Clairo AI provides a service to customers that is model agnostic. This means that Clairo’s AI platform is not built on a single model, but rather serves as an access point to many models. We firmly commit to staying up to date with the latest models to ensure that we have a future proof solution. Language models have been around for many years, but their creation and adoption have been accelerated since the 2017 when Google Brain published the paper Attention Is All You Need. This paper sparked many artificial intelligence companies to start funding research in the development of artificial general intelligence (AGI), most notably Google and OpenAI. In 2020, OpenAI released GPT-3 which was the first Large Language Model to make a significant contribution to businesses. Fast-forward to the end of 2021, when ChatGPT was released, it was obvious that LLMs are here to stay.
So, what different types of large language models are out there, and what characterises them?
There are two main distinctions of Large Language Models (LLMs)- private LLMs and public LLMs - both of which Clairo AI offers its users. A private LLM is one that is deployed and managed within a secure environment, allowing full control of where data flows and is stored. A public LLM is one that is accessed through an API via another company's server, which forces a business to forfeit the control of its data. It’s important that a business chooses their model type based on their data privacy strategy.
Open-source and closed-source AI models: what’s the difference?
The AI space is divided into two areas, open-source and closed-source models. Open-source and closed-source models represent two different approaches to the development and distribution of software, including Large Language Models.
Open-source models are characterised by their transparency and collaborative nature. The source code and weights of these models is made publicly available, allowing anyone to view, use, modify, and distribute it. This openness fosters a community-driven approach to development, where improvements and innovations can come from any user. Open-source LLMs can be fine-tuned for specific tasks, and their transparency allows for better understanding of the model's behaviour and decision-making process. However, they may require significant computational resources and technical expertise to use effectively.
On the other hand, closed-source models are proprietary software where the source code and models are not publicly available. These models are typically developed by a single organisation which maintains exclusive control over the models. Closed source LLMs, like ChatGPT, are often provided as a service, where users can interact with the model through an API or a user interface but cannot access or modify the underlying code. This approach allows for more control over the model's use and can provide a more user-friendly experience, but it lacks the transparency and customisability of open-source models.
What are the pros and cons of Private and Public LLMs?
When deciding whether to use a private LLM or a public LLM, a company must consider several key factors: data privacy needs, computational costs, technical capabilities, and the desire to stay abreast of industry advancements.
Firstly, the priority of data privacy is paramount. Private LLMs ensure that private data remains exactly that: private. If a company deals with sensitive data or has a stringent data strategy, a private LLM would be more suitable, as it offers full control over data flow and storage. Needing to adhere to privacy regulations and standards, private LLMs are hosted on secure and trusted infrastructure that protects company and user data from the risk of data breaches or unauthorised access.
However, this additional stringency comes with the cost of compute, which can be significant, and requires a tech team with the necessary skills to manage and maintain the model. Training and inference of large language models requires vast data processing and computational demands, leading to additional operational costs for a business. On the other hand, a public LLM, while more cost-effective and requiring less technical expertise, risks compromising data control as it operates on another company's server.
Lastly, the ability to keep up with the industry is a crucial factor. Public LLMs, often provided as a service by AI companies, are typically updated to keep pace with the latest advancements in the field. In contrast, private LLMs require the company's own resources to stay current, which can be challenging given the rapid pace of development in the AI space.
The decision between a private or a public large language model for your business is a hefty one and should be based on a balanced assessment.
Clairo AI guarantees privacy at a fraction of the cost
Clairo AI offers a unique middle ground solution that combines the benefits of both private and public approaches to LLMs, providing businesses with a flexible, secure, and efficient Generative AI platform. By prioritising data privacy and sovereignty, Clairo AI ensures that client data remains private and under the client's control at all times. To achieve this, Clairo AI provides two deployment options. Firstly, businesses can choose to deploy Clairo's AI platform on their net-zero servers located in Iceland, benefiting from the country's renewable energy resources and Clairo's commitment to sustainability. This option ensures that data is processed and stored in a secure, environmentally responsible manner, while also benefiting from the expertise of Clairo's AI specialists who can help businesses fully understand the risks and costs associated with a managed service.
Alternatively, for businesses with strict data residency requirements, or those who prefer to maintain full control over their data environment, Clairo AI offers the option to deploy the application in the company's own environment. This approach allows businesses to leverage the power of Clairo's Generative AI platform while ensuring that data never leaves their premises, providing an additional layer of security and control. In both scenarios, Clairo's commitment to privacy, data sovereignty, and sustainability remains unchanged, offering businesses a flexible and secure solution that meets their specific needs.
Furthermore, Clairo AI's open-source approach and fixed pricing model ensure economic efficiency, making it an attractive option for businesses looking to leverage the power of Generative AI without incurring unpredictable costs. By offering customized applications across various sectors and emphasizing a net-zero carbon footprint, Clairo AI sets a new standard in the digital transformation landscape, providing a unique blend of privacy, efficiency, emphasising a net-zero carbon footprint, and productivity.
