Build vs Buy: Making Smart Decisions About Custom LLM Models

Should your organization build a custom LLM model or use existing solutions? This is one of the most critical strategic decisions in enterprise AI, and getting it wrong can cost millions. ⏱️ TIMESTAMPS: 0:02 - Introduction: The Build vs Buy Debate 0:25 - When Building Custom Models Makes Sense 2:02 - The Real Costs of Building Your Own Model 3:35 - Real-World Example: AIDoc at AWS Expo 4:09 - The Case for Off-the-Shelf Solutions 5:44 - Optimizing Model Selection and Cost 6:46 - Final Recommendations and Wrap-Up In this episode, I break down the true costs and considerations of building custom LLM models versus leveraging existing solutions. Drawing from real-world insights from the AWS Expo, including AIDoc's experience, I explore: ✅ When building custom models makes strategic sense ✅ The hidden costs of LLM development (data prep, training, maintenance) ✅ How to optimize model selection using platforms like AWS Bedrock ✅ Why the most expensive model isn't always the right choice ✅ Practical frameworks for making build vs buy decisions Whether you're a CTO evaluating AI strategy, a technical lead implementing LLM solutions, or a business leader trying to understand the AI landscape, this episode provides actionable insights for making informed decisions. 🔔 SUBSCRIBE for weekly AI briefings and practical insights on implementing AI in your organization. 💬 DROP A COMMENT: Are you considering building a custom LLM or using existing models? What's your biggest challenge in this decision? 📚 RESOURCES MENTIONED: - AWS Bedrock - Anthropic Claude models (Opus, Sonnet, Haiku) - AIDoc presentation insights