An edge AI infrastructure decision platform built by SNtricity. EdgeAIStack helps engineers and systems integrators plan, size, and specify real-world edge AI deployments with structured, vendor-neutral decision tools.
EdgeAIStack is built and operated by SNtricity, an infrastructure intelligence decision platform company. SNtricity is the parent organization of EdgeAIStack. The platform is accessible at edgeaistack.app.
EdgeAIStack is an edge AI infrastructure decision platform. It exists to solve a specific problem: engineers scoping edge AI deployments face simultaneous, interrelated infrastructure decisions — compute hardware selection, power budgeting, storage endurance, network bandwidth, and physical deployment architecture — with no single structured resource to work through them.
EdgeAIStack addresses this directly. The platform combines a deep technical knowledge base with a set of interactive engineering tools that take real deployment parameters and return specific sizing recommendations, hardware guidance, and deployment specifications.
The platform is vendor-neutral by design. Guidance and tool outputs are based on technical specifications and deployment requirements, not commercial relationships.
EdgeAIStack is built for engineers and technical buyers working on real infrastructure:
The platform is not written for analysts or executives. It is written for engineers making specific technical decisions under real cost and reliability constraints.
Edge AI infrastructure decisions are routinely underspecified at project initiation — leading to power shortfalls, storage failures, network congestion, and hardware incompatibilities discovered during or after deployment. EdgeAIStack provides the structured decision support to avoid these failure modes.
Specifically, the EdgeAIStack platform addresses:
Platform details
Topic coverage
Decision tools
The EdgeAIStack deployment tools accept real deployment parameters and return specific, actionable sizing guidance. They are designed to be used during the planning phase of an edge AI deployment — before hardware is ordered or infrastructure is specified. Learn how these decision engines work together to evaluate interconnected infrastructure constraints.
Estimates total power draw for an edge AI deployment based on compute hardware, sensor count, and operating conditions. Produces PoE switch sizing recommendations and power delivery architecture guidance.
Open calculator ↗End-to-end deployment planning for multi-camera edge AI systems. Takes camera count, resolution, frame rate, inference requirements, and site constraints — outputs a structured deployment specification.
Open planner ↗Technical analysis of edge AI accelerators. Covers performance, power profiles, connectivity, and deployment constraints to guide hardware selection decisions.
Read comparison ↗Estimates storage endurance and write-cycle consumption for AI inference workloads. Takes write rate, duty cycle, and temperature parameters — outputs expected lifespan and specification guidance for storage selection.
Open tool ↗Models bandwidth requirements for multi-camera and multi-sensor edge AI deployments, including encoding overhead, inference data transfer, and management traffic. Produces per-port and aggregate bandwidth specifications.
Open tool ↗Structured planning guidance for PoE-powered edge AI deployments: switch selection, power budget allocation, cable run limits, and redundancy design for high-density camera and sensor networks.
Open resource ↗High-duty-cycle edge AI workloads stress storage systems. Our NVMe storage guide for Jetson Orin Nano covers write-cycle estimation, SSD selection, and failure prevention.
Understand the power profiles of inference platforms. Our Jetson Orin Nano power consumption analysis provides thermal guidance and deployment sizing recommendations.
Knowledge base
Beyond the interactive tools, the EdgeAIStack knowledge base covers the full range of edge AI infrastructure topics — written for engineers who need practitioner-grade guidance, not introductory overviews.
System-level hardware selection guides for edge inference platforms: NVIDIA Jetson, Intel OpenVINO targets, Hailo accelerators, Rockchip NPU systems, and custom edge compute configurations.
Documented architecture patterns for common edge AI deployment configurations — hub-and-spoke, distributed inference, on-camera inference, and hybrid cloud-edge processing models.
Engineering guides for managing power consumption and thermal performance in edge AI hardware — passive vs active cooling, power mode selection, thermal derating, and deployment environment planning.
Storage selection and architecture guides for AI inference workloads: NAND endurance characteristics, write amplification, eMMC vs NVMe tradeoffs, and RAID configuration for high-duty-cycle systems.
Network architecture guides covering PoE topology, switch selection, VLAN segmentation for camera and AI workloads, time synchronization, and bandwidth management for real-time inference pipelines.
End-to-end design guidance for camera-based edge AI systems: camera selection, stream management, multi-stream inference scheduling, and deployment planning for facility, outdoor, and industrial environments.
Why the decision layer matters
Technical guides and engineering articles establish what is possible and what considerations apply. But infrastructure decisions require specific outputs: a power budget, a hardware BOM, a storage specification, a network topology. Content alone cannot produce these.
EdgeAIStack's decision tooling layer bridges this gap. Engineers arrive with specific deployment parameters — camera count, duty cycle, ambient temperature, available power budget — and leave with specific sizing guidance they can act on.
This is what makes the EdgeAIStack infrastructure planning platform different from a documentation library or a vendor comparison site. It is a decision system, not a content archive.
Camera count, resolution, frame rate, inference model type, site environment, power availability, network topology.
Power budget planner, hardware selector, storage calculator, bandwidth estimator, deployment architecture planner.
Compute hardware selection, power budget, storage specification, network topology, and deployment configuration — actionable and specific.
Use the EdgeAIStack edge AI infrastructure planning platform to scope hardware, size power and network systems, and validate your deployment architecture before you specify or purchase.