Open-Weight Models
Models whose weights you can download and run yourself — often called open source, usually not quite.
When not to use it
- When a hosted API would do. You're taking on infrastructure, evaluation, and updates to save money you may not be spending yet.
- Without reading the licence. Several popular "open" models carry user thresholds or use restrictions that rule out common cases.
- Assuming open means auditable. Without training data you can observe behaviour and nothing else.
Reach for something else instead
- Hosted APIs — better models, no operations, and the arithmetic favours them longer than people expect.
- Private cloud deployment of a hosted model, when the concern is data residency rather than cost.
- A smaller task-specific model when the real need was one narrow job, not general capability.
Sources & further reading
- Touvron et al. (2023), Llama 2: Open Foundation and Fine-Tuned Chat Models — the release that made this mainstream, licence and all.
- Solaiman (2023), The Gradient of Generative AI Release — the spectrum from closed to open, framed clearly.
- Widder, Whittaker & West (2023), Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI — the argument that "open" is doing work here it wasn't designed for.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Calling them open source. Weights without data or training code is a compiled binary, not source.
- Underestimating the operational cost. GPUs, scaling, uptime, and updates are a team's work, not a weekend's.
- Skipping evaluation because the API provider used to do it. That job is yours now.
At a glance
FieldTools & Ecosystem
Core ideadownload the weights, run them yourself
Notopen source, in most cases
Real reasonsdata residency, volume, offline
Real costoperations and evaluation
DifficultyIntermediate
Flashcards for this concept
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Often compared with
Open-weight vs. hosted API — your hardware and your quality bar vs. someone else's, at someone else's price.