Cloud LLMs: Harnessing scalable AI without the hardware hassle

Large language models (LLMs) can understand and generate human-like text, making them incredibly useful for various applications, from chatbots to content creation. Their deployment on the cloud significantly enhances their accessibility and efficiency, making it easier to leverage their capabilities.

A cloud-based large language model works in a cloud environment, so investing in expensive hardware and infrastructure to host and operate is unnecessary. Businesses can utilize the powerful computing resources provided by the cloud providers without purchasing and maintaining their own data centers. In this article, we’ll explore cloud-based LLMs, how they work, and why they matter.

What are cloud LLMs?

A large language model is an artificial intelligence (AI) model designed to understand and generate text. These models are typically built using deep learning techniques and trained on massive data. Training and running LLMs require substantial computational resources, which can be costly and environmentally impactful. Due to budget constraints, many enterprises need help to afford to deploy and fine-tune LLM models.

Cloud computing involves delivering computing services—servers, storage, databases, networking, software, and more—online (in the cloud). It has revolutionized how we use and interact with technology, and LLMs are no exception. Major cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform offer the infrastructure and tools necessary to run large-scale applications without needing physical hardware on-premises.

By deploying LLMs on cloud platforms, companies can leverage the immense computational power needed to train and run these models without investing in expensive hardware. Users can access these models via APIs, making it easy to integrate advanced language processing capabilities into their applications with minimal effort.

LLM as a service

Large companies tend to run most of their applications locally, meaning the systems and databases they use reside on their own servers. According to the McKinsey report, cloud services most often take up only 15% to 20% of the IT infrastructure, even though reliable cloud services have been around for a long time and offer a wide range of business benefits. The computational power and expertise required to deploy and manage large language models can be a significant barrier for enterprises. In other words, LLM as a service could be the factor that accelerates cloud adoption by businesses. It refers to cloud-based platforms that offer access to LLMs via APIs. 

These services allow developers and businesses to integrate the capabilities of sophisticated language models into their applications without managing the whole AI infrastructure. Moreover, companies can pay only for what they use, avoiding the upfront costs of purchasing and maintaining high-performance hardware.

Users do not need to worry about the complexities of maintaining the language models. Cloud-based LLMs enable non-technical specialists to benefit from advanced machine learning capabilities without deep technical knowledge.

These platforms offer user-friendly interfaces, APIs, and pre-trained models that simplify the integration of sophisticated natural language processing (NLP) capabilities into various applications. For instance, businesses can now automate customer support, generate content, analyze sentiment, and more with minimal technical intervention.

What are local LLMs?

Local large language models refer to large-scale language models that are deployed and run on local hardware, such as on-premises data centers, that do not rely on cloud-based infrastructure. They are usually more suitable for companies with AI research labs and teams with high ML expertise.

When deploying large language models locally, specialists have more control over the model, which makes more precise customization possible. This can include fine-tuning the model on specific datasets to improve performance for particular tasks or industries. Although the set-up costs may be higher initially due to the need for powerful hardware and the costs of expertise, on-premises LLMs are potentially more cost-effective in the long run due to the absence of ongoing fees for cloud platforms.

However, scaling up local LLMs to handle larger volumes of data or more complex tasks can be challenging compared to scalable cloud solutions. More than that, technical expertise and regular maintenance are required to keep the LLM models updated and make them run efficiently.

Pros of using LLMs in the cloud

Cost Efficiency. The pay-as-you-go pricing model of cloud services allows for cost management based on actual usage. This reduces the need for substantial upfront investment in hardware and can be more economical, especially for projects with variable computational needs;

Scalability. Cloud-based LLMs can handle varying workloads, from small projects to enterprise-level applications, by efficiently scaling resources up or down based on demand. This flexibility ensures that you can manage peak loads without investing in additional hardware;

Accessibility. Cloud LLMs are accessible from anywhere with an internet connection. This makes it easier for distributed teams to collaborate and for applications to be deployed globally without geographical constraints;

Maintenance-Free. The cloud service provider maintains the infrastructure, including updates, patches, and hardware maintenance. This reduces the burden on internal IT resources and ensures that the models always run on the latest and most secure platforms.

Cons of using LLMs in the cloud

Data Privacy and Security Concerns. Sensitive data needs to be transmitted to and from the cloud, which can pose risks of data breaches and raise concerns about compliance with data protection regulations;

Speed Issues. Depending on the network speed and location of the cloud servers, there can be latency in processing requests. This can be a challenge for applications that need real-time responses;

Ongoing Costs. While the pay-as-you-go model can be cost-efficient, it can also lead to significant ongoing costs, especially for high-volume usage. Over time, these costs may accumulate and potentially exceed the cost of running LLMs locally;

Dependency on Internet Connectivity. Cloud-based solutions require a stable internet connection. Any disruption in connectivity can affect access to the LLMs and disrupt services;

Limited Customization. While cloud providers offer some customization, there may be limitations compared to what can be achieved with local deployments. This can be a drawback for specialized applications requiring deep integration or specific optimizations;

Local vs. Cloud LLMs

Local LLMs offer significant advantages in terms of data privacy and security,  running models locally ensures that sensitive data remains within the internal infrastructure. Unlike cloud services, deploying the LLMs locally reduces the risk of data breaches and helps to comply with strict data protection regulations. In addition, local models reduce the latency associated with sending data to and receiving data from remote servers, thus making them ideal for applications that require quick response.

However, local LLMs have several drawbacks. The initial setup costs can be significant, requiring investment in hardware and infrastructure, such as powerful GPUs and storage solutions. They also necessitate ongoing maintenance, updates, and potentially additional staffing or expertise to manage the infrastructure and software. Moreover, scaling up may involve additional hardware purchases and setup, which can be less flexible compared to cloud solutions.

On the other hand, cloud LLMs offer their own set of advantages. They are easily scalable to handle varying workloads, from small-scale to enterprise-level applications, and can quickly adjust resources as needed. This scalability is coupled with lower initial costs since there’s no need to purchase expensive hardware, and pay-as-you-go pricing models allow for cost management based on usage.

Cloud LLMs are also maintenance-free, as the service provider handles model updates, infrastructure maintenance, and performance tuning, freeing internal resources. Additionally, cloud-based models are accessible from anywhere with an internet connection, facilitating remote work and collaboration.

Despite these benefits, cloud LLMs have their disadvantages. There are data privacy and security concerns, as sensitive data must be transmitted to and from the cloud. This can raise concerns about data breaches and compliance with data protection laws. Potential latency due to network delays can also be problematic for real-time applications. Lastly, ongoing operational costs, based on usage, can accumulate over time and potentially exceed the cost of local infrastructure.

Cloud or local LLM hosting: what to choose?

The emergence of cloud-based large language models is a groundbreaking development that significantly lowers the barriers to utilizing advanced machine learning models. Cloud platforms make LLMs accessible to many enterprises, including those with limited technical expertise. A cloud provider takes care of updates, support, and security, reducing the burden on your team. However, cloud solutions have drawbacks. Transmitting sensitive data over the internet can be a security risk, and delays can occur due to network and data transfer speed.

Locally hosted LLMs also have their advantages. The most important of these is that data is processed locally, which reduces the risk of data breaches. The models perform faster since there is no need to transfer data online, the models perform at faster speeds. Being locally hosted provides more opportunities to optimize models for specific tasks. But this approach also has its drawbacks. It requires significant amount of computing resources, such as powerful GPUs and RAM. Hardware and software require regular upgrades and maintenance. If additional capacity is needed, it will demand extra investment in hardware.

Consequently, the choice between cloud and on-premises hosting of large language models depends on factors such as your business's needs and available resources. Cloud hosting is suitable for companies that are looking for scalability and no major upfront costs, while on-premises hosting is better for organizations with high-security requirements and those willing to invest in powerful hardware and technical support. Evaluate your priorities and resources to make the most appropriate choice for your company.