Nikola is dual-licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) and a Commercial License.
The Nikola AI architecture, training methodology, reference implementation, and associated tools are licensed under the GNU Affero General Public License v3.0.
You are free to:
- Use Nikola for personal, educational, or research purposes
- Use Nikola in open-source projects
- Modify and distribute Nikola
- Train models using Nikola's architecture
- Deploy models for research or personal use
Under these conditions:
- Any modifications to Nikola must be released under AGPL-3.0
- If you run Nikola as a network service (e.g., inference API, cloud-hosted model), you must make the source code available to users
- Models trained with Nikola and deployed as network services must disclose the training code
- All derivative works must also be licensed under AGPL-3.0
Full license text: https://www.gnu.org/licenses/agpl-3.0.en.html
If you wish to use Nikola in a proprietary product or service without the AGPL-3.0 requirements, you must obtain a commercial license.
Commercial licensing allows:
- Use in closed-source AI products and services
- Running Nikola-based models as proprietary APIs
- Embedding Nikola in commercial applications
- Training proprietary models without source disclosure
- Custom support, optimization, and priority feature development
Commercial licenses are available for:
- AI research labs and companies
- Cloud AI service providers
- Enterprise organizations deploying internal AI systems
- Startups building AI-powered products
To obtain a commercial license, contact:
- Email: licensing@ailp.org (or alternative contact method)
- Include: Description of intended use, deployment scale, company information
Nikola represents a novel approach to AI architecture based on physics principles (toroidal holographic memory). We want this research to be freely available for education and scientific advancement while ensuring commercial users contribute to ongoing development.
- Researchers and academics: Use Nikola freely for research under AGPL-3.0
- Students and educators: Use Nikola freely for learning under AGPL-3.0
- Open-source AI projects: Use Nikola freely under AGPL-3.0
- Commercial AI companies: Support Nikola's development through commercial licensing
Q: I'm a researcher publishing a paper using Nikola. Do I need a commercial license?
A: No. Academic research is covered under AGPL-3.0.
Q: I'm building an open-source AI tool using Nikola. Do I need a commercial license?
A: No, as long as your project is also open-source and AGPL-3.0 compatible.
Q: I'm training a model with Nikola for my company's internal use. Do I need a commercial license?
A: It depends. If the model is only used internally (not exposed as a service), you may be covered under AGPL-3.0. If you're running it as an internal API/service accessed by employees, contact us to clarify.
Q: I want to offer Nikola-based inference as a paid API service. Do I need a commercial license?
A: Yes. Running Nikola as a network service for profit requires a commercial license.
Q: Can I use Nikola to generate training data for another model?
A: Yes, under AGPL-3.0, as long as the resulting model and training process are also open-sourced.
Q: What about models trained with Nikola - do they inherit the license?
A: Under AGPL-3.0, if the model is deployed as a network service, the training code must be disclosed. Commercial licenses remove this requirement.
Q: How much does a commercial license cost?
A: Pricing varies based on use case (inference scale, training scope, company size). Contact licensing@ailp.org for a quote.
Educational institutions using Nikola for teaching or research are always covered under AGPL-3.0, even if they receive funding from commercial sponsors, provided:
- Research results are published openly
- Student projects remain educational in nature
- Any commercial spin-offs obtain separate commercial licensing
Copyright (c) 2024-2025 Alternative Intelligence Liberation Platform (AILP)
All rights reserved.
Nikola is a trademark of AILP.
Nikola's core innovation involves:
- Toroidal holographic memory structures
- Golden ratio × π optimization for memory distribution
- Physics-based learning dynamics
These architectural concepts are published openly to advance AI research. We encourage academic exploration and citation while ensuring commercial implementations support continued development through licensing.