US enterprises are shifting artificial intelligence from pilot projects to mission-critical systems. Finance chiefs now demand transparent return on investment, audit committees expect comprehensive risk governance, and regulators look for controls that align with familiar risk-management standards. AI executives must weigh three paths: developing solutions internally, licensing vendor platforms, or blending both approaches.
No single approach fits every situation. Choices should tie each use case to its potential for competitive difference, the level of regulatory scrutiny it will face, and the capacity to build and support the solution over time.
Unlike the European Union’s prescriptive AI Act, US policy remains focused on specific sectors and driven by enforcement actions. Firms often benchmark against:
- NIST AI Risk Management Framework (RMF): de facto federal guidance shaping procurement reviews and vendor assurance programs across agencies, now mirrored in corporate risk protocols.
- NIST AI 600-1 (Generative AI Profile): detailed expectations for hallucination testing, ongoing monitoring, and evidence of model reliability.
- Banking and finance rules: Federal Reserve SR 11-7 on model risk, FDIC/FFIEC joint guidance, and OCC scrutiny of AI models in underwriting and risk assessment.
- Healthcare regulations: HIPAA privacy rules alongside FDA oversight of clinical decision-support algorithms.
- FTC enforcement: risk of citations for deceptive practices if AI outputs lack required transparency or disclosure.
- SEC requirements: public companies must report material AI-related risks, including bias, cybersecurity gaps, and data-usage concerns.
No uniform federal AI statute exists. Yet standard risk-management obligations extend to AI oversight, model governance, and vendor risk. Regulatory teams are focusing on algorithmic bias, data lineage, and cybersecurity integration. Cross-functional risk groups must map AI models to established enterprise risk frameworks to meet audit and board expectations. Boards and regulators will probe audit trails, governance frameworks, and contractual safeguards. Leadership teams must maintain documentation that satisfies formal reviews and board-level inquiries.
A common oversight is ignoring differences in subscription period versus development timeline. Comparing one-year vendor contracts to three-year build investments will distort the financial picture.
When firms weigh build versus buy, they should align decisions with business drivers, data sensitivity, compliance demands, and desired time-to-value. A simplified framework breaks down as follows:
- Build: when AI capabilities deliver unique advantage, process regulated data (PHI, PII, financials), or require deep integration with proprietary systems.
- Buy: when solutions are commoditized, rapid deployment is critical, or vendors already provide compliance artifacts and safety controls.
- Hybrid: for most scenarios, deploy proven vendor platforms for core services—multi-model routing, safety layers, and audit trails—then deliver custom last-mile components for prompts, retrieval, orchestration, and domain-specific evaluation.
A quantitative scoring model replaces subjective debates. Each dimension receives a rating of 1–5 and is weighted by strategic priority. Decision guidelines translate results into clear actions:
- Build if the build score exceeds the buy score by at least 20%.
- Buy if the buy score exceeds the build score by at least 20%.
- Hybrid if the score difference falls within a 20% margin.
This method converts opinions into data and generates transparent metrics for board reporting.
Accurate comparisons require matching three-year build expenditures against three-year subscription and usage fees. Many companies mistakenly contrast one-year vendor fees with a three-year internal budget, creating a distorted view of long-term costs.
Key components of a three-year build total cost of ownership include:
- Internal engineering: platform, machine learning, site reliability, and security specialists.
- Cloud compute: GPU and CPU capacity for training, inference, caching, and autoscaling.
- Data pipelines: ETL processes, data labeling, continuous evaluation, and adversarial testing.
- Observability: vector stores, evaluation datasets, monitoring pipelines, and logging.
- Compliance: NIST RMF audit preparation, SOC 2 readiness, HIPAA assessments, and penetration testing.
- Data egress: fees and replication costs across regions.
Key elements of a three-year buy total cost of ownership include:
- Subscription or license fees and seat counts.
- Usage charges: token volumes, API calls, and context lengths.
- Integration and change-management efforts.
- Add-on services: proprietary retrieval enhancements, evaluation tools, and safety layers.
- Vendor compliance fees: SOC 2, HIPAA BAAs, and NIST mapping deliverables.
- Exit costs: data portability, cloud egress charges, and migration expenses.
Vendor lock-in and usage volatility can introduce budget uncertainty and operational limits. Unmanaged token spikes may lead to unplanned overages. Legal and procurement teams must negotiate exit terms, data portability, and egress-fee discounts to avoid surprises.
Best-fit build scenarios often involve:
- Core IP: essential underwriting logic, risk scoring, or financial anomaly detection tied to revenue streams.
- Data sovereignty: requirements that PHI, PII, or trade secrets remain within controlled pipelines.
- Custom integration: deep links with claims processing, trading platforms, or ERP workflows not served by off-the-shelf solutions.
- Continuous compliance: audit teams demand evidence artifacts rather than policy summaries.
- Specialized talent: recruiting and retaining senior LLMOps engineers for in-house operations remains competitive.
- Hidden overhead: investments in red-teaming, observability, and evaluation infrastructure often exceed initial estimates.
Common buy scenarios include:
- Commodity tasks: note-taking assistants, Q&A knowledge hubs, ticket deflection, and code copilots.
- Speed to market: deployments within a single fiscal quarter.
- Vendor compliance: providers aligned to SOC 2, HIPAA BAAs, and NIST RMF mappings.
- API lock-in: some vendors restrict embeddings and retrieval to proprietary interfaces.
- Usage volatility: token-based billing can spike costs without strict rate controls.
- Exit considerations: egress fees and migration expenses can erode ROI if not addressed up front.
In practice, most large US companies adopt a hybrid approach. They license core platforms for governance, audit trails, multi-model routing, role-based access, data loss prevention, and compliance attestations. They reserve in-house teams for last-mile work—custom retrieval layers, tool integrations, evaluation datasets, hallucination testing, and sector-specific guardrails. This combination delivers scale without ceding control of sensitive IP or compromising board-level risk oversight.
If buying, contracts should include:
- ISO/IEC 42001 or SOC 2 certification with mapping to NIST RMF.
- HIPAA Business Associate Agreements, data retention limits, redaction rules, and regional segregation.
- Explicit exit and portability clauses, with negotiated egress-fee relief.
- SLAs covering latency, throughput, US data residency, bias metrics, and safety evaluation deliverables.
If building in-house, teams should implement:
- Governance under NIST AI RMF pillars—govern, map, measure, manage.
- A multi-model orchestration layer to prevent lock-in and robust observability pipelines for cost, trace, and hallucination metrics.
- A dedicated LLMOps unit with embedded evaluation, security, and compliance expertise.
- Cost controls such as request batching, retrieval optimization, and egress-minimization tactics.
Use Case: Automated claim review and benefits explanation
- Strategic impact: efficiency gains versus standard processing baselines.
- Data sensitivity: PHI governed by HIPAA plus potential FDA oversight for clinical decision support.
- Integration: tight coupling with legacy claim management systems.
- Time-to-value: deployment window of six months.
- Internal capability: mature ML pipelines but limited LLMOps experience.
- Recommended path: hybrid—license a US vendor platform with HIPAA BAA and SOC 2 Type II assurance for the base LLM and governance layer. Build custom retrieval modules, medical code adaptation, and domain-specific evaluation datasets in-house. Map oversight to NIST AI RMF and prepare audit-ready board documentation.
A scored, weighted framework produces board-ready evidence for audits and regulators. Most blended estates prevail, with enterprises retaining control of prompts, retrieval logic, and evaluators as critical intellectual property.
Organizations should track evolving AI rules at the state level, such as guidelines from New York’s Department of Financial Services, to keep governance processes current.
Enterprises must align build and buy decisions to NIST AI RMF, SOC 2, ISO/IEC 42001, and sector-specific laws like HIPAA and SR 11-7. A three-year TCO forecast, including cloud egress, plus clear exit rights should be part of every contract.
For US organizations in 2025, the build-versus-buy decision will hinge on strategic alignment, governance rigor, and disciplined execution. AI leaders who apply this structured framework can accelerate deployment timelines and strengthen defenses against regulatory and board-level scrutiny.