In the rapidly evolving landscape of artificial intelligence, the decision between utilizing API-based or self-hosted Large Language Models (LLMs) is increasingly becoming a critical consideration for enterprises. Each approach offers distinct advantages and challenges, influencing the scalability, flexibility, and security of AI applications. This blog explores the key differences between these two options, with the hopes of helping the reader deciding which one is best for them.

API-based vs. Self-hosted LLMs: What is the difference?

API-based LLMs:

API-based LLMs are models provided over the internet (via an API as the name suggests!), allowing developers to integrate AI capabilities into their applications without the need to manage the underlying infrastructure. The LLMs are hosted in a 3rd party environment, and you request the LLM through the API. This model offers convenience and ease of use, with the AI provider managing updates, scalability, and maintenance.

Recently, some LLM providers are now allowing these proprietary models to be deployed in your VPC, however this typically comes with vendor lock-in and expensive multi-year contracts.

Self-hosted LLMs:

Self-hosting involves running open-source LLMs on your own infrastructure (either VPC or on-prem). This model offers greater control over the data, customization, and operations. It allows for stringent security measures, considering the data never leaves your enviornment, making it an attractive option for enterprises concerned with data privacy and regulatory compliance.

Key Areas of Consideration

When deciding between API-based and self-hosted LLM solutions, enterprises should consider several key factors:

The Case for Self-hosting

Self-hosting LLMs is increasingly recognized as a strategic advantage for enterprises. It addresses critical concerns around data sovereignty, customization, and operational costs. However, the perceived complexity and resource requirements have historically deterred organizations from adopting this approach.

This is where TitanML's Titan Takeoff comes into play. Titan Takeoff is designed to bridge the gap, making self-hosting as straightforward as using API-based models. By simplifying the deployment, management, and scaling of LLMs, Titan Takeoff enables businesses to leverage the benefits of self-hosting without the traditional barriers.

Overcoming the Challenges with TitanML

The major downside of using API-based models is their inherent limitations in building and using custom AI solutions tailored to specific enterprise needs. TitanML recognizes this challenge and offers Titan Takeoff as a solution that combines the ease of API-based services with the flexibility and security of self-hosting.

With Titan Takeoff, enterprises can:

• Easily deploy LLMs in their own environment, ensuring data privacy and compliance.
• Customize and scale their AI solutions without the limitations imposed by third-party APIs.
• Reduce infrastructural burden by providing battletested state of the art infrastructure out of the box.

Encouraging Self-hosted LLMs for Enterprise Use

For enterprises looking to harness the full potential of AI while maintaining control over their data and infrastructure, self-hosted LLMs represent a compelling option. TitanML's Titan Takeoff empowers organizations to embrace self-hosting with confidence, combining the best of both worlds: the control and customization of self-hosting with the ease of use traditionally associated with API-based solutions.

As the AI landscape continues to evolve, the choice between self-hosted and API-based LLMs will significantly impact an enterprise's ability to innovate and compete. By leveraging solutions like Titan Takeoff, businesses can navigate this choice with greater ease, ensuring their AI strategies are both powerful and aligned with their broader operational goals.

Reach out to hello@titanml.co if you would like to learn more and find out if the Titan Takeoff Inference Server is right for your Generative AI applications.