An edge computing platform lets you deploy, run and manage applications and AI models on devices and servers close to where data is created, such as factories, stores, vehicles and cell towers, from one central console. It handles software rollout, updates, monitoring and security across hundreds or thousands of sites, so each location doesn’t need its own IT team.
Updated October 2026
What changed in 2026
- Edge spending kept climbing. IDC puts worldwide edge computing spending at $265 billion in 2025, nearly doubling to about $450 billion by 2029, and says edge AI "is no longer experimental" (IDC).
- New edge AI hardware. NVIDIA's Jetson T4000 (64 GB, up to 1,200 FP4 TFLOPS) went on order in January 2026 with JetPack 7.1, and IGX Thor became generally available for industrial edge AI in March 2026 (JetsonHacks, NVIDIA).
- Platforms are judged on AI. Gartner advises infrastructure leaders to prioritise extensible edge platforms that handle edge AI, and expects 30% of enterprises to use edge platform software or services by 2029 (Gartner via ZEDEDA).
- Device security reporting became law. From 11 September 2026 the EU Cyber Resilience Act requires manufacturers to report actively exploited vulnerabilities within 24 hours (McCann FitzGerald).
Why edge, not just cloud
- Speed: decisions in milliseconds where a cloud round trip is too slow, such as stopping a machine or flagging a defect.
- Bandwidth: process video and sensor data locally and send only results.
- Resilience: sites keep working when the network drops.
- Data control: sensitive data stays on-site to meet privacy and sovereignty rules.
Types of edge platforms
| Type | Examples | Good for | Watch out for |
|---|---|---|---|
| Hyperscaler edge | Azure IoT Edge, AWS IoT Greengrass, Google Distributed Cloud | Teams already on that cloud | Tied to one cloud’s services and pricing |
| Kubernetes-based edge | K3s, KubeEdge, Red Hat OpenShift at the edge, Portainer | Container apps across mixed hardware | Needs Kubernetes skills; small devices may struggle |
| Industrial IoT edge | ClearBlade and similar IIoT platforms | Protocol support (Modbus, OPC UA), plant data | Less suited to heavy AI workloads |
| AI hardware stacks | NVIDIA Jetson / IGX, Intel OpenVINO | Running vision and AI models efficiently on devices | Hardware-specific; you still need fleet management |
| Agent orchestration | NexaStack | Running AI agents that act on edge data, with central rules and audit | Pairs with the layers above rather than replacing your hardware |
What to look for
- Zero-touch device onboarding and remote updates
- Runs on the hardware you already have
- Offline operation with sync when the link returns
- Model deployment and monitoring (MLOps at the edge)
- Security: device identity, encryption, signed updates
- Central policies for what AI can do at each site, with an audit trail
- Clear pricing per device or site
Where AI agents fit
Most edge platforms run software. Physical AI needs that software to decide and act: hold a batch, adjust a setpoint, dispatch a technician. NexaStack orchestrates those agents at the edge and in your cloud, under rules you set. See What is Physical AI? and NexaStack Edge AI.
Where it's heading: 2026 → 2030
| When | What it looks like |
|---|---|
| Around 2020 | Edge platforms mainly ran containers and forwarded data to the cloud (Azure IoT Edge, AWS IoT Greengrass). AI at the edge was a pilot. |
| 2026 (now) | Platforms are chosen for running and updating AI models across fleets, on GPU-class modules such as Jetson Thor, with security reporting now required in the EU. |
| By 2028 (expected) | Gartner expects more than two-thirds of enterprise-managed data to be created and processed outside the data centre or cloud; most EU Cyber Resilience Act duties apply from 11 December 2027 (Gartner via ZEDEDA, McCann FitzGerald). |
| By 2030 (expected) | Gartner expects over two-thirds of enterprises to deploy edge AI by 2029, up from 10% in 2025; IDC expects edge spending near $450 billion by 2029 (Gartner via ZEDEDA, IDC). |
Frequently asked questions
What are examples of edge computing platforms?
Azure IoT Edge, AWS IoT Greengrass, Google Distributed Cloud, K3s/KubeEdge, and industrial platforms such as ClearBlade.
Is Netflix edge computing?
Netflix uses a content delivery network that caches video near viewers. That is edge delivery of content, not edge computing on local data.
Is edge computing replacing the cloud?
No. The edge handles fast, local decisions; the cloud handles training, coordination and long-term storage.
What are the latest edge computing platform trends in 2026?
Platforms are judged on how well they deploy and update AI models across many sites, GPU-class edge modules are getting cheaper, and EU security reporting rules now apply to connected devices.
Pick the right edge stack for your sites
Comparing edge platforms for AI at many locations? We will map your sites, hardware and use cases to a practical architecture. Book a 30-minute edge AI readiness session →
