Open, closed, or both: A framework for running AI workloads
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Where each workload runs now matters more than which model family you pick. That choice sets your cost floor, your latency, and how much control you keep.
Frontier closed models and fine-tuned open models are both strategic choices, and most companies need both.
This guide is about the decision underneath that one: not which model you bet on, but where you run the models you pick. You leave with two things, a decision framework to run against your own workloads, and five properties that make any provider's platform claims verifiable.
You’ll learn:
Frontier closed models and fine-tuned open models are both strategic choices, and most companies need both.
This guide is about the decision underneath that one: not which model you bet on, but where you run the models you pick. You leave with two things, a decision framework to run against your own workloads, and five properties that make any provider's platform claims verifiable.
You’ll learn:
- Five questions that decide where a workload runs. Volume, latency, customization, data, cost at scale.
- Where each model type wins.
Closed for discovery and the hardest problems. Open for custom weights and sovereignty. - The five-property test for "your model."
Verify a platform's claims before you sign. - What production fidelity takes.
Holding weights and serving them well are different capabilities.