What Build vs Buy Actually Costs in Engineering Time

When sales leaders consider new AI tools, the “build vs. buy” question often comes up. The immediate focus is usually on licensing fees versus initial development costs. However, this overlooks a critical component: engineering time. What build vs buy actually costs in engineering time extends far beyond the initial sprint. It encompasses discovery, development, testing, deployment, and, most significantly, ongoing maintenance and iteration.

The true cost of engineering time in a build scenario includes not just salaries, but also the opportunity cost of diverting skilled resources from core product development or other strategic initiatives. This often makes buying a more attractive option, especially for non-core functionalities.

The Hidden Iceberg of Engineering Costs

Initial development is just the tip of the iceberg. Many stakeholders underestimate the long-term commitment required from engineering teams. A custom-built AI tool isn’t a “set it and forget it” solution.

Consider a simple internal tool, like a Slack assistant for competitive intelligence. On the surface, it seems straightforward. An engineer could probably get a basic version running in a few weeks. However, that basic version lacks robustness, security, and scalability.

“The real cost of a custom build isn’t the first line of code, but every line that follows for maintenance and adaptation.”

Beyond Initial Development

Here’s a breakdown of the engineering time components for a custom build:

• Discovery & Design (Weeks 1-4):
  • Understanding sales team needs.
  • Researching appropriate AI models and APIs.
  • Designing architecture.
  • Planning data pipelines.
• Initial Development (Months 1-3):
  • Coding the core functionality.
  • Integrating with existing systems (your CRM, internal data sources).
  • Setting up infrastructure (servers, databases).
  • Basic testing.
• Deployment & Initial QA (Weeks 2-4 post-dev):
  • Rolling out to a pilot group.
  • Gathering feedback.
  • Bug fixing.
• Ongoing Maintenance (Indefinite):
  • Bug fixes: Inevitable as usage scales and data changes.
  • Security patching: AI models and underlying libraries require constant updates.
  • Infrastructure management: Scaling servers, managing databases, monitoring performance.
  • API changes: External AI models (like large language models) frequently update their APIs, breaking existing integrations.
  • Feature requests: Sales teams will always ask for more.
  • Performance optimization: Ensuring the tool remains fast and reliable.
  • Data hygiene: Maintaining the quality of data feeding the AI model.

For a detailed look at the decision process, see How to decide build vs buy in a week.

Opportunity Cost: The Unseen Drain

Every hour an engineer spends building or maintaining an internal sales AI tool is an hour not spent on your core product or other revenue-generating initiatives. This is the opportunity cost. If your engineering team is already stretched, diverting resources can slow down your main product roadmap.

Imagine your company’s primary product is a SaaS platform. Allocating two senior engineers for six months to build a sales AI tool means six months less development on features that directly impact customer retention or acquisition for your core offering.

Estimating Engineering Time and Cost

Let’s use a placeholder loaded rate for an engineer. A fully loaded cost for a skilled engineer (salary, benefits, overhead) might be $200,000 per year. Assuming 2000 working hours per year, that’s $100 per hour.

This table uses placeholder numbers. Real-world costs can vary significantly based on complexity, team size, and specific technologies. The key takeaway is the substantial ongoing maintenance cost.

The Case for Buying: Focus and Specialization

For many sales AI tools, buying a commercial solution makes more sense. Vendors specialize in these tools. They have dedicated engineering teams focused solely on:

• Reliability and Uptime: Ensuring the tool is always available.
• Security: Implementing robust security measures and compliance.
• Scalability: Handling large volumes of data and users.
• Feature Development: Continuously adding new features and improving existing ones.
• Maintenance: Keeping up with API changes, bug fixes, and performance tuning.

This specialization means you get a more robust, secure, and feature-rich product without the internal engineering overhead. For example, a competitive intelligence bot might seem simple to build, but maintaining its data sources and accuracy is a full-time job for a vendor.

When Building Makes Sense

Building an internal tool is justified in specific scenarios:

1. Unique Competitive Advantage: The tool provides a core, proprietary advantage that cannot be replicated by off-the-shelf solutions.
2. Deep Proprietary Integration: It requires integration with highly specific internal systems or data that no vendor can access.
3. Unmet Niche Need: No commercial solution exists for your exact problem, and the market is too small for a vendor to address it.
4. Excess Engineering Capacity: Your engineering team has spare capacity and specialized skills perfectly suited for the project, and there are no higher-priority product initiatives.

Consider the example of a contract redlining tool. If your contracts are highly standardized and your legal team has very specific, non-negotiable clauses, a custom-built solution might offer more precise control than a generic commercial offering. However, even then, the maintenance burden is significant.

The “Slack Assistant” Fallacy

The idea that a “Slack assistant beats an enterprise platform” often comes from a place of underestimating complexity. A simple Slack bot that answers basic questions might be quick to build. But what happens when:

• It needs to access secure CRM data?
• It needs to integrate with multiple internal knowledge bases?
• It needs to understand complex sales processes?
• It needs to scale to hundreds of users?
• It needs to be maintained when underlying APIs change?

Each of these requirements adds significant engineering time and complexity. What starts as a quick hack quickly becomes a full-fledged software project with all the associated costs.

Evaluating the Long-Term Engineering Commitment

Before deciding to build, sales leaders must engage engineering leadership in a thorough discussion. This discussion should cover:

• Initial Scope: What is the minimum viable product (MVP)?
• Future Roadmap: What features will be requested in 6, 12, 24 months?
• Integration Points: How many systems will it need to connect to?
• Data Security: What are the security requirements for the data it will handle?
• Maintenance Burden: Who will be responsible for ongoing bug fixes, updates, and performance?
• Technical Debt: What shortcuts might be taken in the initial build, and what will be the cost to address them later?

This detailed assessment helps reveal the true engineering cost. It moves the conversation beyond just initial development hours to the sustained commitment required. Often, this deeper dive reveals that buying a specialized solution is the more financially prudent and strategically sound decision, freeing internal engineers to focus on your core business.