Nexart SDK + CLI are free.
The Canonical Node is where certification happens: deterministic execution, canonical outputs, and verifiable audit trails.
Plans:
• Free — 100 certified runs/month
• Pro — 5,000 runs for $6k/year
• Scale — 50,000 runs for $18k/year
• Enterprise — unlimited, from $50k/year
Built for fintech, simulations, AI pipelines, and anything auditors will ask questions about.
NexArt is often seen as a generative art tool, but that’s just its origin story.
It’s now deterministic execution infrastructure for builders: simulations, procedural systems, games, and verifiable pipelines.
What does that mean in practice?
👉 https://nexart.io/faq
BM
Few plan available on NexArt.io if you need to seal your process for letter review but the sdks is and will remain free
So if you are looking to use the nexart engine for art, gaming or anything else, without creating an engine yourself , you can too
New NexArt update 🚀
Canonical rendering now has:
• Account-level quota enforcement
• Explicit pricing tiers
• Hardened security architecture docs
• Clear protocol compliance standards
If results need to be reproducible, verifiable, and auditable, this is the foundation.
NexArt.io
As AI systems increasingly build on top of other AI systems, drift becomes invisible, and auditing breaks down.
NexArt is built to counter that: deterministic execution, canonical outputs, independent verification.
If results matter, reproducibility isn’t optional.
Quick update on the nexart protocol :
Codemode -> v1.8.4 published
Ui renderer -> v0.9.1 published
Cli -> 0.2.3 published
Node updated
What changes really is now the sdk are fully free , not intent to licence later anymore
Node will have a usage base system
Building the dashboard now
I’m building a deterministic “model runner + visualization layer” so researchers don’t have to build their own front-end every time.
You load a model (code + parameters + optional CSV), run it in a constrained deterministic runtime, and it generates
standard outputs (charts/tables/stats).
Each execution is saved as a verifiable run bundle (model version + inputs + hashes), so anyone can replay the run and get identical results, making sharing and peer review much easier.
What would make this most useful in V1?