Edge Deployment

Edge deployment enables Druid to run AI knowledge management and productivity applications close to enterprise data sources and users, within customer-controlled infrastructure with full data sovereignty, rather than relying on centralized public cloud environments.

Implementation and Architecture

Druid supports edge deployment through its containerized, Kubernetes-based architecture, allowing the platform and its AI components (including knowledge bases, orchestration, and language models) to be deployed:

In on-premises data centers
In private cloud environments
In customer-managed edge infrastructure (e.g., branch locations, regulated environments, isolated networks).

Edge deployments can operate fully standalone or as part of a hybrid architecture, where selected services integrate with centralized cloud components while sensitive data and inference remain local.

NOTE: Edge deployment refers to customer-managed, localized infrastructure deployments (including private data centers and regulated environments) using the same Druid platform architecture as on-premises and hybrid deployments. Edge deployments may operate in restricted or disconnected network environments, subject to customer security policies.

Key Capabilities and Use Cases

This deployment model is designed for organizations that require:

Low-latency access to AI-powered knowledge
Strict data sovereignty and residency
Operation in disconnected or restricted network environments
Compliance with industry and government regulations