Build vs. Buy: AI Governance Solutions
The build vs buy decision affects your ability to manage AI risks, ensure compliance, and drive innovation. Here's what to consider.
Building In-House: Pros and Cons
✅ Advantages ❌ Disadvantages Complete customization to match specific organizational needs and workflowsSignificant upfront investment in research, development, and testingGreater control over implementation, features, and development roadmapExtended time-to-implementation delaying governance benefitsPotential for deeper integration with existing systems and toolsRequires specialized expertise that may not exist within the organizationIntellectual property ownership of the developed solutionOngoing maintenance burden to keep up with evolving best practices and regulationsNo dependency on external vendors for critical governance functionsRisk of obsolescence as governance standards evolveOpportunity to build organizational knowledge and expertise in AI governancePotential for critical oversights due to limited experience in AI governance
Buying AI Governance Framework: Pros and Cons
✅ Advantages ❌ Disadvantages Immediate implementation of a proven governance frameworkLess customization than a fully bespoke solutionAccess to specialized expertise built into the platformPotential integration challenges with legacy systemsRegular updates to keep pace with evolving regulations and best practicesSubscription costs over the long termLower risk with a solution that has been battle-tested across organizationsDependency on vendor for updates and supportPredictable costs through subscription-based pricingMay include features that aren't relevant to your specific needsDedicated support from AI governance expertsMay require process adjustments to align with the platform's workflowsFocus resources on core business rather than governance infrastructure
Detailed Comparison
Criteria Build Option Buy Option Time to Implementation 6-12+ months depending on complexity and resources 2-4 weeks for standard implementation Upfront Costs High (development team, infrastructure, research) Low (subscription setup and onboarding fees) Ongoing Costs Maintenance, updates, security, infrastructure Predictable subscription fees Customization Unlimited, but requires development resources Configurable within platform capabilities, API access Expertise Required AI governance experts, developers, project managers Platform administrators (training provided) Regulatory Updates Manual tracking and implementation Automatic platform updates Scalability Dependent on initial architecture design Built-in enterprise-grade scalability Risk Management Developing from scratch with potential gaps Comprehensive, built on industry best practices Support & Training Self-developed, internal resources Professional support, documentation, training Integration Capabilities Custom built for existing systems Pre-built connectors, APIs, extensibility Time to Value Extended timeline to realize benefits Immediate governance implementation Future-Proofing Requires continuous investment Continuously updated by specialized team
Sample Cost Matrix
Cost Category Build Option (Estimated) Buy Option (Estimated) Initial Development/Setup $250,000 - $500,000 (design, development, testing) Included in subscription Infrastructure $25,000 - $50,000 (servers, security, databases) Included in subscription Staffing (Year 1) $400,000 - $600,000 (developers, AI experts, project management) Included in subscription Annual Maintenance $150,000 - $250,000 (updates, bug fixes, enhancements) Included in subscription Training $20,000 - $40,000 (materials development, sessions) Included in subscription Integration Costs $50,000 - $100,000 (connecting to existing systems) $5,000 - $25,000 (using pre-built connectors) Annual Subscription N/A $5,000 - $30,000 (based on organization size) Regulatory Updates $50,000 - $100,000 (research, implementation) Included in subscription First Year Total $795,000 - $1,390,000 $10,000 - $55,000 Three-Year Total $1,245,000 - $2,140,000 $30,000 - $165,000
Note: These figures are estimates and may vary based on organizational size, complexity, and specific requirements.
When to Build vs Buy
✅ When Building Makes Sense ✅ When Buying Makes Sense Has highly unique governance requirements not addressed by existing solutions Needs to implement governance quickly to address immediate needs Already possesses significant AI governance and development expertise in-house Wants to leverage proven best practices rather than developing them Has substantial time available before governance capabilities are needed Has limited in-house AI governance or development expertise Requires complete control over every aspect of the governance system Prefers predictable costs through a subscription model Has dedicated resources to maintain and evolve the system long-term Values ongoing updates to keep pace with evolving regulations Has a strategic reason to develop proprietary governance capabilities Wants to focus resources on core business rather than governance infrastructure
Key Questions
Before deciding, consider:
What is your timeline for implementing AI governance?
What level of AI governance expertise exists within your organization?
How unique are your governance requirements compared to standard practices?
What is your budget for initial implementation and ongoing maintenance?
How important is it to have the latest regulatory updates automatically incorporated?
What internal resources can you dedicate to governance long-term?
How critical is integration with your existing systems and workflows?