DevOps Consulting for IoT
Device fleets break assumptions web platforms never test: a hundred thousand clients that never close their connections, a power cut that sends them all reconnecting in the same minute, telemetry that arrives forever whether or not you are ready to store it, and software updates that ship to hardware you cannot SSH into when they go wrong. The platform layer — brokers, pipelines, time-series storage, staged rollout machinery, edge observability — decides whether the fleet scales or the fleet is the outage.
The broker becomes the bottleneck as the fleet grows
MQTT brokers that were fine at ten thousand devices hit connection and memory ceilings at a hundred thousand — and a regional outage triggers a reconnect storm that takes down whatever the outage did not.
How we fix it — Clustered broker architecture on Kubernetes sized from measured per-connection cost, connection load tests that include the reconnect-storm case, rate limiting and jittered backoff enforced at the platform edge, and autoscaling on connection count and message throughput rather than CPU.
Telemetry volume grows forever; the pipeline and the bill grow with it
Every device added is permanent write load. Ingestion lags silently, dashboards fall minutes behind reality, and time-series storage kept at full resolution forever becomes one of the largest lines on the invoice.
How we fix it — Ingestion with buffering and backpressure so bursts queue instead of dropping, consumers that autoscale on lag, freshness SLOs with alerting, and a deliberate retention ladder — full resolution while data is hot, downsampled history, cold archive — so storage cost tracks value, not habit.
OTA updates can brick hardware you cannot reach
A bad firmware or config rollout does not roll back like a web deploy — it strands devices in the field, and recovery means truck rolls, RMAs and customer calls. One bad push can hurt more than a year of outages.
How we fix it — Deployment discipline applied to the fleet: cohort-based staged rollouts starting with canary devices, health telemetry gating each expansion step, automatic halt when check-in or error rates move, and rollback channels rehearsed before they are needed. The pipeline assumes a bad build will eventually ship — and makes it a non-event.
The fleet is a black box between support tickets
A thousand devices drop offline and nobody can say whether it is a firmware bug, a carrier outage or a broker problem — the support queue is effectively your monitoring system.
How we fix it — Fleet-level observability as a first-class system: connectivity, check-in rates, firmware-version distribution and error rates sliced by cohort, region and hardware revision, with alerts on fleet-shaped anomalies — offline-rate spikes, version skew, regional drop-offs — instead of per-device noise.
Reliability at fleet scale, from edge to pipeline.
IoT platforms are judged on unglamorous guarantees: connections stay up, data is not lost, updates do not brick devices. Each one is an infrastructure property you can engineer and test — and buyers of connected products increasingly ask you to prove all three.
- Connection-storm testedBrokers and ingestion load-tested for the worst case — the whole fleet reconnecting at once — before the fleet does it for real.
- No-loss pipelinesBuffered, backpressured ingestion with lag SLOs — bursts queue, nothing silently drops, and freshness is a monitored number.
- OTA with a safety netCanary cohorts, telemetry-gated expansion and automatic halt — rollout machinery built for hardware you cannot reach.
- Device identity done rightPer-device certificates with mTLS and a real rotation story — no shared credentials baked into firmware images.
Two free tools, no signup: estimate your cloud waste with the cost calculator, or score your production posture on the security scorecard. Fixed-scope packages and prices are on the pricing page.
Frequently asked
Do you work with AWS IoT Core / Azure IoT Hub, or self-hosted brokers?
Both. Managed IoT platforms remove broker operations but leave you the ingestion pipeline, time-series storage, device CI/CD and observability; self-hosted brokers like EMQX or VerneMQ add cluster operations on top. We build the platform either way — and can put real numbers on the crossover point where self-hosting starts paying for its own complexity.
Can Kubernetes handle hundreds of thousands of persistent connections?
Yes, with tuning that stateless web workloads never need: connection-aware load balancing, node and kernel limits raised deliberately, brokers run as stateful sets with careful disruption budgets, and scaling signals based on connections and throughput rather than CPU. It works well — but the defaults will not get you there.
What does DevOps have to do with firmware updates?
OTA is a deployment pipeline with worse failure modes, and it deserves the same discipline as any release system: versioned artifacts, staged rollouts, health-gated promotion, automatic halt and rehearsed rollback. We build that machinery and its telemetry — your embedded team owns the firmware, the platform makes shipping it safe.
Part of our stack runs on gateways at customer sites. Can that be managed properly?
Yes — lightweight Kubernetes distributions like K3s make an edge gateway a manageable deployment target: GitOps-driven configuration, store-and-forward for unreliable links, and the same observability stack as the cloud side. An edge site becomes a location in your platform, not a bespoke snowflake.
Scale the fleet without becoming the outage.
A free audit of your device platform — broker capacity, pipeline resilience, OTA safety and time-series cost — with a prioritized fix list before the next hundred thousand devices arrive.
NO SIGNUP · NO OBLIGATION · REPORT IS YOURS TO KEEP