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AI-Powered Anomaly Detection and Reporting in Grafana Open Source

utkarsh-pandey

February 26th, 2025 - 3 mins Read

In modern monitoring environments, anomaly detection is critical for ensuring system reliability. However, traditional threshold-based alerting in Grafana Open Source often generates noise or misses subtle issues.

By combining Grafana Open Source with AI models and reporting automation, teams can detect real anomalies, reduce alert fatigue, and distribute meaningful insights across the organization.

In this guide, you’ll learn how to:

Set up AI-powered anomaly detection using open-source ML libraries
Integrate detection results into Grafana dashboards
Create and automate anomaly detection reports in PDF or Excel
Share these reports via email or Slack using tools like Skedler

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Why Grafana Open Source Needs AI for Anomaly Detection

Grafana’s native alerting is based on fixed thresholds. This approach can’t adapt to changing baselines, seasonality, or complex usage patterns.

⚠️ Limitations of Manual Thresholds

Frequent false positives from minor fluctuations
Missed anomalies during low-activity periods
Manual tuning is time-consuming

✅ Benefits of AI-Based Detection

Learns patterns from historical data
Detects deviations intelligently
Reduces noise and improves signal quality

Prerequisites: Tools You’ll Need

To implement AI-powered reporting in Grafana Open Source, make sure you have:

Grafana (v8+) Open Source Edition
Time-series database (e.g., Prometheus, InfluxDB, Elasticsearch)
Python (3.8+), with libraries: scikit-learn, Prophet, or TensorFlow
Grafana JSON API Plugin (to fetch results from your ML model)
Reporting tool like Skedler to generate and send PDF/Excel reports

Step 1: Build an AI Anomaly Detection Model

Using Python, train a model on your metric data. Example: Isolation Forest.

from sklearn.ensemble import IsolationForest
model = IsolationForest(contamination=0.01)
model.fit(metric_data)
predictions = model.predict(metric_data)

You can also use Prophet or LSTM models depending on your data type.

Step 2: Deploy the Model with an API

Serve the model predictions through a Flask API:

@app.route('/predict', methods=['POST'])
def predict():
    # Input: list of values
    # Output: anomaly flags

This API will serve data to Grafana via the JSON plugin.

Step 3: Integrate with Grafana Open Source

In Grafana:

Install the JSON API Plugin
Add the Flask API as a data source
Create dashboards overlaying actual metrics with anomaly flags
Use color-coded visualizations for better interpretation

Step 4: Create Anomaly Detection Reports in Grafana

While Grafana Open Source lacks native reporting, you can use Skedler to:

Export dashboards to PDF or Excel
Schedule reports at intervals (hourly/daily/weekly)
Add custom branding, filters, and report conditions
Deliver reports via Email, Slack, Webhooks, or SFTP

📌 Example Use Case:

Automatically email a daily anomaly report on CPU and memory metrics for your production servers to your NOC and DevOps teams.

Step 5: Automate Retraining and Report Delivery

To keep reports relevant:

Retrain ML models periodically
Update prediction APIs
Reschedule reports via Skedler with the updated dashboards

As a result, you’ll have a fully automated pipeline:

Data → AI Detection → Grafana Visualization → Scheduled Report

Why Use Skedler for Grafana Open Source Reporting?

Skedler bridges the reporting gap in Grafana OSS:

No need for costly enterprise licenses
Works seamlessly with your existing dashboards
Supports audit trails, SLA compliance, and stakeholder reporting

Without Skedler, creating reports means manually exporting screenshots or CSVs.
With Skedler, reports are created, styled, and delivered automatically.