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There is no universally best place to run AI. Cloud can suit experimentation and changing demand; private or on-premise infrastructure may merit analysis for steady, high-volume inference; edge can fit latency or data-locality needs. Choose by workload, total cost, operational conditions and lifecycle impact—not by a blanket preference for cloud or local deployment.
How should you choose where to run AI?
Start with the workload, then compare deployment options against its demand pattern, performance requirements, governance needs and operating conditions. Pete Overell, Managing Director and Founder of Panchaea, makes this workload-specific case in his 18 August 2026 AI Journal article, “Scaling Sustainable AI Infrastructure: Moving Beyond the Cloud Hype.” His article is thought leadership, not a controlled comparison of providers or facilities.
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| Option | When it may fit | Questions to resolve |
|---|---|---|
| Cloud | Experimentation, burst capacity, global deployment, managed services and training are among the uses Overell identifies. | What will inference, data transfer and service terms cost at the expected usage level? What data is processed, logged or retained? |
| Private or on-premise infrastructure | Overell says predictable, high-volume inference may justify a private-infrastructure total-cost-of-ownership analysis, particularly when utilization is high and demand is predictable. | Can the organization account for capital, energy, cooling, operations, support, depreciation and hardware refresh—not just accelerator purchase cost? |
| Edge deployment | Overell identifies latency and data locality as reasons to consider edge deployment. | Where must processing happen, what latency is required, and what power, cooling and support conditions are available at the deployment location? |
These are starting points, not guarantees. The cited sources do not establish a universal break-even point or a measured, like-for-like ranking of cloud, private and edge deployments.
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A proof of concept and a production service can have different demand patterns and cost structures. A cloud service can provide room to experiment or absorb bursts without committing immediately to a fixed deployment. If usage becomes steady and high-volume, Overell recommends analyzing whether private infrastructure’s total cost of ownership could be favorable. That is a conditional case, not a promise of savings.
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Build the comparison around the actual workload and include the costs that tend to sit outside a simple per-token or hardware-price comparison:
- Cloud: inference charges, data transfer, service terms and the cost implications of changing usage.
- Private infrastructure: capital expense, electricity, cooling, operations, support, depreciation and refresh cycles.
- Across options: the expected utilization, scale path and cost of capacity that is idle or unavailable when demand changes.
Overell raises these factors but does not supply a universal cloud-versus-private calculation. Build a project-specific model using the organization’s own workload, facility and service assumptions; do not treat a single token price or hardware quote as total cost.
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What does sustainable AI infrastructure need to measure?
Electricity consumed during model operation is only part of the lifecycle picture. Harvard Edge’s “Sustainable AI” chapter and the ITU AI for Good 2026 programme describe a broader footprint that includes energy, water, materials, infrastructure, hardware manufacturing and electronic waste. Their framing supports looking beyond runtime electricity; it does not establish that one deployment option has the lowest impact in every case.
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For a meaningful project comparison, define the boundary before comparing options: which workload and facility are included, which lifecycle stages are counted, and which impacts are being measured. Then assess the factors that Overell highlights alongside the broader lifecycle dimensions:
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- Supports access to online large model platforms and includes Edge Impulse object detection demo for real-time multi-object recognition
- Equipped with Xtensa dual-core LX7 processor (up to 240MHz), 8MB PSRAM, 16MB Flash, and dual-mode WF + BT LE
- Dual-microphone array with noise reduction and echo cancellation for high-quality voice processing
- Integrated audio input and output module, supporting AI speech interaction and voice recognition applications
- Onboard camera interface (DVP) and SPI / QSPI display interface for image capture, recognition, and external display connection
- Utilization: how much of the available accelerator capacity is doing useful work.
- Power: whether adequate power is available and how its sourcing is represented in the project assessment.
- Cooling: the facility conditions and cooling efficiency required to operate the equipment.
- Hardware lifecycle: manufacturing, useful service life, refresh decisions and end-of-life handling.
- Other lifecycle impacts: water, materials, infrastructure and electronic waste where relevant to the project’s boundary.
A headline accelerator specification alone cannot describe how efficiently a system is used or its full environmental profile. The relative importance of these measures depends on the project, so avoid claiming a sustainability winner without measurements made on comparable boundaries.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do governance, latency and scale affect the decision?
Map requirements to the architecture rather than assuming a deployment location provides particular protections or performance. Overell specifically raises control and data questions; his article does not establish the controls of any particular provider.
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- 8MP Gimbal Camera & Dual Servos for Professional Visual AI: The Starter Kit is equipped with an IMX219 8MP monocular camera and a dual-servo gimbal, supporting face and target tracking, and is ideal for AI edge computing scenarios such as intelligent monitoring, robot navigation, and automated recognition
- 38 Step-by-Step Python Tutorials, From Beginner to Practical Application: The Jetson Orin Nano Starter Kit comes with 38 well-designed Python tutorials progressing from basic programming to vision practice, covering all key knowledge of sensor control, embedded development and AI visual recognition for both beginners and advanced learners
- 11.6-inch IPS HD Screen & AI Voice Interaction System: Built-in 1366*768 resolution IPS screen eliminates the need for an external monitor, enabling one-device experimentation and visual feedback. The exclusive AI voice interaction system supports intelligent Q&A and voice command control for natural human-computer dialogue
- Rich Expansion Interfaces & Portable All-in-One Design: Features 2x I2C, 1x UART and 2 IO expansion interfaces to meet personalized experiment expansion needs; a custom carrying case integrates all components (11.81×7.87×3.94 inch), allowing AI experiments and demonstrations anytime and anywhere
- Latency and data locality: identify where the application needs processing to occur and whether edge deployment is appropriate to those requirements.
- Governance and control: determine where sensitive data is processed and what is logged or retained. Verify the actual service or system terms rather than inferring controls from the deployment label.
- Demand and adaptability: distinguish experimental or bursty use from predictable, sustained use, and assess whether capacity can grow without premature commitment.
- Operations: check that power, cooling, staffing and support can meet the workload’s needs at the intended location.
Overell’s central formulation is: “Sustainable AI isn’t about choosing cloud or on-premise in isolation; it’s about deploying the right architecture for the right workloads at the right stage of AI maturity.” Treat that as his recommendation, not as an independently measured finding.
What can a renewable-energy colocation example tell you?
Soluna Holdings’ 2025 SEC-filed annual report describes data-center colocation alongside renewable generation and hosting AI and high-performance computing workloads. It is a company-reported business example, not independent evidence that this arrangement always reduces cost or environmental impact. For an individual project, the relevant question remains what its own power, facility, utilization and lifecycle measurements show.
Quick Recap
A practical decision sequence
- Describe the workload: record whether demand is experimental, bursty, steady or high-volume, and how it may change as the AI service matures.
- Set performance and data requirements: specify latency and data-locality needs, and establish where sensitive information may be processed and what may be logged or retained.
- Compare complete cost models: include cloud inference and transfer costs, or private infrastructure’s capital and operating costs, with assumptions for utilization, support, depreciation and refresh.
- Set a sustainability boundary: decide which facility, workload and lifecycle stages to assess, then include relevant energy, water, materials, infrastructure, manufacturing and end-of-life considerations.
- Check operational fit and scale: assess power availability, cooling, utilization and the ability to adapt capacity without committing ahead of demonstrated demand.
- Choose for the current stage, then reassess: use the architecture that fits the evidence for the workload now; revisit the decision if demand, governance requirements or operating conditions change.
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