How to Choose the Best Deployment Model for Enterprise AI: Cloud vs On-Prem

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Choosing the right deployment model for Artificial Intelligence (AI) and Large Language Models (LLMs) is a critical decision for any enterprise. This guide helps you navigate between on-premise and cloud deployment models for AI and LLMs, analyzing key factors like security, availability, customization, and costs.

While on-premise solutions offer higher control, security, and cost advantages at scale, cloud solutions provide faster startup and lower initial investment. Leaders must align these deployment models with business needs, data sensitivity, and operational goals to maximize AI’s value and long term ROI.

1. The AI Investment Boom: Why Deployment Choices Matter

AI has firmly established itself as a major driver of innovation and operational efficiency across industries. The investment figures tell a compelling story:

• AI infrastructure spend reached $47.4 billion in 2024, a 97% year-over-year increase (IDC report).
• AI infrastructure investment is expected to surpass $200 billion by 2028 (IDC report).
• The US alone will account for 59% of this spending (IDC report).

Traditionally, the technology industry led the way. However, sectors like oil & gas/energy, manufacturing, and logistics now show that generative AI, LLMs, and agentic platforms have wide applicability beyond hi-tech.

As businesses look to integrate AI for enterprises into their operations, selecting the right deployment model and infrastructure is crucial. Both on-premise (on-prem) and cloud deployment models (including virtual private clouds or multi tenant public clouds) offer unique benefits and trade-offs. Enterprise technology and innovation leaders must understand what each paradigm entails in terms of costs, security, scalability, and control.

Our Insights from Experience:At Allganize, we share learnings from our more than 280 enterprise customers and 1000+ AI projects across energy, manufacturing, finance and insurance, AEC, and hi-tech. This guide provides a detailed overview and analysis of on-prem and cloud LLM and AI hosting, focusing on 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 deployment models effectively and understand their impact on enterprise cost structure and ROI.

2. Key Factors: What to Consider for AI Deployment

Choosing between on-premise and cloud based LLMs and AI systems involves evaluating several critical factors. These dimensions are similar to other IT projects, and analogous ROI considerations should guide your AI investment decisions.

Let's break down the key dimensions:

2.1. Security: Protecting Your Data and IP

Security is a paramount decision driver, especially for enterprises where data and IP protection is of strategic importance. This factor covers:

• The security of data at rest or in transit.
• The security of the underlying infrastructure.
• Crucially, protection of sensitive data from being used to train public LLMs, which could potentially leak into the public domain.

Industry Concerns:

• A Deloitte survey shows that 55% of enterprises avoid at least some AI use cases due to data security concerns.
• An IBM industry survey finds that 57% cite data privacy as the biggest inhibitor to AI adoption.

The Good News: Security does not need to be an obstacle! Innovative vendors are launching new products that enable powerful AI capabilities, including the latest MCP based agent builders for enterprises focused specifically on security.

2.2. Availability: Keeping AI On-Demand

Availability and responsiveness are key metrics that enable AI to deliver value to the enterprise consistently and at critical times. We evaluate this area based on two components:

• Latency: The time it takes for a system to respond.
• Uptime: The percentage of time a system is operational and accessible.

2.3. Customizability: Tailoring AI for Competitive Advantage

Large enterprises often seek the highest value and largest potential impact from any IT and innovation initiative. Such projects are often considered not just for operational excellence and cost reduction, but as potential sources of competitive advantage. In this sense, key considerations include:

• The ability to tailor the AI solution to the specifics of the industry, enterprise, and teams.
• The accuracy of responses and automation.
• The time to deliver the project with such high requirements.

2.4. Costs: Balancing Initial Investment with Long Term Value

Cost, along with security, is the most commonly discussed factor when it comes to AI for enterprises. To provide a complete picture here, we evaluate:

• Costs of infrastructure required to host and deploy the solution.
• Cost of development required to build a system that meets business objectives.
• Costs of training the AI itself (whether training and fine tuning an LLM or a RAG).
• Costs of using the system at scale.
• Cost of supporting the system to maintain accuracy and value over time.

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3. Option 1: On-Prem AI and LLMs

An on-premise (on-prem) or private cloud LLM or AI solution is hosted within infrastructure controlled directly by the organization (owned hardware or leased IaaS like virtual private cloud in AWS, Azure). This setup provides ultimate ownership and control over hardware, processing power, system configurations, and, crucially, the data itself. This makes on-premise deployment an attractive option for industries with:

• Strict regulatory requirements.
• Concerns about IP protection.
• Large amounts of highly sensitive data.

3.1 Advantages of On-Prem Deployments

On-premise deployment models offer distinct benefits for AI for enterprises:

• Highest Security & IP Protection:
  • Data remains entirely within the company’s control, providing the lowest risk level associated with third party breaches.
  • No data crosses over into the public domain, preventing sensitive enterprise data from being used to train public LLMs.
• Customizability Advantages:
  • Beyond generative AI’s Retrieval Augmented Generation (RAG) using internal data, deploying an on-prem LLM inherently tailors the model to the business's specific language and use cases.
  • Small Large Language Models (sLLMs), fine tuned with proprietary datasets and internal AI strategies, combined with a purpose deployed AI platform, often generate the highest accuracy and ability to support generative AI and agentic automation for an enterprise.
• Performance Advantages:
  • No dependence on internet connectivity or external servers.
  • Ensure lowest latency and are uniquely suited for critical real time applications, especially when sized for appropriate availability and fail over capabilities.
• Cost Advantages (at scale): It may seem surprising, but on-premise LLM and AI can provide strong cost savings versus cloud in specific business situations, especially at scale. This is further reinforced through tax incentives.
  • Developer Costs for LLM/Agent Development, Training, Maintenance: While developing your own AI platform is high, commercial or open source AI platforms that can be deployed on-prem minimize development and training costs. Gartner reports that the upfront cost of building custom LLMs can be ~$8M to $20M, but RAG based systems reduce this by over 95%.
  • Hardware and License Capitalization/Depreciation: The higher upfront cost of on-premise solutions can often be capitalized and depreciated, leading to tax benefits and long term savings. This option is not available with pay as you go cloud models.
  • Variable Transactional Costs:
    • LLM/AI Training Costs: For large enterprises deploying on terabytes of data or more, on-premise is often more advantageous than pay per use cloud models.
    • Regular Usage Transactional Costs: On-premise deployments of LLM and AI tend to be more cost effective for large enterprises with a high number of users and significant transaction volumes.

In summary, the ability to capitalize an on-prem AI system, amortize cost over time, and depreciate the asset, combined with the cost of upfront training of LLM or retriever over enterprise size data, often makes on-premise a cost effective option at scale versus cloud.

3.2 Disadvantages of On-Prem Deployments

Despite its advantages, an on-premise approach comes with drawbacks:

• Requires substantial capital expenditure for hardware, software, and skilled personnel, especially if you train your own LLM or build agents in house.
• Scaling operations may be more complex and time consuming, requiring additional infrastructure investments.
• Maintenance responsibilities (software updates, security, infrastructure management, redundancy measures) fall squarely on the organization.

Mitigation: A way to mitigate these challenges is by selecting a partner who can provide a standard product, configuration services, and support for your on-premise deployment. Standard products can significantly reduce costs, time, and risks compared to internal builds.

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The vast majority of AI and LLM vendors deploy their systems in multi tenant cloud environments. In such an architecture, multiple customers share the same hardware and software resources. The vendor is responsible for basic maintenance, redundancy, scalability, and other common needs. This model offers advantages appealing to companies looking to quickly get up and running, especially those without the scale to worry about abnormal costs.

4.1 Advantages of Cloud Deployments

Cloud AI solutions often offer advantages making them ideal for experimentation, learning, and proof of concepts:

• Strength in Target Area: If your business processes align with the solution's general workflows, you get strong value and a positive user experience out of the box.
• Fast Time to Value: Standard, out of the box LLMs and capabilities are easy to get started with and roll out. Often, you face a shorter learning curve as standard products tend to follow familiar UI patterns.
• Low Startup Costs: If you do not need to train your own LLM or do any custom development, the quick rollout of standard functionality provides excellent savings over a heavily customized solution. You eliminate time consuming and costly development, training, testing, and deployment costs.

4.2 Disadvantages of Cloud Deployments

While there are definite advantages to cloud solutions, several potential drawbacks should be considered and balanced against your initiative's business objectives.

• Reduced Security and Control:
  • While multi tenant cloud options are often secure, implementation shortfalls may expose sensitive data. Look for security certifications like SOC2, HIPAA, ISO27001 when considering vendors.
  • Exposure of IP to Commercial LLMs: Many companies with strong IP and proprietary data hesitate to share this information with cloud vendors, fearing it could be used to train LLMs and potentially leak into the public space. This concern is heightened by efforts from leading LLM vendors to classify training LLMs on proprietary data as “fair use.”
• Limited Customization and Accuracy: Since many of these systems are standardized and out of the box, the ability to tailor them to your specific needs and business is often limited. Businesses should always test the system thoroughly before scaling to enterprise level.
• Latency Issues: Depending on the cloud vendor's infrastructure, latency may become an issue, especially as system usage scales up.
• Higher Long Term Costs: While initial startup costs for standard functionality are low, AI cloud deployments can be more expensive in the long term. Deloitte finds that AI API call fees are a reason for public cloud spending exceeding budgets by 15%, and for 27% of public cloud costs being considered “wasted spend”.
  • Operating Expenses: At scale, AI transactions can be expensive. Cloud SaaS deployments are operational expenses. Over time, they tend to be more expensive, especially with heavy usage and automation.
  • RAG Training Costs: While leveraging Retrieval Augmented Generation (RAG) is always more cost effective than training and maintaining your own LLM, initial RAG training on large datasets can still be expensive. Understand costs associated with parsing/embedding documents into the retriever and their impact on startup costs.

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5. Summary Comparison: On-Prem vs. Cloud LLMs and AI

This table provides an easy guide to evaluate the advantages and disadvantages of cloud and on-premise (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:

• Opt for on-prem LLM and AI if security, compliance, customization, and large data amounts are important.
• Opt for cloud LLM and AI vendors if time to value and small scale experimentation is your top priority, or if your project will only use a limited amount of source data.
• Opt for a hybrid approach if different parts of the business have different needs and you can realize strong benefits of reduced costs for certain segments.

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 upfront 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.

By carefully evaluating factors such as cost, customization options, scalability, and security, businesses can make informed decisions that align their business objectives and operational requirements with the AI options they are considering.

Summary and Recap

Choosing between on-premise and cloud deployment models for AI for enterprises is crucial for innovation and ROI. On-prem offers superior security, data control, and cost advantages at scale, ideal for sensitive data and high usage. Cloud solutions provide faster startup and lower initial investment, suitable for quick experimentation. Key factors like security, availability, customization, and costs must align with business needs. Allganize offers expertise in both, guiding enterprises to make informed deployment model decisions.

Frequently Asked Questions

1. What are the primary advantages of an on-premise AI deployment?On-premise deployment models offer maximum security and control over data and IP, high customizability, potentially lower latency for critical real time applications, and cost advantages at scale due to capitalization opportunities and lower variable transactional costs.

2. When is a cloud based AI solution most suitable for an enterprise?Cloud based AI solutions are most suitable for quick time to value, initial experimentation, or proof of concepts, and for businesses with smaller startup budgets or those with non critical data and lower transactional needs. They offer fast go live for standard products.

3. How does the type of data influence the choice of deployment model?For highly sensitive data, intellectual property, or data subject to strict regulatory compliance (like in finance or healthcare), on-premise deployment models provide the highest level of security and control, preventing data exposure to public LLMs. Less sensitive data might be suitable for cloud.

4. What role does cost play in choosing between on-premise and cloud AI?Cost is a major factor. While on-premise has higher upfront capital expenditure for infrastructure, it can be more cost effective at scale due to asset depreciation and lower variable costs for high usage. Cloud solutions have lower initial costs but can become expensive with high transactional volumes and long term operational expenses.

5. Can enterprises use a hybrid approach for AI deployment?Yes, a hybrid approach is often optimal, allowing businesses to leverage the benefits of both on-premise for sensitive or high volume workloads, and cloud for experimentation or less critical applications. This flexibility enables alignment with diverse business needs and maximizes ROI.

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.

Discover how AI is transforming enterprises at allganize.ai