Build vs Buy vs Fine-Tune: The $2M Decision Every AI PM Gets Wrong | AI for PMs Ep.9

Build vs Buy vs Fine-Tune: The $2M Decision Every AI PM Gets Wrong | AI for PMs Ep.9 60% of AI teams waste months building what they could buy. Others pay $500K/year in API costs for something they could fine-tune for $50K once. The difference? A decision framework. In this episode, I break down all 3 approaches with real costs, real timelines, and a 7-factor decision matrix you can use for any AI feature. Plus 3 case studies: Tesla/Spotify (Build), Notion/Canva (Buy), Bloomberg/Harvey (Fine-Tune). 💰 Cost Comparison: • BUILD: $500K – $2M+ (6-18 months) • BUY: $12K – $600K/year (2-6 weeks) • FINE-TUNE: $20K – $200K (1-3 months) 🔑 The 7-Factor Decision Matrix: 1. Differentiation — Is AI your core product or a feature? 2. Data Sensitivity — Can your data leave your infrastructure? 3. Budget — What can you actually spend? 4. Timeline — How fast do you need this? 5. Team — What talent do you have? 6. Control — How much do you need to control behavior? 7. Maintenance — Who handles ongoing work? 🔔 Subscribe for 2 new episodes every week! Next Episode: How to Evaluate if Your Product Actually Needs AI 👇 Comment below: Is your team building something you should be buying? #BuildVsBuy #aistrategy #FineTuneLLM #productmanagement #aiforpms #openaiapi #BloombergGPT #HarveyAI #teslaai #SpotifyML #notionai #AIDecisionFramework #LLMFineTuning #aiproductstrategy #machinelearning