Should you build custom AI internally or license a managed platform? Get a data-driven BUILD, BUY, or HYBRID recommendation based on your capabilities and use case.
Rate each factor from 0 (none) to 5 (maximum). The tool evaluates two axes: your execution capability and your domain uniqueness.
What You'll Get
Verdict
BUILD, BUY, or HYBRID recommendation with rationale
Radar Scorecard
Visual scores across all 8 dimensions
Position Map
2x2 matrix showing exactly where you fall
Cost Comparison
Build vs. Buy vs. Fractional cost estimates
Axis 1 of 2
Execution Capability
Can your organization actually build and maintain custom AI systems?
🧑💻AI Engineering Headcount
How many dedicated AI/ML engineers does your organization have?
Count engineers whose primary role is building, training, or deploying AI/ML models — not data analysts or general software developers who occasionally touch ML.
None3+ dedicated AI specialists
0None
1Minimal
2Some
3Moderate
4Strong
5Full
⏱️Deployment Timeline
How quickly do you need AI capabilities operational?
Building custom AI typically requires 6–12+ months. If your timeline is weeks, buying is almost always the right first move regardless of other factors.
Immediately (weeks)6+ months runway
0None
1Minimal
2Some
3Moderate
4Strong
5Full
⚙️Infrastructure Maturity
What is your current MLOps and infrastructure state?
Includes CI/CD for models, experiment tracking, model registries, monitoring, GPU/compute provisioning, and automated retraining pipelines.
Basic cloud, no automationMature MLOps with automated pipelines
0None
1Minimal
2Some
3Moderate
4Strong
5Full
🧪Evaluation & Testing
Do you have AI model evaluation and testing frameworks?
Comprehensive eval includes benchmark suites, regression testing, bias/fairness audits, latency profiling, and automated quality gates before deployment.
No evaluation toolingComprehensive eval harness with benchmarks
0None
1Minimal
2Some
3Moderate
4Strong
5Full
Axis 2 of 2
Domain Uniqueness
Does your use case actually require custom AI, or will off-the-shelf work?
🔒Data Propriety
How proprietary is the data your AI system would process?
Highly proprietary data (trade secrets, classified, ITAR) often cannot leave your environment, making SaaS platforms impractical or requiring costly on-prem deployments.
Standard industry dataHighly proprietary / classified
0None
1Minimal
2Some
3Moderate
4Strong
5Full
💡Use Case Uniqueness
How novel is your target AI application?
Generic use cases (chatbots, email drafting, document summarization) are well-served by off-the-shelf platforms. Core-business algorithms that define your product are candidates for custom builds.
Would custom AI provide lasting competitive advantage?
If competitors can buy the same SaaS tool and achieve parity, there is no moat. A moat exists when your AI embeds proprietary data, workflows, or domain knowledge that cannot be replicated.
Commodity capabilityMassive strategic moat
0None
1Minimal
2Some
3Moderate
4Strong
5Full
📜Regulatory Requirements
Do you have specific compliance needs for AI processing?
Includes FedRAMP, HIPAA, SOC2, ITAR, air-gapped environments, data residency requirements, or industry-specific AI regulations that limit vendor options.
Standard commercial requirementsStrict regulatory / air-gapped