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