The AI Pricing Playbook Series
TL;DR:
There is no single correct answer, but there is such a thing as the wrong mindset.
Do not begin with “Can we build it cheaper?”
Begin with “What do we need to own, and what is strategic enough to rent?”
In Episode 1 we covered the five pillars of what it truly costs to implement AI. Those same pillars show up again here because many of them remain your responsibility whether you build or buy. Data strategy, governance layers, integration with legacy systems, and ongoing risk ownership all stay with you. Buying a tool does not remove accountability. These responsibilities cross governance, data, compliance, and operations, and they remain yours regardless of the sourcing model.
The Fork in the Road
Every enterprise AI strategy eventually arrives at the same moment. The pilot proved value, the business wants more, the CFO wants predictability, and suddenly the steering committee is debating a familiar question:
“Should we build this internally or buy it from a vendor?”
It sounds simple, yet underneath that question sits a full conversation about architecture, governance, talent, and risk. This is no different from other build versus buy decisions you have made around SaaS platforms, IT labor, or infrastructure. Enterprises routinely mix internal teams with external providers. Data centers followed the same trajectory, with most organizations now purchasing capacity as a commodity from cloud vendors.
AI follows the same pattern. Your decision levers include long term capability building, control, regulatory requirements, speed, and a willingness to pay for usage.
The strongest leaders treat this as a portfolio strategy rather than a binary choice.
What “Build” Really Means
When leaders say “Let’s build,” the mental picture is usually a group of data scientists training custom models on proprietary data. That image is partly accurate and partly optimistic.
Building internally includes:
- Recruiting scarce talent such as data engineers, model operators, and compliance specialists
- Maintaining infrastructure including GPUs, pipelines, and observability tools
- Owning all risk from data drift to ethical bias
- Funding model retraining and revalidation over multiple years
The advantages include:
- Full control of intellectual property
- Strong security and data governance
- Deep institutional learning which becomes reusable “AI muscle”
The trade offs include:
- Slow initial speed to market
- Higher upfront and long term costs
- Cultural friction while teams shift into a product operating model
Building is not only an IT decision. It is an organizational commitment.
What “Buy” Really Means
Buying feels fast, and usually is. A pre built solution or model as a service allows teams to show ROI quickly.
Buying includes:
- Accepting the vendor’s roadmap, pricing, and architecture
- Relying on their compliance, uptime, and quality
- Paying ongoing usage based fees that may grow unpredictably
Advantages:
- Rapid time to value
- Predictable short term budgeting
- Access to proven expertise and specialized models
Risks:
- Hidden costs in data integration and customizations
- Dependency on proprietary architectures
- Limited differentiation because your competitors may use the same core
Buying delivers speed but often reduces long term control.
The Middle Ground: Co Development and Hybrid Models
The most successful enterprises do not choose one path exclusively.
They combine both approaches. Common patterns include:
- Buy the platform and build the IP by adding proprietary layers internally
- Co develop with strategic partners under shared IP frameworks
- License pre trained models for quick value, then gradually insource the capability for long term control
This blended strategy spreads risk, distributes cost, and increases agility. It also requires a more advanced procurement function that knows how to negotiate data rights, SLAs, and evolving model ownership terms.
How Procurement Should Evaluate Build vs Buy
Procurement’s role is not to pick a winner. The real responsibility is to ensure alignment with strategy, cost transparency, and clarity on governance.
Here is a practical evaluation matrix:
| Criteria | When to Build | When to Buy |
| Strategic Differentiation | AI drives core competitive advantage such as underwriting, pricing, or risk modeling | AI augments non core areas such as HR chatbots or document summarization |
| Data Sensitivity | Regulated or proprietary data must remain internal | Data can be anonymized or externalized safely |
| Speed to Market | Long term roadmap allows internal incubation | Immediate delivery is needed to capture value |
| Talent Readiness | Strong data science and MLOps teams already exist | Skills are scarce or not strategic to develop |
| Cost Horizon | Willing to invest upfront for lower long term cost | Prefer predictable OPEX even if costs rise with scale |
Most enterprises operate across the entire matrix at once.
The Cost of Ownership Equation
The pattern that consistently holds true:
Building costs more early and less later.
Buying costs less early and more later.
Building requires capital and patience but provides control and reuse. Buying delivers speed but often leads to compounding subscription and integration expenses.Mature organizations plan for migration curves. They start externally to prove value and gradually shift ownership inside as capabilities grow
The Governance Dimension
Regardless of your decision, governance cannot be outsourced.
Even if the algorithm comes from a vendor, accountability remains yours. Leaders must ask:
• Who owns the risk of bias?
• Who certifies explainability?
• Who manages data lineage and audit readiness?
A vendor can support you, but regulators and customers will still expect you to demonstrate control. This echoes Episode 1 where governance was highlighted as a cost pillar that cannot be avoided.
The Decision Principle
When facing the build versus buy question, remember:
Do not optimize for cost. Optimize for control and adaptability.
AI is not a single project with a clean ending. It is a system that learns over time and evolves with the business.
The real question is not “What is cheaper?”
The real question is “What do we need to control so we can scale safely and continue learning?”
Remember:Buy for speed. Build for sovereignty. Always budget for both.
