Accelerate private AI adoption with Models-as-a-Service

OVERVIEW

When selecting an AI strategy based on open-source technology, many businesses opt for self-hosting their AI models, applications, and agents due to the significant privacy, transparency, and control benefits this approach offers.

However, choosing to self-host your AI Models presents you with numerous decisions and choices that are not typically offered by public AI providers. While this empowers you with greater control over your AI destiny, it’s crucial to avoid certain pitfalls that could lead to inefficiencies.

For example, it can be tempting to offer easy self-service access to AI accelerators (like GPUs) to end-users. However, this approach can sometimes result in duplication and inefficiencies.

The Models-as-a-Service pattern effectively demonstrates the benefits of moving up the stack by providing straightforward, self-service access to the models themselves, rather than the specialized and costly hardware they rely on. By adopting this pattern, you can offer easy access to private AI models for any number of users or applications, while maintaining control over usage and tracking costs with the utmost precision.

TAKEAWAYS:

• Learn when and how to provide developers with easy self-serve access to AI Models. Public Model providers do that very well, but some companies need deeper control and privacy
• Explore how the MaaS pattern brings an API gateway into the picture and enable your IT teams to provide easy self-serve access to private AI Models
• Review the best ways to ensure proper cost optimization, tracking, and chargeback when deploying Large Language Models

Any questions? please contact Elisa Navarro