Build vs. Buy Decision Advisory for LLM Use

Client: Global Insurance Tech Company
Engagement Duration: 2 Weeks
Service: Build vs. Buy Decision Advisory

Objective

Help the client evaluate the pros and cons of using commercial LLM APIs (like OpenAI or Claude), open-source models (like Mistral, LLaMA), or building custom fine-tuned models and recommend a go-forward decision.

What We Did

1. Use Case Deep Dive

Focused on their top priority building an internal assistant to generate insurance claim summaries and customer emails.

2. Options Analysis Framework

Assessed three paths:

• Buy: Use commercial APIs like GPT-4
• Borrow: Use open-source models hosted in a secure environment
• Build: Fine-tune a base model using their proprietary data

3. Evaluation Criteria

Compared each option across:

• Accuracy on domain-specific tasks
• Data privacy and compliance risks
• Cost of ownership
• Latency and scalability
• Customizability and vendor lock-in

4. Final Recommendation

Proposed a phased strategy:

• Start with a commercial API for fast prototyping
• Set up a private open-source model sandbox in parallel
• Re-evaluate the build/fine-tune strategy in 6 months

Outcomes

Decision Clarity: Clear path to begin with minimal investment and low risk

Security & Cost Awareness: Avoided premature infrastructure investments and compliance risks

Document Delivered: 12-slide executive summary comparing all options with pros, cons, and a decision flowchart

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