Train Your Own Model When Inference Exceeds $750/Day: Pallet's Playbook
marcbhargava · x · 2026-08-01
With AI token spend skyrocketing 10x as workflows moved to production, Pallet realized that relying on frontier models no longer made financial sense past a certain threshold, leading them to train their own production model.
Key takeaways from their experience:
- Break-even Point: The financial threshold between training an in-house model and using frontier APIs is around $750/day.
- Dense vs MoE: A smaller Dense model (27B) outperformed a larger MoE model. A smaller memory footprint left more room for long context and KV cache. When memory is the bottleneck, fewer total parameters beat fewer active parameters.
- Data is King: The single biggest performance improvement came from optimizing label taxonomy.
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