Vitria versus Observability and Event Intelligence platforms
These comparison tables highlight key differences between Vitria, EIS platforms and Observability solutions.
| Capability | Vitria | EIS | Observability |
|---|---|---|---|
| Full Stack AIOps | Vitria: 100% | EIS: 20% | Observability: 0% |
| Performance Management | Vitria: 100% | EIS: 0% | Observability: 50% |
| Fault Management | Vitria: 100% | EIS: 75% | Observability: 50% |
| Change Management | Vitria: 100% | EIS: 25% | Observability: 50% |
Full Stack Capabilities
Vitria’s VIA AIOps delivers the capability to manage faults, performance, and change within and across the network, infrastructure (in-house and cloud), and applications automating monitoring, incident detection, root cause analysis, and remediation across the service delivery ecosystem.
Observability systems don’t support the end-to-end service delivery ecosystem across the technology stack and EIS systems are typically focused on the IT domain.
Performance Management
VIA AIOps and Observability platforms ingest performance data in the form of KPIs along with telemetry data such as logs, metrics, and traces to measure and track performance. EIS systems typically don’t ingest performance data in the form of KPIs. They rely on alerts from existing monitoring tools. VIA AIOps also ingests alerts from existing monitoring tools.
By ingesting and correlating both alerts and performance data, VIA AIOps delivers a consolidated view for monitoring, diagnosing and remediating performance issues.
Fault and Change Management
When an issue occurs, the problem could be anywhere in the stack. It may appear as a symptom in the application or when an end user tries to use the service. A hardware component may be causing a poor streaming experience, and tracing the problem can be complex. VIA AIOps provides end-to-end support to detect, analyze, and identify cause across the full stack. Neither observability nor EIS systems typically provide this capability.
| Capability | Vitria | EIS | Observability |
|---|---|---|---|
| Ingest MELT data directly | Vitria: 100% | EIS: 20% | Observability: 100% |
| Automated Topology Discovery | Vitria: 100% | EIS: 20% | Observability: 0% |
Direct Ingestion of MELT Data
VIA AIOps and Observability systems can ingest raw MELT data directly from systems and devices providing granular rich data for identifying performance patterns and anomalies. EIS systems typically don’t ingest raw MELT data.
Automated Topology Discovery
EIS and Observability tools typically depend upon topology data from CMDB and other inventory management systems. These are often out of sync with the actual topology of the network or IT infrastructure. VIA AIOps can also use this data but augments it with automating topology discovery using raw events like syslogs or SNMP traps. This provides a complete view that is updated on an ongoing basis.
| Capability | Vitria | EIS | Observability |
|---|---|---|---|
| Knowledge-Based Correlation | Vitria: 100% | EIS: 50% | Observability: 50% |
| Correlation using Topology | Vitria: 100% | EIS: 0% | Observability: 0% |
| Correlation: AI Supervised Learning | Vitria: 100% | EIS: 0% | Observability: 0% |
Correlation and Supervised Learning
EIS and Observability systems use topology information for correlation, although that information may be out of sync based on environmental changes. VIA AIOps uses AI supervised and knowledge-based correlation to augment topology-based correlation to improve the accuracy of incident detection. VIA AIOps also uses feedback from your experts, historical patterns, and incident information from ITSM systems to continuously learn and improve incident detection and causal analysis.
| Capability | Vitria | EIS | Observability |
|---|---|---|---|
| Knowledge-Based Incident Analysis | Vitria: 100% | EIS: 0% | Observability: 0% |
| Root Cause Analysis | Vitria: 100% | EIS: 50% | Observability: 0% |
| Likely Fix Recommendation | Vitria: 100% | EIS: 50% | Observability: 0% |
| Agentic AI Remediation | Vitria: 100% | EIS: 50% | Observability: 0% |
| Closed-loop integration to ITSMs | Vitria: 100% | EIS: 50% | Observability: 0% |
Knowledge-Based Incident and Root Cause Analysis
Unlike other EIS or Observability systems, VIA AIOps powers incident analysis and root cause analysis by combining correlated incidents with historical, topological, and diagnostic knowledge along with the reasoning capabilities of GenAI.
Likely Fix and Agentic AI Remediation
VIA AIOps along with some EIS solutions can ingest diagnostic information. This includes ticket history and associated work logs, engineer chat sessions, as well as structured and unstructured databases. All of this makes for a better determination of both root cause and likely fix.
VIA’s Agentic AI incorporates a knowledge chain that enables accurate determination of the correct action, explains why VIA came to a decision and understands the impacts or potential risks of taking the action. Rebooting the server may be the correct action but if it causes millions to lose service for 20 minutes, it’s the wrong decision from a business perspective.
Closed-Loop Integration to ITSM
Bidirectional communication with ITSM systems is a feature of VIA AIOps. This is not a capability of other solutions.