Build vs Buy vs Hybrid: Strategic Decision Making for Critical Business Systems

The build vs buy software dilemma is a challenge that most businesses that operate on software face relatively early in their lifetime.

This article reveals how we approached this decision when developing an AI-powered ergonomic assessment tool: from weighing pros and cons to technical considerations and implementation strategy.

Why build vs. buy is a critical strategic question

The difficulty of the build or buy decision is not just about technology; it relates to every aspect of the business and its competitive advantage.

Building custom software often means a higher upfront investment but results in greater software ownership. Buying an off-the-shelf solution speeds up deployment and reduces risk. However, it may lock companies into rigid workflows or limit long-term flexibility.

In Binarics' project for a US healthcare company focused on automating ergonomic assessments using computer vision and machine learning, the decision was especially critical as the customer wanted a differentiated project with control over sensitive workflows. In particular, the build vs. buy decision framework depended on the following factors:

• Pose estimation to analyze posture and joint angles
• Object detection to classify office equipment (chairs, desks, monitors, keyboards, etc.)
• AI-driven reporting to highlight ergonomic risks

No existing product on the market could combine these advanced capabilities with the needed precision.

By building a primarily custom hybrid solution, we ensured full control over AI/ML model training, compliance with HIPAA and internal security policies, and the ability to fine-tune algorithms for niche ergonomic benchmarks. This gave us a differentiated position in a competitive healthcare space.

We also enabled seamless integration with existing company platforms and retained the flexibility to expand features (such as personalized risk scoring and real-time feedback) in the future.

This article focuses on how we did it and whether you should do the same.

Key factors to consider when choosing build, buy, or hybrid

Competitive differentiation

Build when the feature directly drives your core value proposition, pricing power, or customer loyalty. For example, building a custom AI pose estimation model is essential if you're an AI fitness app and your success depends on highly accurate pose detection.

Pricing power means that you can charge more than your competitors because your product is better. Customer loyalty means your customers stay loyal to you for other reasons, including even unique AI or customization that neither of your competitors has.

Some examples of tools that work for competitive differentiation are proprietary algorithms, recommendation engines, ML pipelines, and novel user flows.

Build if:

• The feature is core IP
• Losing exclusivity would weaken your position
• Your version needs to be fundamentally different

Buy if:

• It's a common feature used across industries (e.g., calendars, CRMs)
• You gain no strategic advantage from owning it.
• You can't answer "yes" to: Does this differentiate us?

Hybrid tip: Layer unique UX or analytics on top of commoditized functionality (e.g., analytics dashboards over standard tracking SDKs). Use open-source or headless foundations where possible, then tailor the experience layer.

Cost (not just upfront)

When it comes to cost to build vs buy, build has hidden ones:

• Engineering hours, security reviews, QA cycles
• Maintenance (15–20% of original build cost per year)
• Compliance certifications (SOC 2, HIPAA, etc.) can cost $100k+

Buy spreads cost across vendors' entire customer base, so it ends up being way cheaper.

Build if:

• Long-term value outweighs high initial cost
• You'll use it across multiple products or services

Buy if:

• Compliance burden is high (HIPAA, PCI DSS, ISO 27001)
• Maintenance requires 24/7 support or uptime SLAs
• You're underestimating the cost of documentation, training, and bug-fixing

Hybrid tip: Build only the IP-driven layer; outsource logging and infrastructure. Consider combining custom logic with vendor-managed hosting (e.g., Firebase, Vercel).

Scale and complexity

The more systems and users your feature touches, the more expensive the building becomes.

Build if:

• You require high customization, and you control all use cases
• Performance needs are extreme or highly specific

Buy if:

• You find yourself navigating internationalization, timezone issues, platform quirks, or legacy system connections you don't want to build for
• There are more than 20% "edge cases" in your requirements

Hybrid tip: Use vendor APIs to handle hard infrastructure (e.g., calendar syncing, auth flows), and build proprietary workflows. Maintain control only over bottlenecks and scale-sensitive logic.

Market maturity

Buy when there's a mature vendor ecosystem, which indicates the problem is well understood. Otherwise, build.

Market maturity means that edge cases are solved, pricing is stable, and integration patterns are standardized.

Build if:

• You're entering a niche or underserved market
• Off-the-shelf tools miss critical features you can't add through customization

Buy if:

• There are 3+ strong competing solutions
• Their roadmap moves faster than you can build
• Vendor consolidation is improving value (not harming it)

Hybrid tip: Buy a modular solution and extend it via plugin architecture, API layers, or SDKs. Let vendors handle core, and build around the edges.

Time-to-market

Buy if:

• You need to launch quickly
• You're testing a non-core feature
• Competitors already offer it

Build if:

• Time-to-market delay is worth the unique result
• You're optimizing for long-term control

Don't build to "feel more in control" unless you've identified a real risk of vendor lock-in or roadmap misalignment. Building just for the sake of control does not really cut it.

Hybrid tip: Use vendor infrastructure (e.g., AWS SageMaker, Twilio) to reduce build time. Build your innovation on top of that scaffolding.

Team expertise and bandwidth

Build only if your team can handle the following:

• Engineering
• QA
• DevOps
• Security
• Internal enablement (docs, training)

Build if:

• You already employ domain experts
• You can iterate rapidly and support long-term evolution

Buy if:

• You'd need to hire a dedicated team just to maintain it
• You'd have to context-switch away from core projects

Hybrid tip: Focus internal talent on innovation. Use vendors for infrastructure, integrations, and security layers. Consider building thin customisation layers on top of managed services.

Internal support & adoption

Every internal tool needs documentation, rollout, and training.

Build if:

• You've aligned stakeholders and internal champions
• Your team needs something tailored and wholly owned

Buy if:

• Internal adoption will be low without vendor support and SLAs
• You lack time/resources for onboarding and L&D

Hybrid tip: Buy platforms that offer admin tooling and user training, then embed custom modules or extensions tailored to your workflows.

Data security and compliance

Build if:

• You need granular control over access, encryption, or data flow
• You're storing sensitive data under local jurisdiction (e.g., GDPR, HIPAA zones)

Buy if:

• Vendors already offer certified environments with audit trails
• Your security team is small or reactive

Hybrid tip: Use vendor-certified cloud infrastructure, but own critical enforcement points, such as access control, encryption-at-rest, custom monitoring, and audit logging.

Strategic control

Build when a lack of control introduces real business risks.

Build if:

• Vendor lock-in could threaten business continuity
• You must respond rapidly to regulatory changes
• You're innovating faster than vendors can

Don't build based on theoretical risks. Instead, clearly define specific scenarios where relying on a vendor could harm your business.

Hybrid tip: Own orchestration and APIs; treat vendor components as replaceable modules. This allows for future migration without rewriting everything.

Revenue impact

Build if:

• The feature directly generates revenue
• It allows premium upsells or pricing differentiation

Buy if:

• It's a cost center (auth, search, notifications)
• Customers don't pay more for it
• It's critical, but not monetizable

Hybrid tip: Build monetization logic and dashboards, but use vendor systems for metering, invoicing, or customer management behind the scenes.

Summary: When to build and when to buy

Decision framework: A systematic approach to choosing the right path

Making a sound build or buy decision requires more than comparing upfront costs. A systematic framework should include financial modeling, strategic alignment, and risk assessment to capture short- and long-term impacts. Here are the factors that should be a part of that framework:

Financial evaluation: TCO and ROI

Total cost of ownership (TCO)

• Build: Development hours, infrastructure, ongoing maintenance, staff training, upgrades.
• Buy: Licensing/subscription fees, integration costs, vendor support, and potential customization fees.
• Hybrid: Combination of custom features plus cloud/vendor infrastructure costs. Like, you pay for an AI model nd a pre-developed authentication tool.

Return on Investment (ROI)

• Short-term: How quickly the solution delivers measurable savings (time saved, manual processes replaced).
• Long-term: Gains in competitive edge, scalability, and reduced dependency on external vendors.

Example from our case:

We calculated the cost of ergonomic assessments (AI ergonomics software development ):

• Manual/human-led process: ~$0.30 per assessment
• AI-powered automated assessment: ~$0.03 per assessment

This 10x reduction in unit cost dramatically shifts the unit economics, proving that the build-first hybrid model, while more expensive upfront, pays off rapidly for our client.

Strategic fit and alignment

When deciding, evaluate whether the solution aligns with core competencies and strategic objectives:

• Core competence: If the software defines your competitive edge (as AI/ML ergonomics did for our client), building is the priority.
• Vendor lock-in: Buying may tie you to the vendor’s roadmap. Hybrid helps mitigate lock-in by blending custom components with cloud services.
• Control over IP: Owning intellectual property ensures long-term flexibility and potential to reuse or commercialize technology beyond the initial project scope, which is an investment in the future.

Risk assessment

A balanced framework should explicitly weigh risks:

• Technical risks: Can the solution meet performance and accuracy requirements? (e.g., pose estimation accuracy in complex office environments).
• Financial risks: What if adoption is slower than expected, or costs escalate (e.g., subscription hikes, hidden integration costs)?
• Operational risks: Will employees adopt the solution? Can existing workflows support it without disruption?

In our case, building AI/ML models carried technical risk (accuracy of pose detection and object recognition). Still, it mitigated long-term financial risk by lowering assessment costs and eliminating reliance on costly external services. So, in a way, the two risks ended up balancing each other out.

Our ergonomics project's clear financial upside and the need for unique AI-driven capabilities made Build with Hybrid cloud infrastructure the optimal software procurement strategy.

Technical considerations: Architecture and integration complexity

In our ergonomic assessment project, we initially evaluated ready-made APIs, such as object recognition, to accelerate development. However, we ultimately opted to build core components in-house due to several critical limitations:

• Limited accuracy and customization

Most third-party models are trained on generic datasets. They failed to meet our ergonomic-specific needs, such as accurate joint angle analysis and distinguishing between ergonomic and standard keyboards.

• Lack of flexibility

External services offered limited API support, making it difficult to embed custom logic, detailed logging, or adaptive workflows suited to diverse office layouts and lighting conditions.

• Vendor lock-in risks

Relying on external providers would have tied us to their pricing, SLAs, and update cycles – introducing long-term risks to cost predictability and platform stability.

• Upgrade compatibility

As ML frameworks, SDKs, and cloud services evolve, relying on external platforms would make it harder to control version management and ensure backward compatibility. Building internally allowed us to manage upgrades on our own terms and maintain long-term system stability.

We retained full control over model behaviour and future extensibility by building internally and selectively integrating cloud infrastructure.

Implementation strategy: How we built a custom solution

Our software procurement project focused on automating ergonomic assessments for office workers using AI and computer vision. The goal was to analyse video recordings of workplace setups to assess posture and generate risk reports. This allowed for large-scale, non-intrusive evaluations that would otherwise require manual ergonomic review.

The system was designed to handle:

• Pose estimation of seated/standing employees in a typical static posture
• Joint angle calculations (knees, hips, back, neck, arms, wrists)
• Object detection of workplace items such as laptops, chairs, lighting, and desks
• Risk score generation and ergonomic report output

We built the system to distinguish between:

• Ergonomic vs. traditional keyboards
• Adjustable vs. fixed desks and chairs
• Light sources (windows, lamps)
• Peripheral devices (mice, trackpads, cameras)

The challenge was designing a reliable solution that worked across diverse environments.

To meet project demands, we assembled a dedicated team with specialized roles:

• Solution Architect (part-time): Oversaw the overall architecture and directed tech decisions.
• AI/ML Engineer: Led pose and object detection model development, including data labelling and tuning.
• Backend Developers (2): Integrated models into API services and managed inference pipelines.
• Integration Developer: Connected the new system to existing internal platforms.
• Flutter Developer: Created the mobile interface for image capture and result display.
• Project Manager: Managed Agile workflows and cross-functional delivery.

Our system architecture was hybrid: custom-built ML components combined with scalable cloud services.

• AI/ML Frameworks:
• MMPose for pose estimation
• YOLOv8 for object detection
• PyTorch/TensorFlow for core model training
• Annotation & Training Tools:
• Label Studio for data annotation
• AWS SageMaker Ground Truth and Notebooks for model training
• Infrastructure:
• AWS Lambda for serverless compute
• DynamoDB for stateful storage
• AWS S3 and SQS for data management and orchestration
• Python for backend logic, Flutter for frontend (mobile)

We retained control over the ML components while using cloud services for scalability and cost-efficiency.

We executed the project using Agile methodology across the following phases:

Phase 1 – Discovery & planning

• Gathered requirements and defined ergonomic evaluation metrics
• Created initial risk model and object taxonomy

Phase 2 – Data collection & annotation

• Captured sample videos across varied office settings
• Annotated pose keypoints and object labels

Phase 3 – AI/ML model development

• Trained baseline models with MediaPipe and YOLO
• Transitioned to MMPose for better customizability

Phase 4 – System architecture & backend

• Deployed AWS Lambda/DynamoDB infrastructure
• Developed real-time and batch processing pipelines

Phase 5 – Mobile app & integration

• Built employee-facing app with Flutter
• Integrated reporting API with company health platform

Phase 6 – Testing & deployment

• Conducted QA, pilot testing, and A/B validations
• Rolled out system across client sites

This phased approach allowed parallel development and testing, accelerating delivery without compromising flexibility for tuning or future enhancements.

Measuring the impact of your decision

Making a build or buy software decision is not the end. It is critical to track whether the choice delivers measurable business value over time. The right measurement framework combines financial, technical, and strategic indicators.

Tracking cost per operation or transaction

A reliable way to evaluate efficiency is by monitoring the unit cost of each transaction or operation:

• Build: Calculate amortized development costs + ongoing infrastructure per transaction.
• Buy: Monitor per-license or per-seat costs against actual usage.
• Hybrid: Track cloud service usage (API calls, inference runs) alongside amortized custom development costs.

In our ergonomics project, shifting from manual assessments ($0.03 each) demonstrated a 10x improvement in cost efficiency.

Performance metrics and quality indicators

To make sure the solution performs as intended, define clear KPIs:

• Accuracy & reliability: For AI models, track pose estimation precision, false detection rates, and report consistency.
• System uptime & latency: Measure availability (e.g., 99.9% SLA) and average response time for user-facing applications.
• Adoption rate: Monitor how many employees or teams actively use the system vs. those relying on legacy/manual workflows.
• Cost savings realized: Compare operational expenses before and after adoption.

Time-to-market and competitive advantage

Beyond operational efficiency, evaluate how your choice influences broader business goals:

• Time-to-market: Track the actual duration from concept to deployment compared with industry benchmarks.
• Innovation advantage: Assess whether building proprietary technology has unlocked differentiators (e.g., ergonomic AI models unavailable off-the-shelf).
• Market positioning: Monitor client acquisition or retention influenced by offering a more advanced product.

Suggested analytics dashboard

To unify these indicators, organizations should implement a decision impact dashboard combining:

• Financial metrics: TCO, ROI, cost per transaction over time.
• Performance metrics: Accuracy, uptime, latency, error rates.
• Adoption metrics: Active users, % of workflows automated, employee satisfaction.
• Strategic metrics: Competitive benchmarks, time-to-market, feature innovation index.

Such a dashboard could be built in Tableau, Power BI, or AWS QuickSight, integrating directly with operational data and cloud usage logs.

By measuring not just costs but also adoption, quality, and time-to-market, companies can validate whether their hybrid vs. build or buy decision continues to create strategic advantage.

Conclusion: Strategic takeaways

A smart software sourcing strategy starts with a realistic build vs buy software analysis.

In our case, off-the-shelf ergonomic tools couldn't offer reliable pose estimation for static seated postures or the flexibility to process video locally due to strict data protection policies. That's why we built a custom computer vision pipeline in-house and combined it with existing infrastructure on AWS. This hybrid approach gave us the accuracy and control we needed without overengineering the entire system.