Watchtower
Uptime monitoring with incident-based detection and a cost-aware multi-model AI layer that cut operating cost ~60%.
- Node.js
- Background workers
- SMTP
- Multi-model AI
Naive monitors alert on every failed check and bury real incidents in noise. And once you bolt AI features onto a monitoring system, cost explodes if every task hits a frontier model — most of them don't need one.
Watchtower had to detect genuine incidents (not blips), alert reliably on both downtime and recovery, and keep the AI layer cheap without sacrificing quality.
Incident-based monitoring engine
Automated HTTP health checks run through secure background workers. Detection is incident-based rather than per-check, so a single flaky response doesn't page anyone — and email alerts fire on both downtime and recovery so on-call always knows the current state.
Cost-aware multi-model AI routing
The AI layer routes each task by complexity: frontier models (Claude 4.8) are reserved for hard reasoning, while routine parsing is delegated to cheaper models (Claude 4.6, GPT-4o-mini). A memory/caching layer eliminates redundant calls entirely.
Together these cut AI operating cost by roughly 60% with no measurable drop in output quality.
- 1Scheduler
enqueues health-check jobs
- 2Secure background workers
run HTTP checks
- 3Incident engine
distinguishes blips from real downtime
- 4SMTP alerts
downtime + recovery notifications
- 5AI router
complexity-based model tier + memory/cache
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