# Survey of Artificial Intelligence Model Marketplace
## 6 Challenges and Future Directions
For instance, DataStax is built with NVIDIA AI and provides a platform for registered members to try the AI models on their designed virtual IDE. Online service is provided if a customer has any questions about the development of the AI model. However, DataStax requires its members to have strong technical backgrounds, understand databases, know how to do data mining, etc. * _Security:_ AI being a double-edged sword. AI models are vulnerable to attacks, such as adversarial attacks, model inversion and evasion, reverse engineering, etc., which can threaten intellectual property and data confidentiality. AI marketplace requires an end-to-end safety and protection mechanism to safeguard and protect AI models and detect any potential threats targeting AI models or even the marketplace itself. Similarly, users need to trust the models they are using, especially in critical applications in the field of healthcare or finance. Clear documentation on how models work, their limitations, and their training data is essential. Target training is necessary to provide such kind of support from the AI marketplace. A follow-up evaluation is also needed to ensure users use the AI model properly. Providing such kind of training to users quickly and properly is another challenge that the AI marketplace needs to consider. * _Future Marketplaces:_ In the future, AI model marketplaces could evolve to include small language models (SLMs) [119] and agentic AI models [120] alongside the predictive and generative AI models in the catalog of its trading models, creating a more diverse model trading ecosystem. SLMs, with their efficiency and task-specific focus, will be preferred by buyers needing lightweight, resource-friendly solutions. These models will be particularly valuable for AI on-device, real-time applications, and privacy-sensitive use cases [121]. These Marketplaces may offer SLMs as task-specific tools, customizable solutions, or subscription-based libraries, making them accessible to businesses with limited computational resources. Agntic AI models, on the other hand, will introduce autonomy and decision-making capabilities to their operating environment. These models may be employed in complex, goal-oriented tasks like supply chain optimization or autonomous customer support [84; 122]. Marketplaces may offer agentic AI as a service, where buyers pay for outcomes rather than owning the models. However, their integration will require robust monitoring tools and strict compliance frameworks to address ethical and regulatory concerns, given the risks associated with autonomous decision-making [120]. In order to handle this diversity, model marketplaces may need to adapt by categorizing models based on their type and use case, ensuring interoperability, and providing tools for customization and fine-tuning. Ethical and regulatory compliance will be critical, especially for agentic AI, while performance metrics and transparency will help buyers evaluate and trust the models. Despite these potentials, challenges like scalability, buyer education, and ethical concerns will persist, requiring ongoing innovation monitoring and governance. By addressing these needs, future marketplaces will empower buyers to effectively leverage the full spectrum of AI models or technologies as they evolve daily.