AIOps System for Infrastructure Monitoring and Alerting

Standard microservice architectures generate hundreds of alerts per day. Engineers spend hours finding the root cause, while false positives reach 30%. Static thresholds and manual correlation are a guaranteed path to an alert storm. AIOps turns the chaos of metrics, logs, and traces into manageable incidents with minimal noise. We have implemented solutions for 30+ projects: MTTR is reduced by 3x, and the number of alerts by 80%. According to Gartner, organizations that have adopted AIOps reduce downtime by an average of 40%.

How AIOps automates alert noise reduction?

The system clusters events, analyzes time series, and builds dependency graphs—eliminating the need for manual filtering. One failure triggers an avalanche of notifications, but clustering via DBSCAN (temporal and semantic proximity) and a causal graph based on OpenTelemetry and Jaeger combine up to 30 alerts into a single incident with a probable root cause indicated.

Problems in monitoring that AIOps solves

Alert storm: when one failure triggers an avalanche of notifications

One incident causes hundreds of alerts from interconnected systems. The alert storm drowns the team in notifications while the engineer searches for the root. Our approach: alert clustering via DBSCAN (temporal and semantic proximity) and building a causal graph based on distributed traces. A cluster merges up to 30 alerts into one incident with a probable root cause. For building the dependency graph, we use OpenTelemetry and Jaeger, along with service configuration data from Kubernetes.

Why static thresholds don't work?

Threshold CPU > 80% triggers a false alarm at night during a batch job and misses the problem during the day at 75% with a rising trend. We use Prophet for seasonal metrics and EWMA for real-time adaptation. An anomaly is defined as a departure from the dynamic interval at a confidence level of 0.99. Comparison of methods:

Dynamic thresholds reduce false alarms by 6x compared to static ones.

Technical components of AIOps

Dynamic thresholds: Prophet and EWMA

from prophet import Prophet
import pandas as pd

def train_dynamic_threshold(metric_series, confidence_level=0.99):
    df = pd.DataFrame({
        'ds': metric_series.index,
        'y': metric_series.values
    })
    model = Prophet(
        seasonality_mode='multiplicative',
        weekly_seasonality=True,
        daily_seasonality=True,
        interval_width=confidence_level
    )
    model.fit(df)
    future = model.make_future_dataframe(periods=60, freq='5min')
    forecast = model.predict(future)
    return forecast[['ds', 'yhat_lower', 'yhat_upper']]

Alert correlation: DBSCAN and Causal Graph

from sklearn.cluster import DBSCAN
import numpy as np

def cluster_alerts(alerts_df, temporal_eps=300, spatial_eps=0.5):
    features = np.column_stack([
        alerts_df['timestamp'].astype(int) / 1e9,
        alerts_df['service_embedding'],
        alerts_df['severity_numeric']
    ])
    from sklearn.preprocessing import StandardScaler
    features_scaled = StandardScaler().fit_transform(features)
    clusters = DBSCAN(eps=0.5, min_samples=2).fit_predict(features_scaled)
    alerts_df['incident_cluster'] = clusters
    return alerts_df.groupby('incident_cluster').agg({
        'alert_id': 'count',
        'service': lambda x: x.mode()[0],
        'severity': 'max',
        'timestamp': 'min',
        'message': list
    })

Causal Graph for RCA

import networkx as nx

class ServiceDependencyGraph:
    def build_from_traces(self, traces):
        for trace in traces:
            for span in trace.spans:
                if span.parent:
                    self.graph.add_edge(span.parent_service, span.service, latency=span.latency)

    def find_root_cause(self, incident_services, anomaly_time):
        anomaly_set = set(incident_services)
        root_candidates = []
        for service in anomaly_set:
            ancestors = nx.ancestors(self.graph, service)
            if not ancestors.intersection(anomaly_set):
                root_candidates.append(service)
        return root_candidates

Predictive diagnostics: trends before an incident

We train a model on historical data: 30 minutes before an incident, precursors appear—rising error rate, p99 latency, CPU and memory trends. LogisticRegression provides a prediction with 85% precision and 78% recall. This allows us to respond before a service degrades. In one project, the predictive model prevented 60% of incidents, reducing MTTR from 45 to 15 minutes.

How is an AIOps system implemented?

Implementation happens in stages:

1. Infrastructure audit—collecting metrics, logs, traces, analyzing current alerts, and defining key metrics.
2. Data pipeline design—Kafka for streaming, ClickHouse for analytics, preparing data for ML.
3. ML model development—dynamic thresholds (Prophet, EWMA), clustering (DBSCAN), causal graph.
4. Integration with tools—Prometheus, Grafana, PagerDuty, OpsGenie, Slack, adjusting alerting.
5. Team training—documentation, workshops, knowledge transfer.
6. Warranty support—24/7 monitoring of ML components in the first two weeks.

What's included in the work

• Dynamic thresholds for 5+ key metrics (CPU, memory, p95 latency, error rate, disk I/O)
• Alert clustering and causal graph RCA for your architecture
• Predictive diagnostics of trends
• LLM-assisted incident analysis
• Integration with Grafana, PagerDuty, OpsGenie, Slack
• Documentation and team training
• Warranty support for ML components 24/7 for the first 2 weeks

To evaluate your infrastructure, order a preliminary audit—our engineers will prepare a roadmap within two days.

Results of AIOps implementation

Comparison of monitoring approaches

AIOps reduces false alarms by 6x compared to static thresholds. Full picture:

Reducing MTTR with AIOps

MTTR reduction is achieved through automatic alert clustering and causal graph: the average time to find the root cause drops from hours to minutes. Predictive diagnostics enable pre-incident response, reducing downtime. In a typical scenario, MTTR falls from ~90 minutes to 20–30.

Integration with existing tools

We connect to any stack: Prometheus, Grafana, Loki, Tempo, Kafka, Elasticsearch. Alert correlation is possible directly via PagerDuty Events API or Grafana AIOps Plugin. LLM incident analysis generates summaries and next steps in natural language.

Timelines and scope of work

Dynamic thresholds + alert clustering + Slack integration—from 4 weeks. Full cycle with causal graph, predictive diagnostics, and LLM—from 3 months. Cost is calculated individually after an audit. Operational cost savings can be significant for a medium enterprise.

Contact us for a preliminary assessment—our engineers will analyze your infrastructure and propose the optimal solution.