Build vs Buy AI.
A decision framework that saves millions.
The wrong platform choice wastes 6-18 months and hundreds of thousands of dollars. We give you a structured framework to get it right the first time.
The build vs buy AI decision depends on 8 key factors: strategic differentiation, data sensitivity, time to value, internal capability, customization depth, total cost of ownership, vendor lock-in risk, and maintenance burden. Most enterprises end up with a hybrid approach: buying commodity AI and building where it creates competitive advantage.
Source: What About AI? Enterprise Advisory
Why this decision matters
The build vs buy decision is one of the highest-stakes choices in your AI strategy. Get it wrong and the consequences compound.
of enterprise AI projects never make it to production
average cost of a failed AI initiative at the enterprise level
average delay when teams choose build and underestimate complexity
The root cause is almost never the technology. It is the decision-making process. Teams choose to build because it feels strategic, or choose to buy because it feels safe — without a rigorous framework for evaluating which path actually fits their situation.
The Decision Framework
Eight criteria that determine whether building or buying is the right choice for each AI use case.
Strategic Differentiation
Does AI give you a competitive advantage?
Favors Build
AI is core to your product or creates a defensible moat. Your competitors cannot buy the same capability off the shelf.
Favors Buy
AI is a supporting function (internal chatbots, document processing). It improves operations but does not differentiate you in the market.
Data Sensitivity
Can your data leave your environment?
Favors Build
Regulated industries (healthcare, finance, defense) or proprietary datasets that cannot be sent to third-party APIs. On-prem or private cloud required.
Favors Buy
Standard business data with no regulatory constraints. Cloud-based SaaS solutions are acceptable under your compliance framework.
Time to Value
How fast do you need results?
Favors Build
You have 6-18 months before competitive pressure requires a solution. Long-term value justifies the upfront investment.
Favors Buy
You need results in weeks, not months. A vendor solution deployed in 30-60 days beats a custom build that ships next year.
Internal Capability
Do you have ML/AI talent on staff?
Favors Build
You have (or can recruit) ML engineers, data scientists, and MLOps capability. You can maintain and iterate on custom models.
Favors Buy
Your engineering team is strong but lacks ML specialization. Hiring AI talent is expensive and competitive. Consider buying now, upskilling later.
Customization Depth
How unique are your workflows?
Favors Build
Your processes are highly specific to your domain. No vendor product maps cleanly to your requirements without heavy modification.
Favors Buy
Your use case is well-understood (customer support, content generation, data extraction). Multiple vendors solve this problem well.
Total Cost of Ownership
What does the 3-year TCO look like?
Favors Build
At scale, custom solutions often cost less per-unit than vendor licensing. The break-even point typically hits at 18-24 months.
Favors Buy
Vendor solutions amortize R&D across thousands of customers. For small-to-mid deployments, buying is almost always cheaper over 3 years.
Vendor Lock-in Risk
How portable does the solution need to be?
Favors Build
You own the code, the models, and the data pipeline. You can switch infrastructure without rewriting your AI layer.
Favors Buy
Evaluate exit clauses carefully. Some vendors make data export trivial; others make it nearly impossible. This is a negotiation point, not a dealbreaker.
Maintenance Burden
Who handles updates, drift, and retraining?
Favors Build
You accept ongoing MLOps responsibility: monitoring for model drift, retraining on new data, and maintaining inference infrastructure.
Favors Buy
The vendor handles model updates, security patches, and performance optimization. Your team focuses on integration, not infrastructure.
The hybrid approach
Most organizations end up with a mix — and that is the right answer. The goal is not "build everything" or "buy everything." It is knowing which is which.
Buy (Commodity AI)
Capabilities where vendor solutions are mature and your requirements are standard.
- Customer support chatbots and ticket routing
- Document processing and data extraction
- Email and content generation tools
- Code review and security scanning
- Meeting transcription and summarization
Build (Differentiating AI)
Capabilities that create competitive advantage and require deep domain knowledge.
- Proprietary prediction models on your data
- Domain-specific recommendation engines
- Custom workflow orchestration with AI agents
- Industry-specific compliance automation
- Product features powered by your unique dataset
The key insight: buy the infrastructure layer (LLMs, vector databases, orchestration platforms) and build the application layer (your specific prompts, workflows, and domain logic). You get speed-to-market from vendors and differentiation from your custom implementation.
Vendor evaluation framework
When buying, use this framework to evaluate any AI vendor — regardless of what category they are in.
Security and Compliance
- SOC 2 Type II certification
- Data residency options (region-specific hosting)
- Encryption at rest and in transit
- Role-based access controls and audit logging
- GDPR / HIPAA / industry-specific compliance
Integration Requirements
- REST / GraphQL API availability
- Webhook support for real-time events
- SSO integration (SAML, OAuth)
- Data import/export in standard formats
- SDK availability for your tech stack
Pricing Model Analysis
- Per-seat vs per-API-call vs enterprise license
- Overage charges and rate limits
- Minimum commitment periods
- Volume discount thresholds
- Total cost at 2x and 5x current usage
Support and SLA Expectations
- Uptime SLA (99.9% minimum for production)
- Response time guarantees by severity
- Dedicated account manager availability
- Implementation support included vs extra
- Training resources and documentation quality
Exit Strategy Requirements
- Data export capabilities and formats
- Model portability (can you take trained models?)
- Contract termination notice period
- Data deletion guarantees post-termination
- Migration assistance availability
Quick decision path
Start here for a rapid directional read. Then use the full framework above to validate.
"Is AI core to your competitive advantage?"
Yes
Lean toward Build. Owning the AI layer creates a moat your competitors cannot replicate by purchasing the same vendor product.
No
Lean toward Buy. Do not invest custom engineering resources in capabilities that are table stakes in your industry.
"Do you have in-house AI/ML talent?"
Yes
Build is viable. You have the team to execute, maintain, and iterate on custom AI solutions.
No
Buy now, upskill your team. Start with vendor solutions while investing in capability building.
Explore our upskilling program"Is your data highly sensitive or regulated?"
Yes
On-prem or private cloud deployment. Build custom or require vendor on-prem options. Data cannot leave your controlled environment.
No
SaaS solutions are viable. Cloud-hosted vendor platforms offer the fastest path to value with minimal infrastructure overhead.
These questions give you a directional signal. For a rigorous analysis tailored to your specific use cases, schedule a consultation.
Frequently Asked Questions
How long does a build vs buy analysis typically take?
What if our answer is different for different use cases?
How do you stay vendor-neutral in your recommendations?
What if we have already started building and realize we should have bought?
How do we handle the build vs buy decision when AI is evolving so fast?
Can you help with vendor negotiations after the analysis?
Stop debating. Start deciding.
In 2-4 weeks, we deliver a Build vs Buy Decision Matrix tailored to your specific AI use cases — with TCO modeling, risk analysis, and a clear recommendation for each initiative.
No vendor bias. No referral fees. Just the right answer for your organization.
The framework pays for itself the first time it prevents a six-figure mistake.
Or email us directly: business@whataboutai.com
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