Decision Framework

Build vs Buy AI:
A Framework for Enterprise Teams

"Should we build our own AI or buy an off-the-shelf solution?" is the wrong question. The right question is: which approach creates a sustainable competitive advantage for your specific use case, data environment, and business model? — Here is the complete framework.

Total cost of ownership Customisation & privacy Time to value
6–18mo
Buy: Typical Lock-in
40–70%
Custom: Lower Long-term Cost
98%
Client Satisfaction
Decision Factors
Decision Framework
Total Cost of Ownership (3-year)95%
Customisation & Data Privacy92%
Time to First Value90%
Competitive Differentiation88%
TCO
Privacy
Lock-in
Scale
⚖️ Build vs Buy
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The Framework

We Help You Build or Buy Well

End-to-end capability — from strategy and build to integration, monitoring, and ongoing support.

🔍

AI Feasibility Assessment

We assess your specific use case, data environment, and requirements — and give you an honest recommendation on whether to build, buy, or adopt a hybrid approach, with TCO analysis.

💰

Total Cost of Ownership Modelling

3-year TCO modelling comparing custom build vs leading SaaS alternatives — including development, infrastructure, maintenance, and projected usage-based vendor costs at your scale.

🏗️

Custom AI Development

If building is the right answer, we design and deliver the custom AI system — with clear ownership, no vendor lock-in, and long-term support on your terms.

🔌

SaaS AI Integration

If buying is the right answer, we help you evaluate, select, and integrate the best-fit SaaS AI solution — with proper security review and integration architecture.

🔀

Hybrid Architecture Design

Most enterprises benefit from a hybrid approach — buying for non-differentiating capabilities, building for core competitive use cases. We design the right split for your situation.

📊

Vendor Evaluation & RFP Support

If you are evaluating AI vendors, we provide independent technical evaluation, scoring frameworks, and RFP support — without vendor bias.

How We Work

Our engagement process in 5 steps

A disciplined, outcome-focused approach from first call to go-live.

  1. 1

    Use Case Scoping

    Define precisely what the AI system needs to do, the data it requires, and the business outcome it must deliver.

  2. 2

    Requirements Analysis

    Document functional and non-functional requirements — accuracy, latency, compliance, integration, and scale — that any solution must meet.

  3. 3

    TCO Modelling

    Build a 3-year total cost model comparing build vs buy — including all hidden costs (implementation, customisation, scale-up, migration risk).

  4. 4

    Recommendation & Architecture

    Provide a concrete recommendation with rationale — and if building, a proposed architecture and delivery plan.

  5. 5

    Execution

    Deliver the chosen solution — whether custom build, SaaS integration, or hybrid — with clear milestones and measurable success criteria.

Technology Stack

Tools & Frameworks We Master

A production-tested, vendor-agnostic stack built for enterprise security and compliance requirements.

If You Build (Custom AI)

PyTorchLangChainMLflowKubernetesPineconeFastAPI

Common SaaS AI Vendors

OpenAI APIGoogle Vertex AIAWS AI ServicesAzure AICohereHugging Face

Evaluation Framework

TCO ModellingVendor ScorecardSecurity ReviewPoC BenchmarkingIntegration Audit

Deployment & Cloud

DockerAWSAzureGCPOn-premiseTerraform
Real-World Scenarios

Use Cases by Industry

Production AI systems we have built across regulated, data-heavy industries.

BFSI

Build: Fraud Detection System

SaaS vendor quoted $800K/year at target transaction volume. Custom XGBoost + streaming API built in 12 weeks — $120K one-time cost, $40K/year maintenance. 5-year saving: $3.6M.

BuildFraudBFSI
Healthcare

Build: Clinical NLP

No HIPAA-compliant SaaS vendor met accuracy requirements on clinical text. Custom fine-tuned BERT deployed on-premise — 93.7% F1, fully compliant, zero data leaves hospital.

BuildNLPHIPAA
E-commerce

Buy: Basic Customer Chat

Standard FAQ chatbot — no proprietary data advantage. Deployed Intercom AI in 3 weeks. Custom build would have cost $150K for comparable functionality.

BuyChatbotFast
Retail

Hybrid: Recommendations + Analytics

Custom recommendation engine (proprietary catalogue + customer data) + Tableau for BI (commodity analytics). Build where differentiated, buy where generic.

HybridRecommendationsBI
Logistics

Build: Route Optimisation

SaaS route optimisation tools lacked API access to proprietary network constraints. Custom RL model built in 14 weeks — 22% fuel cost reduction, $4.2M annual saving.

BuildRLLogistics
SaaS Startup

Buy-then-Build Strategy

Bought an AI SaaS tool to validate the use case in Month 1. Proved value, then built custom in Month 6 — retaining learnings, eliminating $240K/year vendor cost.

Buy-then-BuildStartupValidation
Client Voices

What Teams Say After Shipping with Us

Real results from teams who needed clear answers, not vendor pitches.

AndolaSoft has been a valued partner providing excellent customer service. Issues with clients or troubleshooting are handled in a timely manner and positive resolution is always the outcome.
JK
Jim Kaplan
Founder, AuditNet
I got a recommendation on AndolaSoft. They are more than half the cost, they have a can-do attitude, and they are responsive, timely, and easy to work with.
CV
Caroline Van Sickle
Pretty in my Pocket, Atlanta GA
Andolasoft team is very hardworking, dedicated and professional that follows through with their goals. The technical leadership is also a superior value to any other developers.
ZN
Zeid Nasser
Editor-in-Chief, theCollegeDriver.com
FAQ

Frequently Asked Questions

Build when: your use case is core to competitive differentiation, you have proprietary data that gives you an edge, you need tight integration with internal systems, you have specific compliance requirements off-the-shelf tools cannot meet, or the 3-year TCO of building is lower than buying.

Want an Honest Build vs Buy Assessment?

Tell us your use case, data environment, and scale. We will give you a clear TCO analysis and recommendation — with no bias toward either outcome.