Fine-tuning open-weight models (Kimi K2.6, DeepSeek V3.1, Qwen 3.5, Nemotron 3, GPT-OSS, Inkling, and more) on your business data. Own the weights.
Palantir invented the FDE in 2005 because its customers could not disclose their work. Twenty years later the constraint is identical: the client cannot put proprietary data into multi-tenant SaaS, and the generic model keeps missing because it was never tuned on their corpus.
We rewrote our evaluation so a trained model has to be right, not merely return HTTP 200. The first model scored honestly failed. Then the platform refused our own training data, and it was correct to.
The 2026 recursive self-improvement literature names the exact mechanisms that keep an improving loop safe: checks with teeth, held-out keep-better gates, and auditable memory. Forward deployed engineers are being asked to build these for clients. Here is the pattern, from a loop that runs against its own production weekly.
An agent that can act on its own is the wrong shape for a determination that carries legal weight. Draw the workflow, put a human gate in it, and the run pauses in an approval inbox until a person decides. Verified in production, not on a roadmap.
Fine-tuning adapts a model to your task. Distillation transfers a bigger model's behaviour into a smaller one. They solve different problems, and the difference decides what you can afford to run.
Fine-tuning is a one-time cost. Serving is the bill that arrives every month. Quantization is the main lever on it, what it trades away, and how to tell whether the trade cost you anything.
LoRA is the technique that made fine-tuning cheap enough for normal businesses. What it is, why it costs a fraction of full fine-tuning, and what it means for owning your model.
Closed models are easy to start with; open-weight models let you own what you build. A clear-eyed comparison across cost, control, privacy, and capability, and when each actually wins.
As AI usage scales, closed-API bills balloon, often for tasks that never needed a frontier model. The levers that actually cut LLM costs, and why a fine-tuned open-weight model is the biggest one.
Moonshot AI just released Kimi K3, billed as the world's first open 3T-class model, with open weights and native agentic abilities. What's known, what it signals for open AI, and how to fine-tune and own models like it on InfoPlatform.ai.
An OpenAI-compatible API means you can swap the model behind your app with a one-line change. What that actually means, why it prevents lock-in, and how it lets you run any model in your existing tools.
When you use a closed AI API, your data leaves your control and you own nothing you build. Why data privacy and model ownership are the same problem, and how open-weight fine-tuning solves both.
Upload your data, fine-tune an open-weight model, and own the result in hours. No ML expertise required.
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