Updated: June 6, 2026

Generative AI has emerged as a powerful way for organizations to unlock their data to produce game-changing insights. At the same time, though, this has created a growing need to ensure that these insights, and the data underpinning them, are both confidential and secure. In response, a growing number of business leaders are opting for private deployments of AI that give them more control over hardware, software, and data.

In a private deployment, companies implement and run AI models within a controlled, internal environment. It generally comes in two flavors: on-premises (on-prem) and on a virtual private cloud (VPC).

With VPC, an organization uses infrastructure needed to host AI models from a cloud provider while still retaining control of how the data is stored and processed.

An on-prem deployment gives organizations full control over both their data and the AI system on their own premises with their own hardware. They procure their own GPUs, servers and other hardware to insulate their environment from external threats.

Now let’s take a closer look at the enhanced security that on-prem and private cloud provide, along with some other benefits and considerations.

Why businesses choose private deployments

Businesses choose private deployments to keep their data secure, customize models for better performance, and deliver results faster.

Ring-fence your data to enhance security

A thicket of regulations is growing around data and how it is used by generative AI. In the U.S., healthcare information is governed by rigorous regulations like HIPAA, while in Europe laws such as GDPR and the Artificial Intelligence Act have added to a rising number of guidelines and limitations.

This is a concern for businesses used to working with public cloud services for a couple of key reasons. One, sending data to, from, and within their cloud provider increases the risk of that data being leaked or hacked — which could trigger an expensive penalty from regulators.

Second, if an organization is using an AI model in a public cloud, there’s a heightened risk that employees may feed it sensitive information through their prompts and queries. Those prompts could then be used by the model provider or the cloud vendor to train the model, which could result in the transmission of private data to the outside world.

When on-prem, both the data and the AI model are ring-fenced from the outside world. Unlike cloud-based solutions, information isn’t exposed during transmission on external networks or within the hardware where the same AI models are often shared with other organizations.

A private cloud deployment offers similar yet somewhat less rigorous protection. Both hardware and AI models are not shared with other organizations and exist within your own virtual private cloud but your data has to leave your premises to reach the model in the cloud. That could crack the door open to potential threats.

Customize your model privately for security and performance

Whether they’re an insurer, bank or government department; different organizations often have wildly different needs and opportunities with GenAI. And they may want an AI partner that specializes in customizing models for enterprises with very sensitive and domain-specific data.

Fine-tuning in a private environment allows companies to maintain strict control over their data, avoiding the risk of sensitive or proprietary data leaking as it goes through a public cloud. It can also lead to superior processing and response performance by minimizing the communication delays that can occur with remote servers.

As it builds GenAI capabilities into its Fusion Cloud Applications, for example, Oracle has refined privately deployed Cohere models to improve performance and accuracy across a range of use cases, including finance, HR, supply chain, and customer experience.

Speed up delivery

Public cloud is fast and easily scalable but for some organizations it isn’t fast enough in cases where the necessary hardware is not available in the region. The need for more speed could arise in a financial firm that needs to execute trades seconds ahead of others, or with organizations in Asia or the Middle East located far from their cloud provider’s data centers in North America and Europe.

A few years ago, maintenance on submarine cables between Europe and Asia caused latency to increase by 40 milliseconds.

In such cases, on-prem may provide the fastest solution.

Getting started with private deployments

To get started with private deployments, focus on how you will measure ROI from the beginning and what type of skills and teams you will need to get it right.

Measure the ROI from the start

But with every benefit there is a cost. And for organizations looking at improved data protection, customization and speed, there will be additional expenses that come with private deployment.

Buying your own hardware like GPUs for on-prem deployment is going to be more costly and harder given the high demand for the chips. And if an organization is limited in the amount of hardware they can afford, this can limit their ability to scale like you can with a large cloud provider.

But there are some positives here too. Running a model on-prem can mitigate some of the unpredictability in pricing that occurs with public cloud. More control equals more clarity around costs.

These costs are also being addressed by increasingly efficient AI models that require a smaller GPU footprint.

Then there’s ongoing progress with vector databases, with steady improvements in compression allowing more vectors with less storage.

These developments and others are adding up to significant cost savings for some on-prem users. For instance, a Cohere customer with multiple high volume use cases might incur a lower cost on-prem compared to the cost of running that model via a SaaS API after a certain usage volume. That’s because pricing for on-prem is based on the number of instances rather than on usage.

There’s one more major consideration when it comes to costs. And that’s the price to be paid in the event of a data breach—not just in terms of cost that now adds up to almost $5 million per breach—but in the loss of customer trust and brand reputation as well.

Set up your AI dream team for success

AI models are complex and it often takes a specific set of skills to both set them up and keep them running. But adopting and running an AI model can be surprisingly smooth and fast.

How fast? It takes Cohere less than a week to put a base GenAI model in a container and ship it over to a customer with their own up-and-running infrastructure. And that applies to whether a customer is managing their environment on-prem or in a private cloud.

A customized model will take longer depending on a customer’s knowledge of infrastructure and AI models. Where they need assistance, Cohere can provide technical experts to fill any gaps. A simple fine-tuning may take a month, while deep customization or building a brand new model from scratch usually takes up to three months. If hardware isn’t available, that will add some lead time as well.

With the right partners and a strong AI team, businesses can get their on-prem solution up and running in a matter of weeks and a fully customized solution in a couple of months. And based on what we've seen over the last year, these timelines will likely drop further—providing organizations with a quick transition to a secure, customized solution that improves both innovation and productivity.

Mastering private AI deployments

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Why more businesses choose private deployments of AI