On Premise vs Cloud Based LLM: Which Is Right for Your Industry?

Every business wants to incorporate AI into its operations. Yet a huge number of enterprises are lagging in leveraging AI capabilities for their business.

The reason is not only the lack of skills but also the lack of clarity over how to embed the artificial intelligence capabilities. One primary question that arises here is - How should they deploy Large Language Models (LLMs)? Should they use agile cloud large language model services or look forward to completely local hosting with on-prem LLMs?

Well, the debate is crucial to address. The choice between on-premise vs cloud-based LLM deployment can significantly impact data control, compliance, cost, and scalability.

However, we are here to end this debate between on-prem vs cloud-based LLM deployment to provide you with advantages and attention to the pointers that need careful consideration.

Understanding the LLM Deployment

Before discussing how to deploy LLMs, it is essential to understand what they are. LLMs, or large language models, are trained on vast amounts of data. This training helps them understand and create human-like text.

What is On-Premise LLM Deployment?

On-premises LLM deployment refers to a more specialized LLMs that is directly integrated within your organization’s infrastructure. The primary benefit of on-prem LLM deployment is data sovereignty. The entire data processing is done within a controlled environment. So, businesses that keep data privacy as a top concern can highly benefit from this deployment model.

These models offer stringent security and compliance adherence. However, deploying LLMs locally requires robust on premise infrastructure. This includes dedicated servers, storage, and networking resources, which can impact both cost and maintenance requirements.

What is Cloud-Based LLM Deployment?

Cloud-based LLMs like OpenAI, Gemini, Anthropic, and Llama are openly accessible through API endpoints. They have already become a strong approach for organizations looking to integrate generative AI capabilities quickly. So, the cloud model offers quick implementation speed and lower initial resource investment.

Cloud models provide advanced capabilities with less infrastructure management. When connecting to cloud LLMs via APIs, companies can rapidly prototype and deploy AI solutions without the substantial infrastructure investments. So, cloud deployment significantly decreases time-to-market while allowing businesses to validate AI use cases quickly.

Comprehensive Comparison between On-Premise vs Cloud-Based LLM

The table below summarizes the critical factors organizations should evaluate when choosing between on-premise and cloud-based LLM deployments.

Now that we had a quick glance over the differences between both the deployment approaches, let us compare the parameters in detail.

1. On-Premise vs. Cloud LLM Security and Data Privacy

When it comes to sensitive data, where and how your model operates actually matters. This significantly affects the security of your data. In fact, a Deloitte survey found that 55% of companies avoid some AI applications due to concerns about data security.

Also, an IBM survey showed that 57% of businesses see data privacy as the biggest barrier to using AI. That is why the security of LLM deployment model is of utmost importance.

Using an on-premises LLM deployment model provides the best security and data privacy. In this model, all data is kept within your organization’s local environment.

Since there is no data sent outside, the risk of breaches or leaks from third parties is lower. In fact, 52% of companies are trying to reduce their reliance on U.S.-based cloud providers because of concerns about data sovereignty.

Source

Using cloud-based services to deploy LLMs means you rely on outside companies to store and manage your data. Although leading cloud providers have strong security certifications, like ISO 27001. But still your data still travels over the internet and is stored on shared servers. This raises concerns about

• Unauthorized access

• Compliance with laws

• Potential risks on the vendor’s side

So, organizations should carefully evaluate security protocols and regulatory requirements before adopting cloud-based LLMs.

Organizations dealing with highly sensitive data may prefer on-premise deployments.

On-Premise vs Cloud LLM Cost Comparison

The financial impact of using LLMs depends on how you scale and operate.

On-premise deployment requires a large investment in hardware, software, and infrastructure. Businesses face high upfront costs for servers, GPUs, storage, cooling systems, and hiring IT staff. This model uses the capital expense (CAPEX) approach. However, if you use LLMs often and have predictable needs, on-premises setups can be cost-efficient over time.

Cloud deployment uses an operational expense (OPEX) model. You don’t have to make huge investments. Instead, you pay only for the resources you use. Cloud solutions can offer lower costs through flexible pricing.

When choosing between on-premises and cloud deployment for Generative AI models, it’s important to understand the costs involved. Each option has different financial effects that can greatly influence a company’s budget.

LLM Performance and Scalability: Cloud vs On-Prem

How well the model works and how quickly it can grow to meet demand are important for using it in production. This comes as a significant parameter for comparison.

On-premises ensures consistent and predictable performance because resources are dedicated solely to your applications. However, scaling in this model deployment is manual. So, adding capacity means

• Buying new hardware

• Setting it up

• And integrating it into your system

which can take days or weeks.

Cloud-based model offers instant scalability. It allows you to scale up or down based on demand. This is ideal for businesses with fluctuating workloads or those running pilot LLM projects. Modern cloud deployment systems have demonstrated up to 2.1x higher throughput compared to on-prem deployments at similar price points. This highlights the performance flexibility cloud environments can offer.

However, it is essential to have a strong internet connectivity to maintain consistent performance in cloud LLM deployments.

Organizations with fluctuating workloads may benefit from cloud-based solutions more.

Control & Customization in On Prem and Cloud Deployment

LLM deployments often need to be tailored to specific business needs, requiring different levels of control.

On-Premises provides full control over every layer, from the model itself to the servers it runs on. On-premise models can be continuously fine tuned to maintain optimal performance and relevance. This level of customization can provide large enterprises with a significant competitive advantage in their industry. This is ideal for enterprises needing integration with existing systems.

Cloud infrastructure offers limited control over the infrastructure. While some platforms allow for fine-tuning LLM or API-level customization, you are essentially operating within the boundaries set by the cloud vendor. This could become restrictive for businesses with highly specialized LLM needs.

LLM Deployment Latency

For real-time applications or environments where speed matters, latency is an essential factor to consider.

On-Premises infrastructure delivers low latency. This is because it processes data locally, without depending on outside networks. This is crucial in use cases like manufacturing, agentic AI systems, or health diagnostics, where even milliseconds of delay can affect outcomes.

Cloud-based involves network round-trips to and from remote servers. While some vendors offer regional deployments to reduce delay, average latency for cloud LLM inference ranges from 1.4 to 1.8 seconds per request.

On-premise deployments require specialized IT staff. While cloud deployments offer managed services. 

Technical Expertise in On-Prem and Cloud Deployments

Cloud-based implementations typically require less specialized AI expertise than on-premises deployments.

Maintenance and Upgrades in On Prem vs Cloud

Maintaining and upgrading LLMs is a critical aspect of ensuring long-term performance. The approach you take to maintenance and upgrades can have a significant impact on your organization’s ability.

For cloud based LLMs, the maintenance and updgrades are handled by the cloud providers. This frees up your internal IT teams to focus on higher-value tasks.

However, it’s important to account for ongoing costs beyond initial deployment. On average, monthly maintenance and usage costs for cloud-based deployments can range from $500 to $10,000. This could vary depending on usage patterns and the scale of your AI projects. Fluctuating demand can also impact costs, as variable token consumption and API call fees may lead to unpredictable expenses.

On the other hand, on premise LLM deployments require continuous maintenance. This brings responsibility for internal IT teams to

• Monitor system health

• Apply updates

• Refresh hardware

to keep pace with evolving AI models. Also, on premise solutions demand regular retraining and fine tuning of models with new datasets to ensure relevance and accuracy. While this approach offers robust security measures, it also introduces greater complexity and higher long-term maintenance costs.

A Hybrid Approach to Deploying LLMs

The combination of on-prem and cloud deployment brings the best of both worlds. A quick understanding of the hybrid approach is-

• Business develops and prototypes LLMs on-premise solutions.

• Then deploys them to the cloud for wider access and scaling purposes.

Hybrid LLM deployment can also incorporate private cloud environments. This can provide additional security, control, and customization for enterprise AI deployments.

For example, a company could fine-tune a model on-prem with proprietary data, but deploy the resulting model to a cloud service to handle customer queries at scale.

Best-Suitable LLM Deployment Models for Specific Industries

Many organizations across different industries are evaluating on-premise, cloud, and hybrid LLM deployment models to meet their unique needs.

When selecting the best fit for a specific application, it is crucial to take industry-specific considerations into account. Let us check the feasible choices as per the particular industry.

Healthcare

Healthcare data is very sensitive. On-premise or hybrid systems are often the ideal choices for following rules like HIPAA. Some healthcare organizations are already using Google Cloud to deploy LLMs for enhanced patient care and streamlined operations. In healthcare, on-premise LLMs can analyze patient data and comply with HIPAA, GDPR, and other regulations.

Finance

Financial institutions handle sensitive data and must follow rules like GDPR and CCPA. Using on-premise or hybrid models can help keep this data secure and meet these regulations. AI in FinTech can assist with fraud detection, risk analysis, and following rules. By storing sensitive financial data in-house, these institutions can reduce the risks linked to cloud storage.

Government

Government agencies must follow strict security and compliance rules. On-premise or hybrid setups are often the best choices for controlling sensitive data and meeting legal requirements in government agencies.

Retail

Retailers can benefit from cloud LLMs for personalized recommendations and enhanced customer service. However, they need to address data privacy concerns related to customer information.

Many retailers deploy LLMs using cloud services to enhance customer experience, but must also address potential drawbacks such as data privacy and performance issues.

Manufacturing

Manufacturing companies can keep strong control over their sensitive data by using on-premise setups. Cloud solutions, on the other hand, provide flexibility and scalability for certain applications. Many manufacturing companies are now using open-source models to run local LLMs. This approach improves privacy and allows for more customization.

Technology

Technology companies can benefit from cloud solutions. These can help them adapt and grow quickly as their needs change. On-premise options give companies more control over their own code and data.

So, the technology leaders must carefully assess the trade-offs between cloud and on-premise LLM solutions. This is to ensure their choices align with their organization's strategic goals.

Factors to Consider When Choosing an LLM Deployment Approach for your Business

When evaluating investments in AI, it’s important to consider the same factors and return on investment as you would for other IT projects. Organizations launching new AI projects should carefully consider which deployment model best aligns with their goals and resources. We will look at some of the key considerations when opting between on-premises versus cloud solutions:

• Ethical considerations

• Cost Accountancy

• Performance measurement

• Accuracy

• Resource management

• Task expertise

• Bias mitigation

• Inference speed

• Integration and deployment

• Latency requirements

How to Choose Between On-prem vs Cloud LLM?

While choosing between local or cloud LLMs, there are some questions you must consider.

Do you have in-house expertise?

Running LLMs on your own requires a lot of technical know-how in machine learning and managing IT systems. This can be complicated for organizations that do not have a strong technical team. However, using cloud-based LLMs makes things easier. The cloud provider handles much of the technical work, like maintenance and updates. This makes it more convenient for businesses without a specialized internal IT team.

Do you have budget constraints?

Deploying local LLMs can be expensive initially. Businesses need strong computers, especially GPUs, which can be challenging for startups. In contrast, cloud LLMs usually have lower initial costs. They often charge based on usage, similar to subscription services.

What are your data size & computational needs?

For businesses that regularly need a lot of computing power and have the right setup, local LLMs can be a more reliable choice. In contrast, cloud platforms provide the flexibility to scale up resources. This is particularly helpful for businesses with changing demands.

What are your risk management assets?

Local language models enable organizations to maintain better control over data security. This makes them a good choice for handling sensitive information, like FinTech. However, they need strong internal security measures.

Cloud LLMs may carry higher risks because data travels over the internet. But these services are usually managed by providers who invest a lot in security.

However, the right deployment model depends on your unique business requirements.

• Choose On-Premises if security, control, and long-term cost-efficiency matter more than flexibility.

• Choose Cloud-Based if speed, scalability, and ease of deployment are top priorities. And most importantly, data sensitivity is manageable.

• A hybrid deployment approach may offer the best of both models. So, businesses can opt for on-prem for core processing & cloud for experimental or large-scale tasks.

Bottom Line

There’s no one-size-fits-all is the answer in the on-prem vs cloud-based LLM debate. The right choice depends on unique business requirements.

For startups and businesses with changing needs or no MLOps team, using the cloud offers speed and convenience. For large companies with sensitive data, steady workloads, and technical skills, on-premise deployment can be more secure and cost-effective. However, most organizations will likely benefit from hybrid setups that strike a balance between flexibility and control.

We understand the cruciality of choosing the right LLM deployment model for your business. That is why we offer comprehensive LLM development services to handle every aspect with utmost care. Get in touch today to discuss your unique business requirements.