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The Challenges of AI in the Public Cloud
As enterprises increasingly turn to AI to drive innovation and efficiency, many face significant concerns regarding data privacy and security when deploying AI solutions in the public cloud. These concerns are not unfounded; the public cloud can expose sensitive data to potential breaches and compliance issues, leading organizations to seek alternative solutions.
To mitigate these risks, some enterprises are opting for private cloud or on-premises environments. This shift allows them to maintain greater control over their data and adhere to stringent privacy regulations.
Together AI: A Solution for Private Cloud Deployment
Among the vendors addressing these challenges is Together AI, which recently announced its Together Enterprise Platform. This platform is designed to facilitate the deployment of AI in virtual private cloud (VPC) and on-premises environments, providing enterprises with a cost-effective approach to harnessing AI technology.
Founded in 2023, Together AI aims to simplify the enterprise use of open-source large language models (LLMs). The company has already developed a full-stack platform that enables organizations to leverage open-source LLMs on its own cloud service. The new Together Enterprise Platform extends this capability, allowing businesses to deploy AI in environments they control, thereby addressing critical concerns around performance, cost-efficiency, and data privacy.
Cost Efficiency and Performance Gains
One of the standout features of the Together Enterprise Platform is its promise to help organizations manage and run AI models within their private cloud deployments. This adaptability is particularly beneficial for enterprises that have already made significant investments in their IT infrastructure.
Vipul Prakash, CEO of Together AI, emphasizes the importance of efficiency and cost in scaling AI workloads. He notes that enterprises are not only concerned about performance but also about data privacy and compliance with established policies. The Together Enterprise Platform aims to deliver on these fronts by significantly enhancing the performance of AI inference workloads.
Prakash explains, “We are often able to improve the performance of inference by two to three times and reduce the amount of hardware they’re using to do inference by 50%.” This translates to substantial savings and increased capacity for enterprises to innovate and expand their offerings. The performance improvements stem from a combination of optimized software and efficient hardware utilization, showcasing the platform’s ability to maximize resources.
Innovative Model Orchestration with Mixture of Agents
Another key feature of the Together Enterprise Platform is its flexible model orchestration capabilities. In today’s enterprise landscape, organizations often utilize a mix of different AI models, including open-source, custom, and third-party models. The Together platform facilitates the orchestration of these diverse models, allowing enterprises to scale them based on demand for specific features.
Prakash highlights the trend of enterprises using various models in tandem, stating, “What we’re seeing in enterprises is that they’re typically using a combination of different models.” The Together platform allows for seamless integration and scaling of these models, ensuring that organizations can respond effectively to changing needs.
Together AI employs a unique approach known as the Mixture of Agents. This method combines multi-model agentic AI with a trainable system for continuous improvement. In this framework, “weaker” models act as “proposers,” each generating responses to a given prompt. An “aggregator” model then synthesizes these responses to produce a more refined and accurate answer.
Prakash notes, “We are a computational and inference platform, and agentic AI workflows are very interesting to us.” This innovative approach positions Together AI as a forward-thinking player in the AI landscape, with plans to unveil more developments in the coming months.
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