Private AI: Your Choice - Your Place, My Place, or Our Place?

Earlier this month, I spoke at two webinars about Private AI with over 400 registrants and attendees - and participated in the fantastic Q&A that followed the sessions.

Folks are excited about the idea of using Private AI for document search and even making metadata easier!

But, one of the biggest areas of confusion was where Private AI should happen.

Should it be on your premises, my (i.e. vendor hosted cloud) premises, or a shared cloud environment?

Let's break it down.

Like what you see? Want to see more? I invite you to chat with my team at Shinydocs.

The Challenges with New Cloud-Based AI

Many new cloud-based AI providers are available today. They make interacting with AI easy and convenient, so lots of users will reach for tools like ChatGPT or Copilot, making them extremely popular.

But, these cloud-based tools come with some significant challenges, like:

• Intellectual Property (IP) Concerns: Many AI providers use customer data to train their models, potentially putting your proprietary information at risk. 
• Security Risks: Sharing sensitive data with third party cloud vendors increases vulnerability. 
• Performance Risks: Moving data through external providers causes SLOW processing. 

These risks are what make Private AI an attractive alternative, but does that mean you have to run AI on local hardware? Not necessarily!

Choosing the Right Deployment Model

Let's look at the different options along with their trade-offs.

On-Premise AI (Your Place)

If security is your top concern, running AI on your own infrastructure may be your best choice. It provides:

• Full control over your data and models
• Maximum security with no third-party exposure
• No dependency on cloud providers

Yes, maintaining hardware might seem daunting, but it’s entirely possible. I've worked with many companies that do it successfully.

If you still have some of your own infrastructure, this approach ensures you retain complete control.

Managed Private AI (Private Cloud)

If you don’t want to manage hardware but still want a private, controlled AI environment, you can use managed hosting providers.

These services:

• Offer dedicated AI hardware that you control
• Provide security without the complexity of self-maintenance
• Allow for greater flexibility compared to full cloud AI

This is a great middle-ground option for businesses that want privacy and security but without the burden of physical infrastructure.

Hybrid AI (Our Place)

A hybrid approach allows you to start in the cloud while maintaining the flexibility to transition to on-premise later.

With this model, you can:

• Use cloud AI for initial deployment and scale as needed
• Retain control over your AI models and data governance
• Later migrate to on-premise for maximum security if necessary

This strategy ensures you're not locked into a single solution.

If you need more security, you can shift to an on-prem model. If convenience is key, you can remain in the cloud.

Private AI vs. Cloud AI - Looking at the Numbers

One of the biggest misconceptions about AI is that using public cloud services like ChatGPT or Copilot is the most cost-effective option.

In reality, public cloud AI services often have hidden costs (like per-page and per-token costs) that add up quickly, while also increasing security and compliance risks.

When we look at the numbers we recall how much faster and convenient for end users documents were (and in many cases less expensive for IT) before we moved them to the cloud...

It's YOUR Choice - What's Best for You?

You have a choice. Whether you run AI on your hardware, use a managed provider, or opt for a hybrid model, the goal is to protect your security, IP, and long-term flexibility.

If you missed the webinar, check out the full session here.