Today's multi-hour outage of the Lindy platform has, predictably, disrupted a significant number of automated workflows. While the status page cites "database connectivity issues," the broader implication for any team relying on Lindy as a primary automation layer is a stark reminder of single points of failure. This incident prompts a critical architectural question: what is your operational backup plan when your primary automation orchestrator becomes unavailable?
From an SRE and observability perspective, the failure of a central automation tool creates a dual problem. First, there is the immediate loss of business logic execution (scheduled reports, customer onboarding sequences, data syncs). Second, and more insidiously, there is the potential loss of visibility and incident response capabilities if your alert routing, escalation, or remediation runbooks are themselves hosted and executed within the same failed platform.
I propose we move beyond generic discussions of "having a backup" and delve into concrete, implementable strategies. My current environment's approach is based on a principle of redundancy at the *orchestration layer*, not just the infrastructure layer. For critical workflows, we maintain parallel, simplified definitions in a second system. Consider the following tiered strategy:
* **Tier 1 (Critical - PagerDuty-level alerts, automated remediation):** These are defined in both Lindy *and* a failover system. We use a lightweight, cron-driven Kubernetes `Job` schema that can be triggered via a webhook or a simple schedule. The logic is kept in a containerized script.
* **Tier 2 (Important - Scheduled business logic, data pipelines):** These remain primarily in Lindy, but we have a manual runbook documented (not in Lindy!) that allows for a one-click execution via a CI/CD pipeline (e.g., GitHub Actions, GitLab CI) as a stopgap.
* **Tier 3 (Non-critical):** Tolerate downtime, accept queueing until service restoration.
For implementation, here is a simplified example of a Tier 1 failover mechanism for a critical "database disk usage" alert that would normally trigger a Lindy cleanup workflow. This exists as a Kubernetes `CronJob` manifest, disabled by default, which we can enable via a Kustomize overlay or `kubectl patch` during an outage:
```yaml
apiVersion: batch/v1
kind: CronJob
metadata:
name: critical-db-cleanup-failover
namespace: automation-backup
spec:
schedule: "*/15 * * * *" # Runs every 15 minutes
suspend: true # Manually unsuspend during Lindy outage
jobTemplate:
spec:
template:
spec:
containers:
- name: cleaner
image: ourorg/script-runner:latest
command: ["/scripts/clean_old_db_data.py"]
env:
- name: DB_HOST
valueFrom:
secretKeyRef:
name: db-creds
key: host
restartPolicy: OnFailure
```
The key metrics we monitor to decide on a failover activation are:
1. Lindy's own status page API (HTTP endpoint health check).
2. A synthetic transaction that attempts to execute a no-op Lindy workflow every minute (via its API). We alert on 3 consecutive failures.
3. A spike in our internal error logs for "WorkflowExecutionFailed" sourced from the Lindy integration.
This setup requires discipline in maintaining dual definitions, but the cost is justified for truly critical paths. I'm keen to hear how others are architecting for this specific failure mode. Are you employing a different tool for backup orchestration (e.g., Apache Airflow on standby, AWS Step Functions as a secondary), or have you built in-house resilience patterns? Furthermore, how are you monitoring the health and statefulness of workflows that may be interrupted mid-execution during such an outage?
Totally feel this. The moment your alerting depends on the platform that's down is when things get truly scary. Redundancy at the orchestration layer is the key, like you said.
We've had success with a simple, boring pattern for critical cron-like workflows: a tiny, self-contained FastAPI service in a separate environment (even just a different cloud region) that does nothing but expose a health endpoint and a "force run" endpoint for key jobs. The scheduler? A basic cloud scheduler calling that endpoint, with retries and a dead-letter queue. If our main orchestrator (Prefect in our case) is up, it handles the logic. If Prefect's API is unreachable, the fallback service gets the scheduler ping and runs a stripped-down, essential version of the workflow directly.
It's not elegant, but it keeps the absolute must-have processes like daily financial summaries or incident paging alive. The hard part was deciding what made the cut for the "bare metal" fallback list.
Totally agree about redundancy at the orchestration layer being key. The dual loss of business logic *and* visibility you mentioned is the real kicker. We got bitten by that years ago.
Our twist on this is maintaining a separate, dead-simple "safety rail" orchestrator using a different vendor. For us, it's Pipedream as a backup to our main N8N instance. They host the logic for about 20 mission-critical workflows, like daily financial reconciliation and outage alerts. The trick is we keep those workflows dormant and trigger them via a webhook from our own scheduler (a trivial cron job on a separate VPS). If our primary platform's API is healthy, it intercepts the webhook and runs the full, fancy workflow. If the primary is dead, the webhook fails through to Pipedream, which runs a bare-bones version.
It's not cheap, paying for two platforms, but the ROI on avoiding a complete operational blackout during an outage like today's is insane. You just have to be ruthless about what qualifies as "critical" enough to duplicate.
Try everything, keep what works.
That "dual platform" strategy is a sound architectural move, especially if your observability depends on the orchestrator itself. The financial reconciliation use case you cited is perfect for this; you can't lose that data pipeline just because your main automation dashboard is red.
Your point about "ruthless" qualification is crucial. The operational burden isn't just cost, it's maintaining logic parity. We've found you need automated smoke tests for the backup workflows, triggered weekly, to confirm they still function. Otherwise, you're just paying for a second failure mode.
One caveat on the webhook failover: you need to ensure your primary's intercept logic doesn't create a single point of failure. If that "intercept" service is co-located with your main N8N instance and the whole zone goes down, your webhook never gets diverted and the backup won't fire. The scheduler and the intercept logic must be on separate, resilient infrastructure from the primary orchestrator.
null
I like your distinction between infrastructure redundancy and orchestration redundancy. It's easy to have your data and apps spread across zones but still have all your automation logic funneled through a single control plane.
Your point about losing *both* execution and observability is the nightmare scenario. It makes me think teams should qualify their workflows on two axes: business criticality and observability dependency. A workflow that triggers an alert about the outage itself has to have a completely separate, dumb notification path - something as simple as a cron-scripted curl to a PagerDuty API, completely outside the main automation platform.
The "ruthless qualification" someone mentioned later is the hard part. You can't back up everything, so you need a clear, maybe even automated, criteria for what gets a secondary orchestrator. Is it revenue-impacting? Is it a compliance-mandated process? If not, it might just have to wait until the platform comes back.
Yeah, the two-axis qualification you mentioned is spot on. We actually built a quick spreadsheet for this after a similar scare.
We score workflows 1-5 on business impact and observability dependency. Anything that scores high on both (like a compliance audit trail or a critical payment webhook) gets the dual-platform treatment. Medium scores might just get a simplified cron fallback. Low scores ride it out.
The automated criteria idea is interesting. We haven't gone that far, but we do run a monthly script that checks our "backup essential" list against recent logins and API calls to the secondary orchestrator. If a workflow hasn't been touched in 90 days, it flags us to re-evaluate if it's still truly critical.
It's a bit manual, but it prevents backup logic from rotting.
Data > opinions
Scoring on impact and observability dependency is the right move. We do something similar but with a third axis: dependency churn. If the workflow's logic or data sources change frequently, maintaining a backup becomes a maintenance trap. We disqualify it from dual-platform, even if it's critical, and push for making the primary platform more resilient instead.
Your monthly script checking for activity is smart. We added a similar check but against our primary platform's change logs. If a critical workflow hasn't been modified in over 6 months, its backup is probably stable enough to be fully automated. That's when we shift the backup from "active mirror" to a simple, version-locked container that just runs.
Ship fast, review slower
Great framing of the problem - losing both execution *and* observability because they're in the same box is a brutal double-whammy.
We treat our backup plan like a circuit breaker. Critical alerts, for example, have a completely separate "dumb" path. If our main orchestrator is down for more than 5 minutes, a secondary monitoring system triggers a script that posts directly to Slack and PagerDuty via their APIs. No logic, just a blunt notification that the primary system is offline.
This way, the team isn't blind while we fight the main outage. It's not a full workflow backup, but it keeps visibility alive.
Clean code, happy life
That spreadsheet approach is so practical, it's exactly how we got our leadership on board with the redundancy effort. Putting a number on the risk makes the conversation objective.
Your point about the monthly script is key - it turns a good intention into an operational habit. We took it a step further and integrated that check into our incident postmortem template. If a critical workflow fails and its backup hasn't been smoke-tested within the SLA window (for us, 30 days), that's a finding. It directly ties the maintenance work to real outage impact.
Adding a third axis for dependency churn, like user957 mentioned, is a natural evolution of your model. We found that high-churn workflows were where our backup logic rotted fastest.
ship early, test often
You're right about the double loss of execution and visibility. That's why our fallback targets the observability piece first.
We run synthetic checks from a completely separate provider that constantly pings our main orchestrator's health endpoint. If it's down for two consecutive cycles, that check triggers a webhook to a tiny, static S3-hosted HTML page. That page is our "war room" - it lists the manual runbooks for our top five critical jobs, hosted as raw markdown files in another repo. It's ugly, but it gives us a checklist outside the failed system.
The key metric for us is time-to-consensus, not time-to-restore. If the team knows what to do manually within 60 seconds, the backup plan works.
Numbers don't lie
Exactly right on the separate notification path. That's our rule zero.
We treat it as a hard dependency. Any workflow that is *about* system health cannot depend on that system. Our primary orchestrator's own uptime alert is a bash script on a Raspberry Pi in someone's closet, hitting the status API and sending SMS via Twilio. It's idiotic, but it's worked twice when our fancy Grafana/PagerDuty chain was dead because the orchestrator was dead.
Your automated criteria thought is the logical next step, but we found the implementation cost isn't trivial. We settled on a tagging system in our workflow definitions: `backup: none | dumb | full`. The `dumb` tag triggers a CI job that validates the workflow has a corresponding cron script or static function in our backup repo. No script? PR can't merge.
Build once, deploy everywhere
Good pattern. We use similar for payment retries.
Our cut-off metric: if the workflow downtime costs >$10k/hour, it gets a fallback. That's about five jobs. Everything else can wait.
The stripped-down version is key. Ours just logs to S3 and fires a webhook, no transformation. If the backup logic gets complex, you've rebuilt your orchestrator.
Metrics don't lie.
I like that dollar-per-hour cutoff - it forces a concrete business decision instead of technical hand-waving.
>The stripped-down version is key.
Absolutely. Our rule of thumb is the backup should be something a junior engineer could rebuild in a panic from a one-page spec. If it needs more, the primary system isn't resilient enough.
We slipped once by letting a "simple" backup accumulate retry logic and error grouping. Took longer to debug than the outage itself 😅
Data is the new oil - but it's usually crude.