01 / ACCESSSeparate the three access paths first
Closed cloud products and APIs run by the vendor update quickly and simplify deployment. Open weights let you obtain parameters and run inference on your own device or server. Hosted open models are downloadable in principle but currently run by another provider. Truly local operation keeps model files and inference on your own device after download. Strict open source also concerns training code, license, data transparency and redistribution rights. Openness is a bundle of rights, not one label.
02 / TRADEOFFSControl brings operations back to you
Local models can reduce material leaving the device, fix versions, work offline and allow customization; the cost is downloads, memory, speed, heat, updates, backup and security becoming your responsibility. Cloud services centralize those costs but require accepting pricing, policy change and data boundaries. Local does not mean an application never uses network: check logs, updates and plugins. A small model on an ordinary computer is not equivalent to a cloud flagship.
03 / LICENSERead the license, not a label
Code access, weights, modification, redistribution and commercial use may all have different conditions. For example, gpt-oss is marked Apache 2.0, DeepSeek-R1 weights are described under MIT with upstream-license caveats, and Meta Llama uses a Community License with attribution, use and large-scale conditions. Before commercial use, read the license attached to the exact version you downloaded.
04 / DECISIONChoose from ordinary work outward
For everyday low-sensitivity work, begin with a mature web or desktop product. To learn local models or retain suitable material on-device, begin with a small Ollama model. For organizational, client or regulated data, do not skip the organization’s data and security rules merely because something says “local”. A saved model file does not guarantee its interface, quantization format or toolchain stays compatible forever.

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