CPU, memory and disk watched
before they become an outage
Uptime monitoring tells you a service is down after it already is; infrastructure monitoring is supposed to catch the disk filling up or the memory leak building for three days beforehand, and most teams only glance at these dashboards after something has already gone wrong. We build an agent that watches the resource-level signals continuously and flags the trend before it becomes an outage.
The process today
Most teams have a monitoring dashboard, and most teams check it when something is already wrong, not as a habit. CPU, memory and disk usage climb slowly in the background: a log file nobody rotates, a cache that never gets evicted, a queue that backs up by a few hundred messages every day. None of that trips an uptime alert, because the service is still responding, right up until the disk hits 100% or the memory limit gets hit and the process is killed.
The second cost is that when the resource limit finally gets hit, it usually takes down something unrelated to whatever caused the slow climb, a deploy fails because there is no disk space left for the build, a database connection pool exhausts because memory pressure is forcing swaps, and the person debugging it has to work backward from a confusing symptom to a root cause that had been building for days.
The third is capacity planning done by guesswork: scaling up a server because someone eyeballed a dashboard and it looked tight, or not scaling until an outage forces the decision, instead of a clear forecast of how many days are left at the current trend.
What the agent does
The agent pulls CPU, memory, disk and queue depth metrics continuously from every server or cluster in scope, learns a baseline per server over the first one to two weeks, including normal peak-hour variation, and then watches for trends that deviate from that baseline rather than firing on raw thresholds alone. A disk filling three percentage points faster than usual, a memory curve that never comes back down after a deploy, a queue that is growing rather than draining, all get flagged with a forecast: days remaining at the current rate, and what changed recently that might explain it, a deploy, a traffic pattern shift, a cron job that started failing silently.
Alerts are routed by server and severity, so a slow trend on a staging box does not page the same person as a production disk about to fill in six hours. A weekly summary rolls up the state of the whole fleet in one message instead of requiring anyone to check individual dashboards. Typical integrations: Prometheus, Grafana, Datadog or a lightweight agent we install directly, with alerts to Slack, Telegram or PagerDuty.
What stays with humans
Deciding to scale a server up, migrate a service, or accept a known tight resource as a temporary trade-off stays a team decision; the agent’s job is to make sure that decision gets made on a forecast instead of in a panic. Root-causing exactly why memory is leaking in a specific service still needs an engineer; the agent narrows down when the trend started and what changed around that time, which is most of the work of finding the cause.
Guards
Every alert is logged with the metric, the trend and the forecast that triggered it, so a team can review after the fact whether the call was right. Baselines are recalculated on a rolling window rather than fixed once, so a deliberate capacity change does not keep triggering false alerts forever. A kill switch quiets alerting for a specific server during planned maintenance without disabling monitoring for the rest of the fleet, and a full off-switch is available in one message.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $700 | One server or small cluster, baseline learning, trend alerts, weekly summary | 4 to 8 days |
| Department package | from $2,000 | Infrastructure monitoring plus SSL and domain expiry monitoring and cloud cost monitoring | 2 to 4 weeks |
Running cost is usually $10 to $35 a month in model usage on top of whatever metrics stack you already run.
Related
This pairs well with uptime monitoring for the external, service-level view, and with cloud cost monitoring since resource trends and cost trends are usually the same underlying story. For the alerting that happens inside your application rather than at the infrastructure layer, see exception triage and assignment. Full package details are on the AI agents service page and the automation-everything overview; for infrastructure we monitor on our own products, see the ProBay AI agent team case study and the secure infrastructure case study.
Want a warning days before a server runs out of room, not an outage the day it happens? Get in touch and we will look at your current fleet.
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FAQ
How is this different from uptime monitoring?
Uptime monitoring tells you a service is down right now. This watches the resource trends underneath, CPU, memory, disk, queue depth, so you catch the slow leak or the filling disk days before it causes the outage uptime monitoring would eventually detect.
How much does infrastructure monitoring cost to set up?
From $700 for a single server or small cluster, live in 4 to 8 days. A full fleet across multiple environments usually runs $1,200 to $2,000.
Which infrastructure does it watch?
Bare VPS instances, Docker and Kubernetes clusters, managed database instances, and queue systems like Redis or RabbitMQ, through whatever metrics agent or exporter you already run, or one we set up.
Will it flag normal traffic spikes as a problem?
It learns a baseline per server over the first week or two and tunes thresholds against your actual patterns, including expected peak hours, so a normal Friday traffic spike is not treated the same as an unexplained climb.
What happens when it predicts we will run out of disk?
It alerts with the forecast, days remaining at the current rate, and the likely cause if one is visible in the logs, so your team can clean up or scale before it becomes an incident instead of after.