Inference Logs as Training Data

ash-lewis on the hidden moat of open-source deployment:

"It's very difficult to get the data to fine-tune the models, which — it turns out — you have that data. It's in your inference logs, but we just don't get it from the LLM providers."

When you run on a frontier API, the provider keeps the logs. When you deploy an open-source model yourself, the inference logs are yours — and they are the exact distribution you want to fine-tune on (real users, real queries, real edge cases).

Why this matters

This is the strongest 2026 argument for OSS model deployment that isn't about cost: it's about owning the re-training signal. Pairs with faye-zhang's post-training cycle — the sub-agent pipeline is cheap only if the training data exists. Inference logs are that data, and only self-hosted deployments surface them.