few months to go from concept to initial deployment (2–6 months is a typical range for an ML project pilot), so ensure you have leadership buy-in for that duration. A small core team might include, for example, a data scientist or ML engineer, a data engineer, and a product manager or analyst, with part-time input from subject matter experts. Make sure everyone knows their role. Also, budget for ongoing collaboration (regular check-ins, workshops) to keep all departments in sync. All told, even a fairly contained deep learning pilot can run into the low six figures in total cost when you add up data work, development, and infrastructure. Being transparent about these resource needs up front will set realistic expectations with executives and help secure the necessary investment.
Build, Buy, or Partner?
Once you’ve committed to moving forward, the next strategic decision is how to acquire the capability. There are three primary paths — each with distinct trade-offs:
Buy: Accelerate with Off-the-Shelf Solutions
Choose buy when speed and reliability matter more than customization. If your problem is well-understood and commoditized — think OCR, speech-to-text, or generic chatbots — leveraging an existing product or SaaS platform can dramatically shorten time-to-value.
Why it works:
- Proven solutions reduce risk and implementation time.
- Lower upfront development costs compared to building from scratch.
Watch out for:
- Integration complexity and vendor support — these often outweigh the sticker price.
- Recurring license fees and limited flexibility for unique requirements.
Key question: Is this capability a common utility that others have already solved? If yes, buying is often the fastest route to ROI.
Build: Create Differentiation Through Custom Solutions
Opt for build when your proprietary data or processes offer a durable competitive advantage — and you have (or can hire) the talent to operationalize it.
Why it works:
- Full control over design and IP, enabling differentiation competitors can’t easily replicate.
- Tailored fit for unique workflows or domain-specific needs.
Considerations:
- Significant investment in skilled teams, infrastructure, and ongoing maintenance.
- Longer timelines — treat this as a product, not a one-off project.
Litmus test: If this succeeds, are you prepared to fund its upkeep and continuous improvement? If yes, building can yield strategic value beyond cost savings.
Partner: Accelerate and Transform with Expertise
Engage a partner when you need more than code — you need guidance, frameworks, and change management. This is ideal if your organization lacks certain in-house capabilities or wants to jump-start transformation with experienced specialists.
Why it works:
- Access to proven methodologies and accelerators.
- Bridges skill gaps while coaching your team for long-term success.
Trade-offs:
- Higher upfront cost and some dependency on external expertise.
- Choose partners who transfer knowledge, not just deliver a black box.
Key question: Are we tackling something new that requires both technical and organizational change? If yes, partnering can reduce risk and speed up adoption.
The Bottom Line
Understanding the real costs of deep learning — and making smart choices about whether to build, buy, or partner — is essential for turning AI ambition into measurable business impact. Cost clarity isn’t just about avoiding surprises; it’s about setting the stage for sustainable ROI and competitive advantage.
If you’re ready to move from planning to action, explore our guide on Launching AI Right: A 120-Day Plan for Deep Learning Success for a practical roadmap and common pitfalls to avoid. And if you missed it, check out When Deep Learning Makes Sense, and When It Doesn’t for strategic context.
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