The OpsRamp platform consists of multiple layers working together to enable full-stack observability and automated IT operations. These components are structured to work seamlessly and interact with one another to provide IT operations teams with a centralized, real-time view of their infrastructure. Below is a detailed breakdown of the different components of OpsRamp and how they are structured and connected:

Discovery and Monitoring Layer

The Discovery and Monitoring Layer is responsible for identifying and continuously tracking IT resources across hybrid cloud and on-premises environments.

  • OpsRamp Gateway (Agentless Monitoring) – Discovers cloud instances, network devices, and storage systems without requiring local agents.
  • OpsRamp Agents (Agent-Based Monitoring) – Installed on virtual machines and containers for deep monitoring.
  • APIs and Protocols – Uses SNMP, REST, OpenTelemetry, and Prometheus for integration with third-party tools.

Discovered assets are automatically added to the OpsRamp inventory.

Data Collection and Ingestion Layer

Once resources are discovered, OpsRamp starts collecting data. This data is then ingested into the cloud for further analysis.

  • OpsRamp Gateway & Agents – Collect infrastructure and application-level data.
  • Third-Party Data Sources – Supports Prometheus, OpenTelemetry, and CloudWatch integrations.
  • Data Pipelines – Securely transmits collected data to the OpsRamp Cloud.

The OpsRamp Gateway aggregates and transmits data to OpsRamp’s cloud analytics platform. Data pipelines ensure secure and efficient transport, preventing data loss or corruption.

AI-Driven Data Analytics Engine

OpsRamp’s analytics layer processes vast amounts of data using AI-driven insights, event correlation, and automated anomaly detection.

  • Machine Learning Models – Identifies performance bottlenecks and predicts outages.
  • Event Correlation Engine – Groups related alerts to minimize noise.
  • Incident Analysis Dashboard – Provides deep insights into system health and performance.

AI models process historical and real-time data to detect patterns, anomalies, and potential failures. OpsRamp’s Root Cause Analysis Engine identifies dependencies between alerts, reducing MTTR (Mean Time to Resolution).

Automation and Remediation Layer

The final step in the OpsRamp deployment model involves taking proactive or automated remediation actions to resolve IT incidents.

  • Automation Workflows – Executes scripts and predefined actions to resolve incidents.
  • ITSM Integrations (ServiceNow, BMC Remedy) – Automatically escalates unresolved issues.
  • Infrastructure-as-Code (Terraform, Ansible) – Applies automated configuration changes and patches.

When an issue is detected, OpsRamp triggers an automated response based on pre-configured workflows. If automation cannot resolve the issue, OpsRamp creates an ITSM ticket, ensuring IT teams take immediate action.

Data Flow Across OpsRamp Services

OpsRamp’s architecture follows a logical workflow that starts with resource discovery, followed by data collection and processing, leading to real-time analytics, and culminating in automated remediation.

  1. Assets are discovered and monitored using OpsRamp Gateway and Agents.
  2. Data is collected and transmitted to OpsRamp’s cloud data pipelines.
  3. AI-driven analytics process the data, identifying anomalies and potential risks.
  4. Automated remediation is triggered based on predefined workflows or escalations to IT teams.