This guide helps enterprises choose between on-premise and cloud deployment models for AI and LLMs. It analyzes key factors like security, availability, customization, and costs, highlighting the pros and cons of each option. On-premise solutions offer higher control, security, and cost advantages at scale, while cloud solutions provide faster startup and lower initial investment. Leaders must align deployment decisions with business needs, data sensitivity, and operational goals to maximize AI’s value and long term ROI.
AI has established itself as a major driver of innovation and operational efficiency across industries. In fact, IDC reports that AI infrastructure spend reached $47.4B in 2024, 97% year-over-year increase. AI infrastructure investment is expected to surpass $200B by 2028, with the US accounting for 59% of this (IDC report). While traditionally the technology industry has led the way, oil & gas/energy, manufacturing and logistics have shown that generative AI, LLMs, and agentic platforms have wide applicability beyond hi-tech. However, as businesses look to integrate AI-powered solutions into their operations, they must be careful to select the right deployment and infrastructure model. Deploying AI and LLMs on-premise (on-prem), including in a (virtual) private cloud (typical of PAAS offerings such as Microsoft Azure), or in a multi-tenant public cloud (typical for SAAS vendor offerings) are both options that offer benefits and trade-offs. Enterprise technology and innovation leaders must be aware not only of their business needs but also of what each paradigm entails in terms of costs, security, scalability and control.
In this guide, we share learnings from Allganize’s more than 280 enterprise customers and 1000+ AI projects across energy, manufacturing, finance and insurance, AEC and hi-tech to provide a detailed overview and analysis of on-prem and cloud LLM and AI hosting, with focus on the benefits and challenges, especially as relevant to enterprise-size data and systems. Our objective is to equip technology and innovation leaders with the information they need to evaluate these options effectively and understand the impact they can have on their own enterprise and cost structure.
The factors are similar to other IT projects and similar ROI considerations should also be made when evaluating AI investment opportunities. The key dimensions we will use to evaluate on-prem versus cloud are:
Security is a key decision driver, especially for enterprises where data and IP protection is of strategic importance. Security includes both the security of the data at rest or in transit and of the infrastructure. Security will also include protection of sensitive data from being used to train public LLMs, potentially leaking it into the public domain. Indeed, a Deloitte survey shows that 55% of enterprises avoid at least some AI use cases due to data security concerns (source). Agreeing with this finding, IBM industry survey finds that 57% cite data privacy as the biggest inhibitor to AI adoption (source). However, security does not need to be an obstacle. In fact, innovative vendors are launching new products that enable ever more powerful AI capabilities, including the latest MCP-based agent builders for enterprises focused on security.
Availability and responsiveness are key metrics that enable AI to deliver value to the enterprise consistently and at critical times. Latency and uptime are the two components we will evaluate in this area.
Large enterprises often look for the highest value and largest potential impact in any IT and innovation initiative. Oftentimes, such innovation projects are considered not just from operational excellence and cost reduction point of view but also they are seen as potential sources of competitive advantage. In this sense, the ability to tailor the AI solution to the specifics of the industry, enterprise and teams, the accuracy of responses and automation and the time to deliver the project with such high requirements are key considerations.
Cost, along with security, is the most commonly discussed factor when it comes to AI. To provide a complete picture here, we will evaluate the costs of infrastructure required to host and deploy the solution, the cost of development required to build a system that meets the business objectives and project charter, the costs of training the AI itself - whether training and fine-tuning an LLM or a RAG, the costs of using the system at scale and the cost of supporting the system so as to maintain accuracy and value over time.
An on-prem (or private cloud) LLM or AI solution is hosted within infrastructure controlled directly by the organization - owned (i.e. servers, GPUs, CPUs, etc.) or leased IAAS (virtual private cloud in AWS, Azure or similar), so not exclusively on premise. This setup provides ownership and control over the hardware, processing power, system configurations and, of course, the data itself. This setup is an attractive option for industries with strict regulatory requirements, concerns about IP protection, or with large amounts of data.
Since the data remains entirely within the company’s control, on-prem deployments provide for the lowest risk level associated with third-party breaches when compared with private cloud or multi-tenant options.
A related security advantage is the ability of on-prem deployments to protect IP from being used to train public LLMs. Since an on-prem model will be used, no data crosses over into the public domain, protecting the enterprise’s sensitive data.
Even beyond the ability of generative AI’s retrieval augmented generation (RAG) to use company’s internal data for accurate answers and automation, deploying an on-prem LLM by definition tailors the model to the language and use cases of the business. Such Small Large Language Models (sLLM’s), fine-tuned with proprietary datasets and internal AI strategies and combined with a purpose-deployed AI platform, often generate highest accuracy and ability to support generative AI and agentic automation for an enterprise.
Since there is no dependence on internet connectivity or availability of external servers, on-prem deployments of AI solutions, especially when sized for appropriate availability and fail-over capabilities, ensure lowest latency and are uniquely suited for critical real-time applications.
It may be surprising to talk about cost advantages when it comes to on-premise LLM and AI. While the initial investment in hardware and software is high, there are ways to minimize the impact and draw strong benefits. However, there are particular business situations where on-prem can provide strong cost savings versus cloud. This is further reinforced through tax incentives.
There are three factors that we need to consider when it comes to cost:
The cost of developing your own AI platform and LLM remains high – even tools like MS Azure’s AI Foundry require highly skilled developer resources and time to design, implement, test and deploy solutions. That said, commercial or open source AI platforms and LLMs that can be deployed on-prem minimize the cost of developing solutions and training LLMs and the associated maintenance. Yet, for in-house custom sLLMs, especially, the follow-on costs of retraining are not to be ignored when compared to the costs of RAG.
To understand the potential risk and cost of developing/training your own LLM, Gartner research report shows that the upfront cost of enterprises building and training custom LLMs is in the neighborhood of ~$8M to $20M. The cost of building and deploying a system with RAG, even if custom, reduces this cost by more than 95%. For the detailed report see Gartner’s findings here (source).
The higher up-front cost of on-prem solutions can often be capitalized and depreciated, leading to tax benefits and often savings over time. Such an option is not available with pay-as-you-go models like private cloud IAAS/PAAS or multi-tenant SAAS.
Again this cost component needs to be broken down into two subcategories - training cost and regular usage cost.
In summary, the cost part of the consideration, the ability to capitalize an on-prem AI system, amortize the cost over time and depreciate the asset, combined with the cost of up-front training of LLM or retriever over enterprise-size data, often makes on-prem cost-effective option at scale versus private cloud IAAS/PAAS or even multi-tenant SAAS.
We have already covered some of the disadvantages in our discussion so far.
A way to mitigate the above challenges for organizations looking to deploy on-prem is selecting a partner who can provide a standard product, configuration services and support. Standard products can significantly reduce costs as they avoid the resource requirements, time, and risks associated with internal build options.
The vast majority of AI and LLM vendors deploy their systems in their proprietary multi-tenant cloud environments. In such an architecture, multiple customers share the same hardware and software resources. Furthermore, the vendor is responsible for basic maintenance, redundancy, scalability and other common needs. Such a model has a number of advantages that make it particularly appealing for companies looking to quickly get up and running and that do not have the scale to worry about abnormal costs.
Cloud AI solutions often offer advantages that make them ideal for experimentation, learning, and proof of concepts:
While such solutions are often made with general workflows and assumptions in place, if your business follows these processes and fits the mold of the solution, you get strong value and a positive user experience.
Out of the box LLMs and capabilities make such standard solutions easy to get started with and roll out to your team. Furthermore, often you face a shorter learning curve as standard products tend to follow common UI patterns that users are already familiar with.
In the event where you do not need to train your own LLM or do any development, the quick rollout of standard functionality provides excellent savings over a heavily customized solution. You eliminate the time consuming and costly development, training, testing, and deployment costs.
While there are definite advantages to cloud solutions, there are a number of potential drawbacks that should be considered and balanced out against the benefits, especially when considering the initiative business objectives.
While multi-tenant cloud options are often secure, implementation shortfalls may open sensitive data to exposure. This is especially true for smaller, less established vendors where speed to market may compromise the quality of the product and security. Businesses considering such vendors should look for security certifications like SOC2, HIPAA, ISO27001.
A lot of companies with strong IP and proprietary data are hesitant to share this information with cloud vendors as it can be used to train LLMs and potentially be leaked into the public space. Efforts by leading LLM vendors like OpenAI and Google to classify training LLMs on proprietary data as “fair use”.
Since a lot of these systems are standardized and out of the box, the ability to tailor to your specific needs and business are often limited. Businesses should always try the system before scaling to enterprise scale.
Depending on the infrastructure the cloud vendor uses, this may be an issue, especially as the usage of the system scales up.
While the initial startup costs for standard functionality is low, AI cloud deployments can be more expensive in the long term due to a number of factors. Two major costs can scale to a level that tips the decision towards on-prem options. In fact, Deloitte finds that at scale AI API call fees are the reason for public cloud spending exceeding budgets by 15% and for 27% of the public cloud costs considered “wasted spend” (source).
At scale, AI transactions can be expensive. Cloud, SAAS deployments are treated as an operational expense. As such, over time, they tend to be more expensive, especially with heavy usage and automation.
Leveraging Retrieval Augmented Generation (RAG) is always more cost-effective than training and maintaining your own LLM. That said, training the RAG initially on large amounts of data can itself get expensive. Make sure to understand the costs associated with parsing/embedding your documents into the retriever and how this impacts startup costs.
When considering the above options, we can put the following table as an easy guide to evaluate the advantages and disadvantages of cloud and on-prem (virtual private cloud) options based on specific requirements and priorities:
Generative AI, LLMs and agents have become critical enablers of operational efficiency and success for businesses from every industry. Innovation and technology leaders must consider the needs of their businesses and of the target use cases when deciding between on-premise or cloud LLM and AI options. Your specific priorities should drive the decision:
While public cloud investments are still dominating, enterprises with the right mix of needs, maturity and scale can benefit from on-prem options. Advances in technology that reduce the up front cost and risk for new AI projects, in combination with the benefits of on-prem (private cloud) solutions such as cost savings at scale, improved efficiency, scalability, rapid innovation and data and IP protection are driving an expansion of private, on-prem deployments by enterprises worldwide, as outlined in Deloitte’s 2025 Technology Industry Outlook report (source).
By carefully evaluating factors such as cost, customization options, scalability and security, businesses can make informed decisions that align their business objectives, operational requirements with the AI options they are considering.
Still not sure which way to go? At Allganize we are veterans of more than 1000 generative AI and agentic on-prem and cloud implementations with our 280+ enterprise customers across Oil & Gas and Energy, Manufacturing, Logistics, AEC, Finance, Insurance and Hi-Tech. If you are looking to learn more or talk to one of our AI experts, you can contact us directly.
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