Capabilities

DEPLOYMENT MODELS

How BootCtrl supports edge-only, hybrid, and managed cloud deployment models for different customer needs.

Design intent

Use this lens when adopting DEPLOYMENT MODELS: define success criteria, start narrow, and scale with safe rollouts and observability.

Choose a model based on connectivity, regulation, and operational maturity
Keep the workflow snapshot-based across all models
Runbooks and rollback strategies are required regardless of architecture

What it is

Different plants require different architectures. BootCtrl is designed to run from edge-only installations to hybrid and managed SaaS models.

Design constraints

Choose a model based on connectivity, regulation, and operational maturity
Keep the workflow snapshot-based across all models
Runbooks and rollback strategies are required regardless of architecture

Architecture at a glance

Rollout plan defines ordering (canary → expand) and gating signals
Edge reports progress and failures with actionable categories
Rollback selects a known-good snapshot, not a best-effort reverse edit
This is a capability surface concern: reliability comes from process + signals

Typical workflow

Define rollout stages and gating signals (health/telemetry/behavior checks)
Start with a canary site that represents real conditions
Expand in batches; stop on regression; roll back to known-good snapshot
Post-rollout: capture learnings and update acceptance checks

System boundary

Treat DEPLOYMENT MODELS as a capability boundary: define what success means, what is configurable per site, and how you will validate behavior under rollout.

Example artifact

Rollout gates (conceptual)

gates:
  health: runtime_healthy && adapter_connected
  telemetry: gap < 10s && drops == 0
  behavior: acceptance_suite_passed
stages:
  - canary: 1 site
  - batch: 10 sites
  - fleet: all

What it enables

Start edge-only and scale to hybrid
Central governance with local autonomy
Clear upgrade paths for regulated environments

Engineering outcomes

Choose a model based on connectivity, regulation, and operational maturity
Keep the workflow snapshot-based across all models
Runbooks and rollback strategies are required regardless of architecture

Quick acceptance checks

Choose the model based on constraints: connectivity, regulation, and fleet scale
Keep the deployment workflow snapshot-based across all models

Common failure modes

No canary or weak canary that doesn’t represent production conditions
Missing gates: rollout continues despite degraded health signals
Rollback too slow or unclear ownership/criteria
Failure reasons not categorized, causing repeated incidents

Acceptance tests

Gate enforcement: rollout pauses/stops when health regresses
Failure categorization: errors are grouped into actionable buckets
Blast radius control: canary (DEPLOYMENT MODELS) shows issues before fleet expansion
Verify the deployed snapshot/version matches intent (no drift)
Run a canary validation: behavior, health, and telemetry align with expectations
Verify rollback works and restores known-good behavior

Deep dive

Practical next steps

How teams typically turn this capability into outcomes.

Key takeaways

Choose a model based on connectivity, regulation, and operational maturity
Keep the workflow snapshot-based across all models
Runbooks and rollback strategies are required regardless of architecture

Checklist

Choose the model based on constraints: connectivity, regulation, and fleet scale
Keep the deployment workflow snapshot-based across all models
Define rollback strategy and evidence capture regardless of architecture
Start small and expand once runbooks and gates are proven

Next steps

Common questions

Quick answers that help align engineering and operations.

Can we migrate between models over time?

Yes. Many teams start edge-only, adopt hybrid for fleet operations, and move to managed when they want to minimize control plane operations overhead.

What should remain consistent across models?

Immutable versioning (snapshots), progressive rollouts, and observable health/telemetry gates. Those are the safety foundations.

What is the first decision to make?

Whether cloud connectivity can be relied on. That determines how much orchestration/observability you can centralize vs keep local.