Agentic coding tools can be powerful force multipliers for your business, delivering the productivity of entire development and testing teams without increasing headcount. While agents autonomously complete multi-step and end-to-end workflows, your in-house developers can focus on priorities like higher-level design and logic, security, and strategic problem solving. But there is a catch. If you structure development workflows around cloud-based agentic tools, those tools will burn through tokens, multiplying your cloud costs. By running agents and their supporting LLM on local, AI-capable devices, you can avoid snowballing or unpredictable cloud AI service expenses.  

To integrate local agentic AI coding tools with budgetary and operational confidence, you’ll need to know whether local systems can handle the processing load. You’ll also need to understand how the cost of running local agentic coding workloads compares to the cost of running those same workloads using cloud models, and how long it may take to recoup initial hardware outlays.  

To help decision makers navigate those variables, we ran multiple agentic coding workloads on an AMD Ryzen AI Max+ processor-powered mobile workstation and compared the total associated costs to equivalent cloud usage costs over time.  

How we tested local agentic coding and evaluated costs

We used a local Qwen3.6-35B-A3B model, one that provides good support for coding tasks, in LM Studio to run an interactive agentic coding workload on an HP ZBook Ultra G1a 14 inch, a mobile workstation built around an AMD Ryzen AI Max+ PRO 395 processor. The workload ran short, scripted conversations where an AI agent reviewed code, found bugs, or wrote tests—the kind of quick back-and-forth tasks a developer performs throughout the day. We also ran 28 autonomous background coding jobs in which the agent worked unattended on multi-step tasks. 

We then modeled a heavy agentic tool user’s token consumption (30 chat tasks, 4 hands-on agentic tasks, and 6 hours of background agent work per day for 21 days a month) and priced it using a leading frontier, cloud-based model, Claude Sonnet 4.6 API. We used list rates and assumed an input token cache rate of 85 percent. The costs for our local setup included the workstation’s list price and electricity. 

What the testing found

We found that a single AMD Ryzen AI Max+ processor-powered HP ZBook Ultra G1a 14 inch mobile workstation can handle demanding agentic coding work entirely on device and, at a heavy user’s token volume, pay for itself in well under a year and a half.  

  • The workstation completed 27 of 28 autonomous background coding jobs, a 96 percent pass rate, at about 18 completed jobs per hour. 

  • At roughly 16.3 million tokens per day, the on-device setup would break even with cloud costs in 15.7 months. 

  • Over three years, each system would save $6,574. 

What this means for development teams 

While on-device LLM capabilities do not match the compute power of frontier cloud AI services, most day-to-day coding work does not need a frontier model at every step. For developers who have built their workflows around agentic tools, moving high-volume coding work to a local device can turn volatile per-token cloud bills into fixed, predictable hardware costs. That shift can help teams save money, pay off their PCs quickly, stay within budget and allow them to reserve frontier cloud models for harder problems. 

For more in-depth information about our local agentic coding tests and payback analysis, check out the detailed report below.