AI Build vs Buy in Organizations | ROI & Quality Analysis
Averroes
Mar 11, 2025
Build or buy? The wrong AI decision could drain millions before you even see results.
OpenAI lost $540M on development. One manufacturer saved $18M annually. The choice isn’t just about cost—it’s about speed, control, and long-term ROI.
With AI adoption doubling year over year, the pressure is on. Do you invest in custom AI, or leverage proven solutions?
We’ll break it down to expose hidden costs, quality trade-offs, and decision frameworks to help you make the call—without gambling your budget.
Key Notes
Building AI requires significant investment in specialized talent and infrastructure.
Purchased AI solutions deploy in 2-3 weeks versus 3-6 months for custom development.
Quality metrics include technical performance, ethical compliance, and operational efficiency.
Build vs Buy in AI
Artificial Intelligence is redefining how organizations operate across various sectors, driving significant improvements in efficiency and decision-making.
As of 2024, 65% of organizations are regularly using generative AI technology, nearly double the amount from 2023, showcasing the rapid adoption of this transformative technology across nearly every industry.
Organizations looking to integrate AI must choose between two paths:
Build: Develop a custom AI solution in-house, tailored to business-specific needs.
Buy: License or purchase an existing AI platform from a vendor.
This choice influences cost, speed, scalability, and competitive positioning.
Financial Implications of Build vs Buy in AI
Deciding between building an AI solution in-house or purchasing from a vendor involves significant financial and strategic implications.
Here are the key financial factors to consider:
1. Initial Investment and Development
Building AI
Developing a custom solution can lead to substantial initial costs, particularly in talent acquisition and infrastructure setup.
Essential roles—such as AI engineers, machine learning specialists, and data scientists—command competitive salaries. For example, AI engineers may earn around $134,188, while data scientists typically draw around $118,399.
When building a team, organizations must account for these expenses and the potential need for additional personnel to scale.
Trial and Error
Although building your own AI model is enticing, it doesn’t guarantee immediate success; firms frequently experience a trial-and-error phase.
OpenAI, for instance, reported losses of around $540 million in 2022 during its development of advanced AI technologies, emphasizing that these financial commitments can lead to significant setbacks before ROI is realized.
2. Time-to-Market
The speed of deployment is critical. Developing an in-house AI system might take anywhere from 3 to 6 months, leading to delayed revenue and missed opportunities.
In contrast, commercially available solutions can often be operational within 2 to 3 weeks, enabling quicker market entry and revenue capture.
3. Hidden and Ongoing Costs
Ongoing costs do not end once an AI solution is implemented. Maintenance, updates, and compliance-related expenses can accumulate over time.
Built solutions may encounter hidden costs from technical debt, which can hinder financial forecasts if not closely managed.
4. Opportunity Costs
Consider how the allocation of internal resources affects overall productivity.
If teams are diverted to develop custom AI systems, they may not focus on high-priority projects, resulting in reduced ROI across other critical initiatives.
5. Infrastructure Costs
When building an AI solution, organizations must invest in high-performance hardware, including GPUs and other computing resources.
Organizations should also expect to incur additional costs related to software licensing and ongoing maintenance of these systems.
Why Buying AI Often Makes More Sense Than Building It
While custom AI builds offer flexibility, they often introduce long-term complexity that can overwhelm internal teams.
Buying a ready-made solution reduces operational risk and ensures scalability without overextending your resources.
1. Infrastructure & Deployment Management
AI models don’t live in isolation—they require high-performance infrastructure, continuous monitoring, and secure deployment environments. When building in-house, organizations must handle:
Managing cloud or on-premise resources, GPUs, and distributed computing environments
Containerization, API integrations, and production-grade security
Ensuring high uptime, low latency, and scalable architecture
It’s a mistake to think that adopting AI is just about building the model (the data science part). To actually deploy a full AI application, you’ll need frontend (UI), backend, DevOps, and infrastructure teams. These teams must collaborate to ensure the entire system operates smoothly.
Vendor-provided solutions come with all of this out of the box, backed by dedicated DevOps and engineering teams. This eliminates the risk of performance bottlenecks or system outages due to internal resource limitations.
2. Continuous Optimization & Model Monitoring
AI models degrade over time as data drifts or new scenarios emerge. In-house teams are responsible for:
Retraining models with fresh data
Monitoring for concept drift or changing problem definitions
Maintaining robust MLOps pipelines for versioning, testing, and redeployment
With purchased AI solutions, these responsibilities shift to the vendor. Updates, optimizations, and retraining are handled behind the scenes—ensuring your model stays current without straining your team.
3. The Hidden Risk of Key-Person Dependency
Organizations that build AI internally often rely heavily on one or two key data scientists. This creates a single point of failure:
If a lead developer leaves, the model may become undocumented or unsupported
Hiring replacements can take months, stalling progress and delaying ROI
Rebuilding from scratch restarts the entire R&D cycle
Buying eliminates this risk. Vendors maintain continuity with full-stack teams who manage the entire lifecycle of the AI—ensuring support, documentation, and version control are never dependent on a single individual.
Strategic Alignment with Organizational Goals
The decision to build or buy should not solely rely on financial factors; it must also integrate with the organization’s overarching strategic vision.
Here are critical alignment considerations:
Core Competency Focus
If AI significantly contributes to an organization’s competitive advantage, investing in an in-house solution is likely the right choice.
Custom solutions can be tailored to meet the specific demands of the business.
Innovation and Scalability
Evaluate the flexibility needed to adapt to market changes. A homegrown solution might offer better long-term scalability but require ongoing resource commitments to maintain effectiveness.
Data Sensitivity and Compliance Needs
For organizations handling sensitive data, building an in-house solution offers increased control over data privacy and compliance, thus reducing risks associated with third-party vendors.
Evaluating Quality Metrics
The effectiveness of an AI solution hinges on quality, assessed through various performance metrics.
1. Technical Performance Metrics
Accuracy
Latency
Resource Utilization
Measure AI prediction accuracy; tailored models boost performance in critical sectors.
Assess response times for real-time use; custom solutions optimize better than generic ones.
Evaluate hardware efficiency; lower operational costs with optimized AI.
2. Ethical and Compliance Metrics
Bias Detection
Explainability
Ensure fairness, especially in sensitive areas; tailored solutions aid bias mitigation.
Maintain transparency for compliance and stakeholder trust.
3. Operational Metrics
Cold Start Time
MTTR
Fast responses improve user experience; evaluate built vs. purchased solutions.
Quick failure recovery ensures continuity; in-house support is often faster than vendors.
Weighing Your Options: Build vs. Buy AI Solutions
Pros and Cons of Build vs. Buy
Feature
Build
Buy
Customization
✔️
❌
Control Over Capabilities
✔️
❌
Speed of Implementation
❌
✔️
Lower Initial Costs
❌
✔️
Ongoing Licensing Fees
❌
✔️
Building AI Models
Pros:
Customization: In-house development allows for tailored solutions that address your specific workflows and challenges, unlike generic off-the-shelf options.
Control Over Capabilities: Retain complete control over system performance and updates, enabling quick adjustments as business dynamics change.
Potential Long-Term Cost Savings: Although initial costs are higher, eliminating ongoing subscription fees can lead to significant savings over time.
Cons:
High Initial Investment: Substantial upfront costs are necessary for hiring specialized staff, investing in infrastructure, and preparing data.
Longer Development Timelines: Building and deploying an AI system can take considerable time, potentially delaying market responsiveness.
Resource-Intensive: Requires significant human resources and technical expertise, along with ongoing maintenance and support commitments.
Buying AI Models
Pros:
Faster Implementation: Purchased solutions can typically be deployed quickly, often within weeks, allowing for rapid market entry.
Lower Upfront Costs: Acquiring third-party models usually involves lower initial expenditures compared to building from scratch.
Access to Established Solutions and Support: Vendors provide tested solutions and ongoing technical support, simplifying deployment and maintenance.
Cons:
Limited Customization: Off-the-shelf solutions may not fully align with the unique processes of every organization, potentially leading to inefficiencies.
Potential for Vendor Lock-In: Relying on a single vendor can limit flexibility, making it challenging to switch suppliers or adapt to new solutions without incurring additional costs.
Ongoing Licensing Fees: While initial costs might be lower, ongoing subscription fees can accumulate over time, necessitating careful analysis of these long-term financial implications.
Demonstrated ROI: A medical equipment manufacturer that implemented our advanced AI platform saw a reduction in false rejections from 12,000 units per week to just 246 units. This change translated into weekly savings of over $352,000 and nearly $18 million in annual savings per production line.
Stop Wasting Resources On Failed Models
Get 99% accuracy from day one
Frequently Asked Questions
What factors should be prioritized when deciding between building or buying an AI solution?
Organizations should prioritize cost implications, time-to-market, internal capabilities, and the strategic importance of AI to their business. Assessing these factors helps determine which option best aligns with long-term goals.
How can organizations ensure a successful build vs buy analysis?
To ensure a successful analysis, organizations should engage stakeholders across departments, utilize structured decision-making frameworks, and conduct thorough cost-benefit evaluations. Pilot testing can also provide valuable insights before making a final decision.
Are there industries where building AI solutions is more advantageous than buying?
Yes, industries with unique data requirements or regulatory frameworks, such as healthcare and finance, often benefit from custom-built AI solutions. These industries typically require tailored approaches to ensure compliance and provide competitive advantages.
What role does vendor support play in the decision to buy an AI solution?
Vendor support can be critical when buying an AI solution, as it often includes ongoing maintenance, updates, and access to expertise. Organizations should evaluate the level of support offered to ensure it meets their long-term needs and enables smooth integration.
Conclusion
Choosing whether to build or buy an AI solution isn’t just a technical or financial decision. It’s a strategic one with long-term consequences.
Building offers deep customization and full control, but it comes with high upfront costs, slower time-to-market, and ongoing resource demands across infrastructure, maintenance, and talent. It also introduces operational risks, including key-person dependency and the burden of managing continuous updates internally.
Buying, on the other hand, delivers a faster path to deployment, predictable costs, and built-in support from a dedicated vendor team. For most organizations, this means faster ROI, reduced internal strain, and fewer long-term risks.
If your goal is to optimize visual inspection workflows without the complexity of building from scratch, our AI platform delivers high-accuracy results from day one—with minimal setup and zero infrastructure headaches.
Request a free demo and see how quickly you can start saving time and money.
Build or buy? The wrong AI decision could drain millions before you even see results.
OpenAI lost $540M on development. One manufacturer saved $18M annually. The choice isn’t just about cost—it’s about speed, control, and long-term ROI.
With AI adoption doubling year over year, the pressure is on. Do you invest in custom AI, or leverage proven solutions?
We’ll break it down to expose hidden costs, quality trade-offs, and decision frameworks to help you make the call—without gambling your budget.
Key Notes
Build vs Buy in AI
Artificial Intelligence is redefining how organizations operate across various sectors, driving significant improvements in efficiency and decision-making.
As of 2024, 65% of organizations are regularly using generative AI technology, nearly double the amount from 2023, showcasing the rapid adoption of this transformative technology across nearly every industry.
Organizations looking to integrate AI must choose between two paths:
This choice influences cost, speed, scalability, and competitive positioning.
Financial Implications of Build vs Buy in AI
Deciding between building an AI solution in-house or purchasing from a vendor involves significant financial and strategic implications.
Here are the key financial factors to consider:
1. Initial Investment and Development
Building AI
Developing a custom solution can lead to substantial initial costs, particularly in talent acquisition and infrastructure setup.
Essential roles—such as AI engineers, machine learning specialists, and data scientists—command competitive salaries. For example, AI engineers may earn around $134,188, while data scientists typically draw around $118,399.
When building a team, organizations must account for these expenses and the potential need for additional personnel to scale.
Trial and Error
Although building your own AI model is enticing, it doesn’t guarantee immediate success; firms frequently experience a trial-and-error phase.
OpenAI, for instance, reported losses of around $540 million in 2022 during its development of advanced AI technologies, emphasizing that these financial commitments can lead to significant setbacks before ROI is realized.
2. Time-to-Market
The speed of deployment is critical. Developing an in-house AI system might take anywhere from 3 to 6 months, leading to delayed revenue and missed opportunities.
In contrast, commercially available solutions can often be operational within 2 to 3 weeks, enabling quicker market entry and revenue capture.
3. Hidden and Ongoing Costs
Ongoing costs do not end once an AI solution is implemented. Maintenance, updates, and compliance-related expenses can accumulate over time.
Built solutions may encounter hidden costs from technical debt, which can hinder financial forecasts if not closely managed.
4. Opportunity Costs
Consider how the allocation of internal resources affects overall productivity.
If teams are diverted to develop custom AI systems, they may not focus on high-priority projects, resulting in reduced ROI across other critical initiatives.
5. Infrastructure Costs
When building an AI solution, organizations must invest in high-performance hardware, including GPUs and other computing resources.
For instance, training a complex model like GPT-4 reportedly required upwards of $100 million in capital investment.
Organizations should also expect to incur additional costs related to software licensing and ongoing maintenance of these systems.
Why Buying AI Often Makes More Sense Than Building It
While custom AI builds offer flexibility, they often introduce long-term complexity that can overwhelm internal teams.
Buying a ready-made solution reduces operational risk and ensures scalability without overextending your resources.
1. Infrastructure & Deployment Management
AI models don’t live in isolation—they require high-performance infrastructure, continuous monitoring, and secure deployment environments. When building in-house, organizations must handle:
It’s a mistake to think that adopting AI is just about building the model (the data science part). To actually deploy a full AI application, you’ll need frontend (UI), backend, DevOps, and infrastructure teams. These teams must collaborate to ensure the entire system operates smoothly.
Vendor-provided solutions come with all of this out of the box, backed by dedicated DevOps and engineering teams. This eliminates the risk of performance bottlenecks or system outages due to internal resource limitations.
2. Continuous Optimization & Model Monitoring
AI models degrade over time as data drifts or new scenarios emerge. In-house teams are responsible for:
With purchased AI solutions, these responsibilities shift to the vendor. Updates, optimizations, and retraining are handled behind the scenes—ensuring your model stays current without straining your team.
3. The Hidden Risk of Key-Person Dependency
Organizations that build AI internally often rely heavily on one or two key data scientists. This creates a single point of failure:
Buying eliminates this risk. Vendors maintain continuity with full-stack teams who manage the entire lifecycle of the AI—ensuring support, documentation, and version control are never dependent on a single individual.
Strategic Alignment with Organizational Goals
The decision to build or buy should not solely rely on financial factors; it must also integrate with the organization’s overarching strategic vision.
Here are critical alignment considerations:
Core Competency Focus
If AI significantly contributes to an organization’s competitive advantage, investing in an in-house solution is likely the right choice.
Custom solutions can be tailored to meet the specific demands of the business.
Innovation and Scalability
Evaluate the flexibility needed to adapt to market changes. A homegrown solution might offer better long-term scalability but require ongoing resource commitments to maintain effectiveness.
Data Sensitivity and Compliance Needs
For organizations handling sensitive data, building an in-house solution offers increased control over data privacy and compliance, thus reducing risks associated with third-party vendors.
Evaluating Quality Metrics
The effectiveness of an AI solution hinges on quality, assessed through various performance metrics.
1. Technical Performance Metrics
2. Ethical and Compliance Metrics
3. Operational Metrics
Weighing Your Options: Build vs. Buy AI Solutions
Pros and Cons of Build vs. Buy
Building AI Models
Pros:
Cons:
Buying AI Models
Pros:
Cons:
Stop Wasting Resources On Failed Models
Get 99% accuracy from day one
Frequently Asked Questions
What factors should be prioritized when deciding between building or buying an AI solution?
Organizations should prioritize cost implications, time-to-market, internal capabilities, and the strategic importance of AI to their business. Assessing these factors helps determine which option best aligns with long-term goals.
How can organizations ensure a successful build vs buy analysis?
To ensure a successful analysis, organizations should engage stakeholders across departments, utilize structured decision-making frameworks, and conduct thorough cost-benefit evaluations. Pilot testing can also provide valuable insights before making a final decision.
Are there industries where building AI solutions is more advantageous than buying?
Yes, industries with unique data requirements or regulatory frameworks, such as healthcare and finance, often benefit from custom-built AI solutions. These industries typically require tailored approaches to ensure compliance and provide competitive advantages.
What role does vendor support play in the decision to buy an AI solution?
Vendor support can be critical when buying an AI solution, as it often includes ongoing maintenance, updates, and access to expertise. Organizations should evaluate the level of support offered to ensure it meets their long-term needs and enables smooth integration.
Conclusion
Choosing whether to build or buy an AI solution isn’t just a technical or financial decision. It’s a strategic one with long-term consequences.
Building offers deep customization and full control, but it comes with high upfront costs, slower time-to-market, and ongoing resource demands across infrastructure, maintenance, and talent. It also introduces operational risks, including key-person dependency and the burden of managing continuous updates internally.
Buying, on the other hand, delivers a faster path to deployment, predictable costs, and built-in support from a dedicated vendor team. For most organizations, this means faster ROI, reduced internal strain, and fewer long-term risks.
If your goal is to optimize visual inspection workflows without the complexity of building from scratch, our AI platform delivers high-accuracy results from day one—with minimal setup and zero infrastructure headaches.
Request a free demo and see how quickly you can start saving time and money.