IoT & Edge

Edge to cloud.
Thousands of nodes.
One platform.

FalconIO brings Kubernetes-native fleet engineering to IoT and edge infrastructure — device lifecycle management, real-time telemetry routing, fleet-scale observability, BC Manifests for edge tiers, and incident management that treats edge nodes as first-class infrastructure citizens.

Fleet-Scale GitOps OTel at the Edge BC Manifests for Edge Tiers

Edge infrastructure fails
in ways cloud infrastructure doesn't.

Intermittent connectivity. Heterogeneous hardware. Thousands of devices with configuration drift you cannot see until a field failure surfaces it. Firmware updates that cannot brick a device mid-deployment. Real-time telemetry that must reach the cloud even when the network does not cooperate.

Configuration Drift at Fleet Scale

Device configuration drift is invisible until a field failure surfaces it. At thousands of nodes, manual drift detection is not feasible. You need the same reconciliation model as cloud infrastructure.

Telemetry Routing at Scale

IoT telemetry from thousands of devices overwhelms naive pipeline designs. Backpressure, buffering, and graceful handling of intermittent connectivity must be designed in — not bolted on.

No DR Plan for Edge Tiers

BC/DR for edge fleets is typically not planned until after the first major incident. Offline-mode fallback, cloud-sync resumption sequencing, and fleet-tier recovery need to be declared as code — before the incident.

Every device.
A managed infrastructure node.

Edge node configuration profiles are provisioned via the IDP service catalogue. Standard fleet configurations execute via Crossplane compositions. Complex edge deployments — heterogeneous hardware, conditional connectivity profiles, region-specific firmware — execute via Pulumi stacks with the same tested, audited automation as cloud infrastructure.

We have operated Kubernetes-based edge control planes managing AI inference on GPU-accelerated edge devices, with blockchain-anchored supply chain audit trails, at fleet scale. This is not a whitepaper. It is operational experience.
Telemetry Pipeline
Edge Device OTel Collector Vector (buffered) Cloud Aggregator ClickHouse + Object Storage
Kubernetes-native edge fleet management — same control plane as cloud workloads
Edge node intents in IDP catalogue — fleet operators use same self-service surface as cloud infra
FluxCD GitOps for edge delivery — firmware and config updates, blast-radius-controlled rollout
Vector pipelines for telemetry — backpressure-managed, buffered routing to ClickHouse at fleet scale
OTel Collector at the edge — standard telemetry normalised into central observability pipeline
Fleet-scale observability — device connectivity, telemetry throughput, config version in ClickHouse
BC Manifests for edge tiers — offline-mode fallback and cloud-sync resumption as code
Blast radius management — config updates in controlled waves with pre-calculated impact modelling
Blockchain-anchored audit trails (optional) — Hyperledger Fabric for supply chain IoT compliance
AI inference at the edge — GPU-accelerated edge devices, model updates via GitOps
Edge incidents in native queue — device failures with fleet topology context auto-attached