An API (Application Programming Interface) is a set of rules and protocols which allow different software applications to communicate and interact with each other. It enables developers to access specific features or data from external services, libraries, or platforms, making it useful for building AI-powered applications.

API model deployment is an effortless process.

In terms of AI adoption, many enterprises currently rely on API-based model deployments. This is because, historically, proprietary large language models, including GPT-4, have been considered the gold standard, whilst open source models were seen as significantly cheaper but ultimately, poor-quality substitutes. Yet, in 2023, there were significant improvements in the quality of open source models. In December, Mistral AI’s Mixtral demonstrated significantly better performance than GPT-3.5. As major players, including Middle Eastern nations and Meta, continue to invest heavily within this space, we expect Llama 3 (or equivalent) to be as good, if not better than GPT-4. This point at which open source models will be as good as proprietary ones, will mark a significant turning point for the industry. It will mean the use of API-based models over self-hosted models will no longer be a decision taken solely on the basis of model quality, and instead move to a more complex one, which takes privacy, control, ease of use and cost into account. We therefore expect a significant number of enterprises to move from deploying API-based models, to self-hosted ones. Many of our clients have already planned for this eventuality and are now using the Titan Takeoff Inference Server to make the process of self-hosting models as pain free as possible.

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