Cost-Efficient AI on the Hardware You Already Own

Today, many organizations play cloud AI providers for access by the token. This is convenient, but is not a metric that aligns with most businesses. The cost only increases as each team adopts AI and increases their usage.

It also doesn’t account for the fact that companies already have hardware available sitting on their desks. Countless desktop computers and laptops ship with iGPUs and NPUs purpose-built to run AI, yet in most cases this available computing power sits idle.

The growing cost of rented compute and the underutilization of existing hardware is one of the biggest inefficiencies in corporate technology spending today.

The pricing model for cloud AI inference works against the buyer in two ways.

Firstly, token-metered services are an unpredictable cost. Businesses are familiar with per-user and per-seat software licenses, and can comfortably account for this in their yearly budgets. While cloud AI providers do offer per-seat plans, these don’t work for companies doing a lot of automation (which use a lot of compute but don’t necessarily take up a “seat”), and often have fluctuating limits on usage.

Although the cost of a single token is decreasing over time, AI agents are running increasingly complex workflows resulting in higher net token usage. As mentioned, this also means the more a company adopts AI, the more they have to pay. This makes it an extremely difficult expense to account for.

The second way it works against buyers is that it physically cannot be adopted by certain industries. Many organizations in media, professional services, healthcare, and other public sectors likely need AI the most, and outside of the cost, they can’t allow their data to leave the building. The industries that could benefit from AI the most, therefore, have to adopt it more slowly because of legal limitations rather than technological ones.

To solve this problem we built Cascadia: software that turns the fleet an organization already owns into private AI infrastructure.

Cascadia splits AI models across the computers on your network, so machines that could usually run one small model can instead run larger, more powerful models by working together.

For employees and users, nothing about their experience using AI changes. What changes with Cascadia is where their work is happening.

They can continue performing the same tasks and workflows as they were previously, but the AI requests and workloads happen within your own network, on devices you own, rather than on someone else's infrastructure.

Cascadia works for organizations with fleets of any size, but the largest benefits are for industries dealing with sensitive and/or proprietary data.

A few examples of what a private, in-house AI can do for different industries:

Media

Newsrooms and studios work with unreleased material every day such as scripts, footage logs, and embargoed stories. A private assistant can summarize, search and draft against that material without needing to share assets with a third-party (or a token bill associated with it).

Professional services

Law and advisory firms have confidentiality obligations that make cloud AI a hard conversation with every client. Running on the firm's own machines, an assistant can review documents, compare drafts and prepare summaries while privilege and confidentiality stay intact.

Healthcare and the Public Sector

Patient records are some of the most protected documents to exist, and as such, are usually off limits to share with cloud providers, even with Zero Data Retention (ZDR) agreements. Running in-house AI can help staff summarize notes, and perform internal search tasks while staying compliant with regulations in their respective regions.

The same applies to government bodies, which are required by law to store and process citizen data on infrastructure they own. Cloud-based providers may also back up or replicate data across different countries. Using AI on a government-owned fleet helps protect the sovereignty of their citizens’ data.

Manufacturing and telecom

Operations teams sit on decades of manuals, procedures and internal documentation. A private assistant that has read all of it can turn this into useful information people can use without sending anything out of their network.

Running AI on your own fleet with Cascadia can cost you 47–56% less per user per year, and if each employee works from a single machine, the advantage can reach 73–78%. This is compared to the prices of standard per-user AI tools that are already in most company budgets.

Cascadia also works on a fixed-cost, per-device basis, which can help reduce the variability of costs. There is no per-token cost, and so teams can encourage the use of AI internally without being constrained by token budgets.

We’re currently offering a free pilot program to select partners who wish to try out Cascadia. We’ll set up your first private AI agent in under a week on your own hardware, and we measure success based on your own internal workloads and cost savings rather than arbitrary benchmarks.

After the pilot program ends, if you’re convinced we’ll transfer you to a predictable per-device enterprise plan. If not, you’ll at least know what your hardware is capable of.

If you’d like to try out Cascadia and save on your token costs, book a discovery call with us here.