As AI adoption expands and token consumption climbs, businesses are looking beyond cloud-only deployments to balance performance, access, and long-term costs. Artificial intelligence is becoming deeply embedded in how businesses work, but as organizations expand their AI usage, another question is becoming harder to ignore: how much will all of this AI actually cost?
The rise of frontier models, broader employee access, and agentic AI is driving higher usage across enterprises. More employees are using AI tools, workloads are becoming more intensive, and every prompt, response, and automated task can contribute to growing cloud bills. For businesses that expect AI adoption to continue accelerating, relying entirely on cloud infrastructure could become increasingly expensive.
That is where hybrid AI, which combines local and cloud-based AI workloads, is emerging as a potential way to control costs without limiting access to advanced AI capabilities.
According to Alexey Navolokin, General Manager, APAC at AMD, organizations should begin considering this balance now rather than waiting for AI costs to become a larger financial burden.
The rising cost of cloud AI
For companies that have expanded their AI usage over the past 18 months, the cost curve can be familiar.
Employees gain access to AI tools, usage increases, and organizations begin experimenting with more advanced applications. As agentic AI becomes more common, those interactions can involve significantly more tokens, further increasing consumption.
What begins as a productivity investment can eventually become a recurring cost-management challenge. This is particularly relevant for knowledge workers whose AI workflows often involve multiple rounds of prompting before a final result is produced.
An employee may ask an AI assistant to draft something, refine the response, change its tone, shorten it, generate another version, or troubleshoot an approach before finally executing the task. When all of these interactions run through a cloud API, organizations may effectively be paying cloud rates for work that does not always require the capabilities of a frontier model.
A hybrid AI strategy offers another approach: use local AI for workloads that can run efficiently on employee devices while reserving cloud resources for tasks that genuinely require them.
What the AMD Tokenomics Calculator reveals
AMD is promoting its Tokenomics Calculator as a way for businesses to compare the potential economics of different AI deployment strategies.
The tool models three scenarios: Cloud Only, Local (AMD), and Hybrid. It calculates total cost of ownership over one, three, four, or five years, along with monthly costs, estimated break-even points, and recommended AMD hardware configurations based on workload and team size.
The calculator also includes a Hybrid Mix slider, allowing organizations to adjust what percentage of their AI token volume is processed locally versus through the cloud.
AMD’s example uses a medium workload tier representing approximately 5.7 million input tokens and 574,000 output tokens per user per day. Under the company’s model, a fleet of 500 AMD AI PCs operating with a 50% local and 50% cloud split could produce projected three-year savings of 40% to 60% compared with cloud-only deployment, depending on the cloud model being used.
A fully local deployment could produce higher projected savings, with the example reaching its break-even point in under 24 months. These figures are estimates rather than guarantees. Actual costs will depend on factors such as workloads, hardware configurations, electricity consumption, cloud pricing, and negotiated enterprise rates.
Still, the exercise highlights a broader consideration for businesses: not every AI workload necessarily needs to run in the cloud.
Why some AI workloads make sense locally
Many everyday AI interactions do not require access to the largest available model.
Consider the early stages of creating a prompt. Employees may spend several rounds testing instructions, restructuring questions, or experimenting with different approaches before reaching the final request. Those interactions benefit from speed and availability, but they may not require frontier-level reasoning.
Running suitable workloads locally can therefore provide employees with an always-available AI environment without generating additional cloud token costs for every interaction.
AMD argues that devices powered by AMD Ryzen AI processors and AMD Radeon graphics can provide another execution path for workloads such as drafting, summarization, analysis, and code generation. The result is not necessarily an either-or decision between local and cloud AI. Instead, businesses can assign workloads according to their requirements.
Local AI can handle routine or iterative tasks, while cloud AI remains available for complex reasoning and workloads that require more powerful models.
AI access can also become a workforce issue
Cost is not the only consideration.
As AI becomes increasingly important to workplace productivity, restricting access because of the cost of additional cloud licenses can create another problem.
Enterprise AI services are often tied to a certain number of seats. Once those licenses are exhausted, additional employees may have to wait, rely on alternative tools, or work without access to the same AI capabilities as their colleagues.
That can create an indirect cost that does not necessarily appear on an organization’s AI invoice.
Local inference offers another access path. Employees who do not have cloud AI seats could potentially use locally executed AI for appropriate workloads without consuming an additional license or generating additional cloud token usage.
For organizations with hundreds or thousands of employees, that distinction could become increasingly relevant as AI adoption spreads beyond specialized teams.
Hybrid AI puts IT and finance at the same table
The enterprise AI conversation is gradually shifting.
Early discussions centered heavily on what AI can do. As adoption matures, businesses are increasingly asking whether they can sustain those capabilities at scale.
For IT leaders, the challenge is to provide employees with useful AI tools without allowing infrastructure and licensing costs to grow uncontrollably.
For finance teams, meanwhile, the question is whether increased AI spending will translate into measurable productivity and business value.
Hybrid AI potentially addresses both concerns by distributing workloads across different forms of compute.
Rather than sending every request to the cloud, organizations can determine which tasks benefit from local execution and which justify the cost and capabilities of cloud-based models.
That approach also gives businesses more flexibility as AI models, hardware, pricing, and employee usage patterns continue to evolve.
The next phase of enterprise AI may be about efficiency
Cloud AI is unlikely to disappear. Frontier models will continue to play an important role in advanced reasoning, complex workflows, and agentic applications.
But businesses may not need to send every AI interaction to those models.
Advances in local AI hardware and smaller AI models are making on-device inference increasingly practical for everyday workloads. At the same time, tools such as AMD’s Tokenomics Calculator give organizations a way to model different deployment scenarios before committing to a particular infrastructure strategy.
For enterprises entering the next stage of AI adoption, the question may therefore be less about cloud versus local and more about finding the right balance between them.
The companies that get that balance right could gain access to AI’s productivity benefits without allowing token consumption and licensing costs to scale at the same rate as usage.
The future of enterprise AI may not belong to businesses that simply use more AI, but to those that know where each AI workload should run.
AMD notes that calculator outputs are estimates based on publicly available pricing and configurable hardware assumptions. Actual results may vary depending on workload, hardware configuration, electricity costs, and negotiated pricing.
