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.
Direct comparisons
For a capability-level comparison against a specific platform:
- VIA AIOps vs BigPanda
- VIA AIOps vs Dell AIOps Incident Management (formerly Moogsoft)
- VIA AIOps vs Splunk ITSI
Evaluating platforms rather than comparing two? Best AIOps for Telecom Operators sets out the criteria to apply and how to test each one, and the AIOps Buyer’s Guide covers the full evaluation process.
Frequently Asked Questions and Answers
How is VIA AIOps different from an observability platform?
Observability platforms collect and analyze telemetry — logs, metrics, events and traces — so that an engineer can investigate a problem. VIA AIOps adds the interpretation and action layer above that: it correlates signals across domains into a single incident, determines root cause using accumulated operational knowledge, and can execute remediation within configurable guardrails. Observability tells you what is happening; VIA AIOps determines why and acts on it. VIA AIOps also ingests MELT data directly from source systems, so it does not require an observability platform to be in place first.
What is the difference between event intelligence and AIOps?
Event intelligence solutions consolidate signals and events from across a portfolio of monitoring tools, reducing noise through correlation and deduplication so that operations teams see incidents rather than alert floods. AIOps is the broader practice of applying AI and automation across IT operations. In practice, most event intelligence products stop once incidents are correlated and deduplicated. VIA AIOps performs that consolidation and continues through it — knowledge-based root cause analysis, Likely Fix recommendations drawn from how comparable issues were resolved before, and agentic remediation that closes the loop.
Does VIA AIOps replace our existing monitoring tools?
No. VIA AIOps ingests MELT data directly from source systems and can also consume alerts from the monitoring tools already in place, so it can be introduced alongside an existing estate rather than requiring a migration first. Organizations often reduce monitoring overlap over time, but that is a consequence of adoption rather than a prerequisite for it. Production deployments in large environments have typically been reached in 90 to 120 days, including at an ISP processing petabytes of telemetry daily.
How does VIA AIOps determine root cause?
By combining topological dependency paths with operational knowledge accumulated for that specific environment. Automated topology discovery maps what depends on what, so downstream symptoms can be separated from the upstream failure producing them. The knowledge plane retains how comparable issues have previously manifested and been resolved in that environment, and applies it alongside statistical correlation. The output is an explained determination with its reasoning exposed rather than a ranked list of probable causes — which is what makes an operations team willing to let the system act rather than only advise.
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