ZEDEDA Architecture and Core Platform

The ZEDEDA Edge Intelligence Platform manages the full lifecycle of edge intelligence deployments, from device onboarding to AI workload orchestration and ongoing operations. It automatically reduces the complexity associated with matching AI models to the right inference engines and running them across a wide variety of edge AI hardware.

The platform establishes a consistent operational model across diverse edge hardware, allowing infrastructure, applications, models, and agents to be deployed and managed through a centralized control plane.

 ZEDEDA connects to edge nodes running EVE-OS through a secure, outbound-only model—eliminating inbound ports and enforcing a Zero Trust posture by design.

The architecture is built to:

  • Operate across heterogeneous edge hardware, from lightweight gateways to GPU-enabled systems
  • Support edge intelligence workloads under real-world constraints, including limited bandwidth and intermittent connectivity
  • Scale from initial deployments to large, distributed fleets using the same workflows
  • Enforce Zero Trust across devices, workloads, and communication paths
See How ZEDEDA is Built →

How the Platform Works

ZEDEDA brings together orchestration, infrastructure, and AI lifecycle management into a single operational model designed for how edge intelligence actually runs in distributed environments.

The platform is built on three core pillars:

ZEDEDA Edge Intelligence Platform

The centralized control plane for deploying, managing, and securing edge intelligence at scale.

It governs access, enforces policy, and manages lifecycle operations across distributed edge clusters at global scale, with integrated edge-native services for updates, access control, and workload lifecycle management, including the inference engines, AI models, infrastructure services and AI agents that bring native MLOps and GitOps practices to the edge.

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EVE-OS

A secure, open-source edge operating system purpose-built for edge intelligence.
It abstracts hardware complexity and provides a trusted foundation for running containers and virtual machines side by side on any edge device, with strong isolation, hardware-rooted identity, and no local user access. EVE-OS is supported on a wide range of hardware platforms, including x86 and ARM architectures. It comes with drivers and SDK support for a wide range of edge AI hardware accelerators to deliver the best in breed AI model inference performance.

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ZEDEDA Edge Marketplace

A curated ecosystem of hardware vendors, infrastructure software partners, AI model providers, agent framework providers, edge AI silicon partners, edge intelligence solution partners and AI observability providers.

It enables interoperable, production-ready deployments without fragmentation or vendor lock-in.

Explore the Marketplace →

Edge AI: From Model to Production in Minutes, Not Months

ZEDEDA operationalizes edge AI through a unified workflow that connects model development, deployment, and lifecycle management, without requiring custom infrastructure or heavy DevOps dependency.

The workflow follows four key stages:

STEP 1

Create a Project and Define Workloads

Administrators begin by creating a project, which may contain one or thousands of edge devices. Within the project, they define:

  • Application and network policies
  • Security and attestation requirements
  • User access rights (RBAC)
  • Expected hardware variations (GPU acceleration, LTE modules, etc.)

     

Using the ZEDEDA Marketplace, administrators select the applications and workloads to deploy. Marketplace manifests specify:

Source and Runtime Type

  • Image locations
  • Whether the workload runs as a container, VM, or Kubernetes service

Compute and Network Configuration

  • vCPU and memory allocations
    Network and firewall configurations

Instantiation Parameters

  • Optional scripts or templates needed during instantiation

Hardware Requirements

  • Direct-attach hardware (GPU, FPGA, USB, Serial)

     

Once reviewed, the configuration is applied to all current devices and automatically to any new devices that meet the project policies.

How ZEDEDA Works

ZEDEDA delivers a consistent and secure edge orchestration experience from device onboarding to full lifecycle management. The following steps illustrate how the platform works in practice.

Device Trust & Integrity

  • Measured boot and remote attestation verify system integrity
  • TPM-based identity prevents device spoofing and cloning

Data Protection

  • Encryption at rest and in transit
  • Model signing and encryption, with keys rooted in hardware security modules such as TPM

Access Control & Isolation

  • Outbound-only communication model
  • Lockdown of physical interfaces to prevent tampering
  • Strong workload isolation across containers and virtual machines

Application & Deployment Security

  • Cryptographic verification of system and application artifacts
  • Distributed firewall enforcement at the workload level
  • Secure, fail-safe updates with rollback protection

AI & Agent Security

  • Built-in guardrails for agent behavior and execution
  • Human-in-the-loop approval for selected workflows before autonomous actions

Security at Every Layer

ZEDEDA enforces Zero Trust across the full edge stack, protecting devices, workloads, data, and AI systems in environments where physical and network risks are inherent.

Threats addressed include:

  • Unauthorized access and credential compromise
  • Physical device tampering
  • Network-based attacks on edge nodes
  • Runtime and OS exploits
  • Model theft, poisoning, and misuse
  • Malicious or compromised agent interactions

To address these threats, ZEDEDA provides security controls at multiple layers:

Explore ZEDEDA’s Security Architecture →

Automation with APIs

ZEDEDA integrates into existing enterprise workflows through APIs and infrastructure-as-code support.
For advanced workflows, ZEDEDA supports Terraform and northbound APIs to automate the full lifecycle of edge intelligence, from provisioning to deployment and operations, while integrating with CI/CD systems, application controllers, orchestration platforms, and cloud environments.

Use cases include:

  • Infrastructure and workload automation using Terraform
  • Integration with cloud services for data pipelines and edge-to-cloud workflows
  • SD-WAN and firewall integrations for secure connectivity
  • Kubernetes ecosystem integrations for portable, cloud-native workload orchestration at the edge
  • Custom lifecycle workflows built through northbound API integration with existing enterprise systems
Design Your Automation →

Why ZEDEDA Works for the Edge

ZEDEDA is designed for the way edge intelligence operates in the real world, across distributed environments with constrained resources, limited connectivity, and no on-site IT.

Built for Distributed Environments

Operates reliably across air-gapped, low-bandwidth, and segmented networks

Vendor-Neutral by Design

Open architecture eliminates hardware and platform lock-in

Consistent Operational Model

Deploy and manage containers, virtual machines, models, and agents through a single workflow

Proven at Scale

Trusted to run mission-critical edge workloads across large, globally distributed fleets of edge nodes

Operationally Simple

Centralized control, policy-driven workflows, and GitOps keep distributed edge environments manageable for small teams