Executive summary
This guide gives solution architects a pragmatic framework to decide what runs at the edge, what belongs in the cloud, and how to design hybrid systems that don’t crumble under real-world constraints (latency, data gravity, offline tolerance, compliance, and cost). You’ll get a decision matrix, reference architectures, cost model cues, and a build checklist you can apply immediately.
TL;DR — Decision matrix
| Workload trait | Edge | Cloud | Hybrid (edge + cloud) |
|---|---|---|---|
| Tight latency (human/perception or control loops ≤50 ms) | ✅ Vision/controls, AR/VR, robotics | ❌ | ✅ Edge for loop, cloud for coordination |
| Intermittent/expensive connectivity | ✅ Local processing & caching | ❌ | ✅ Sync deltas to cloud when available |
| Data residency / privacy-by-design | ✅ Process/filter locally | ❌ | ✅ Redact/summarize at edge, store raw locally, publish features to cloud |
| Burst scale / global access | ❌ | ✅ Web/mobile apps, API backends, analytics, SaaS | ✅ Edge precompute + cloud distribution |
| ML training / heavy analytics | ❌ | ✅ GPU clusters, data lakes, model training | ✅ Edge inference + cloud training |
| Safety-critical / operational continuity | ✅ Keep running when WAN fails | ❌ | ✅ Local-first, cloud-supervised |
| Cost dominated by backhaul egress | ✅ Reduce uplink | ❌ | ✅ Tiered retention (hot at edge, warm in cloud) |
| Device/OT integration (PLCs, sensors) | ✅ Direct protocols & timing | ❌ | ✅ Cloud twin + edge adapters |
One-liners:
- If your SLA is in milliseconds or your site must survive WAN loss, put the decision + action at the edge.
- If your SLA is human-scale and you need elastic scale or global reach, anchor in the cloud.
- Most real systems are hybrid: edge for low-latency & privacy, cloud for model training, fleet control, analytics, and integration.
A three-question decision tree
- What’s the latency budget to a “useful” action?
- ≤50 ms → Edge compute.
- 50–200 ms → Edge preferred, or hybrid with local cache/hints.
- 200 ms → Cloud acceptable.
- What happens when the WAN is down?
- Must keep operating safely → Edge-first (local state + durable queues).
- Can degrade or pause → Hybrid with retries/backpressure.
- Can stop → Cloud.
- What data can legally/ethically leave the site?
- Raw PII/PHI/OT telemetry restricted → Process at edge; publish redacted features.
- Aggregates/learned features OK → Hybrid.
- No restriction → Cloud.
When the edge wins (patterns)
- Perception-to-action loops: machine vision QC, cobots, AMRs, AR-guided picking.
- Local survivability: retail POS, manufacturing cells, energy microgrids, hospitals, ships, mines.
- Bandwidth economics: video analytics, high-frequency telemetry; send events, not raw streams.
- Privacy/regulatory: on-site PII minimization; compute-to-data rather than data-to-cloud.
- Protocol gravity: direct OT/fieldbus integration, deterministic scheduling, GPS-denied ops.
Tactics: local state machines; prioritized queues; read-optimized stores; signed/attested workloads; OTA updates with staged rollouts.
When the cloud wins (patterns)
- Global scale & burst: consumer apps, partner APIs, data products.
- Model training & analytics: GPU farms, lakehouse ETL, feature stores, experiment tracking.
- Cross-organization integration: IAM brokering, billing, observability, compliance reporting.
- Any workload that benefits from managed services (databases, pub/sub, serverless) and isn’t latency-sensitive.
Tactics: multi-region active/active, managed queues & functions, autoscaling, policy-as-code.
Hybrid that actually works (reference patterns)
1) Cloud control plane + edge data plane
- Edge: containers/wasm orchestrated locally (k3s/micro-k8s/wasm runtime), processing sensors/cameras, caching configs/models, durable queues.
- Cloud: fleet registry, desired-state config, model registry, analytics, monitoring, and CI/CD.
- Sync: delta uploads (features, events), batched with backpressure and idempotent retries.
2) Digital twin with tiered storage
- Edge: time-series hot store (hours–days), local OLAP for quick dashboards.
- Cloud: lakehouse for months–years, BI/ML, cross-site benchmarking.
- Policy: retention tiers; redact at source; encrypt-in-use where feasible.
3) Edge inference + cloud training
- Edge: INT8/FP16 optimized models, hardware accelerators, sliding window inference.
- Cloud: training/finetuning, evaluation, A/B, shadow testing, rollout gates.
- Safety: canary % at edge, fallback to last-known-good, staged ring deployments.
Security & compliance blueprint (edge-first zero trust)
- Device identity & attestation: each node has a unique identity; verify measured boot; only run signed artifacts.
- mTLS everywhere: mutual auth for device–cloud and device–device; short-lived certs, automated rotation.
- Secrets & SBOM: hardware-backed secrets (TPM/TEE); maintain SBOM and block on critical CVEs.
- Network posture: least-priv egress, deny inbound by default, microsegments per function.
- Data zones: classify raw/PII, features/aggregates, and telemetry; apply different movement policies.
- Observability with privacy: redact at collector; field-level encryption; store raw only where mandated.
- Ops hardening: OTA with signed bundles, staged rings (lab → canary site → 10% → 100%); automatic rollback.
Reliability & SRE considerations
- Define SLIs per site: p95 decision latency, successful actuation %, data freshness, sync lag.
- Backpressure & queues: never drop; persist locally; retry with exponential backoff; design idempotent consumers.
- Offline-first UX: explicit degraded modes; local cache of policies/ML models; split-brain protection.
- Chaos & drills: pull WAN, kill nodes, corrupt queues—prove your fail-safes.
- Capacity at the edge: plan CPU/GPU headroom for spikes + model upgrades.
Cost model cues (how to avoid surprises)
- Backhaul math beats list prices: Egress + cellular links often dwarf edge compute costs.
- Right-size retention: store raw briefly; keep aggregates/features longer.
- Placement ROI trigger: move compute to the edge when (egress_cost + downtime_cost + privacy_penalty) > (edge_hw + ops).
- Lifecycle TCO: include truck rolls/remote hands, spares, and device MTBF.
- Accelerators: prefer power-per-inference over raw TOPS; measure $/k inference.
Reference architectures (industry-flavored)
Retail store analytics
- Edge: camera ingestion → person/product detection → event stream to POS; local rules for queue alerts; storewide cache.
- Cloud: fleet configs, dashboard, anomaly detection, retraining.
- Data movement: send counts/heatmaps; upload snippets on exceptions.
Manufacturing cell
- Edge: PLC adapters, time-sync, vision QC, robotic control; local historian (24–72 h).
- Cloud: twin-of-twins, predictive maintenance, cross-plant KPIs.
- Safety: deterministic scheduling; WAN loss tolerates full-rate production.
Media/streaming or gaming
- Edge: packaging, watermarking, matchmaking, CDN edge functions.
- Cloud: origin, libraries, billing, anti-fraud/anti-cheat analytics.
- Latency target: ≤30 ms RTT within metro; precompute variants at edge.
Smart city / transport
- Edge: roadside units, sensor fusion, priority signals; secure V2X.
- Cloud: policy, coordination, simulation, planning.
- Connectivity: mesh/5G with store-and-forward.
Build checklist
Foundation
- Define latency budgets & offline behavior per use case
- Classify data zones; write movement policies
- Choose runtimes (containers/wasm), OTA channel, and fleet manager
Networking
- Private egress only; mTLS; DNS controls
- Local broker (MQTT/NATS/Kafka) + durable storage
- Bandwidth shaping, QoS, and compression
Data & ML
- Edge time-series DB; retention tiers
- Feature extraction at edge; drift monitors
- Model registry + signed artifacts; staged rollouts
Security
- Device identity & attestation; signed images
- Secrets in hardware; SBOM & CVE gates
- Microsegmentation; policy-as-code
Observability & Ops
- Metrics/traces/logs with redaction
- Health probes, watchdogs, self-healing
- Runbooks & chaos tests; rollback verified
Anti-patterns to avoid
- Shipping raw video to the cloud “for analytics.” Convert to events at the edge.
- Treating sites as cattle without local autonomy. Edge needs brains, not just buffers.
- Static configs. Everything drifts—use a desired-state control plane and closed-loop reconciliation.
- Single-queue failure. Use multi-tenant topics and backpressure-aware producers.
- Un-signed updates. No artifact should run without signature verification.
Vendor evaluation questions
- How do you prove attestation and artifact signature at the edge?
- What’s the rollback story if a fleet update goes bad?
- How do you handle offline-first (queuing, conflict resolution, replay)?
- What’s your SBOM process and CVE gate?
- Can we set data-movement policies by type (raw/features/telemetry) and audit them?
- What’s the observability footprint and bandwidth of your agents?
- How do you support staged deployments and A/B at the edge?
Wrap-up: What runs where
- Edge: anything that must be fast, private, and resilient to WAN loss—vision/controls, POS, OT, safety-critical loops.
- Cloud: anything that must be global, elastic, and integrated—APIs, analytics, ML training, user identity, cross-site orchestration.
- Hybrid: almost everything else—edge for decisions, cloud for context.
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