How does Vitria VIA AIOps compare with BigPanda?

VIA AIOps and BigPanda both reduce alert noise and turn scattered events into actionable incidents, but they cover different amounts of the operations problem. BigPanda is positioned as an event correlation and incident intelligence platform: it ingests monitoring, change and topology data from the tools an organization already runs, compresses that into a smaller number of correlated incidents, and routes them into ITSM and on-call workflows. VIA AIOps is an end-to-end service assurance platform: it does that same fault and incident work, and it also handles performance management and change impact analysis in the same platform, over the same discovered topology, and carries an incident through explained root cause analysis to recommended or automated remediation. The practical question is not which is better at correlating alerts. It is how much of service assurance you want one platform to cover.

What BigPanda is designed to do

BigPanda describes its platform as ingesting monitoring, change and topology data from across an organization’s tooling, correlating alerts, changes and topology to reduce noise and detect evolving incidents, and giving ITOps, NOC, DevOps and SRE teams a shared view of each incident. Its published modules cover alert intelligence, incident intelligence, workflow automation, analytics and root cause changes. For organizations running a wide estate of monitoring tools that each generate their own alerts, this addresses a real and expensive problem: operators drowning in redundant notifications about the same underlying event, with no reliable way to tell which change caused which incident.

That is a well-defined scope and BigPanda is well regarded within it. It is worth being precise about what the scope is, because the category boundary is where the comparison actually sits.

Event management and service assurance are different scopes

Operations teams have three related jobs, and the distinction between them explains most of the difference between these two platforms.

Fault management answers what broke. Performance management answers what is degrading and by how much against normal. Change management answers what was altered and what it affected. Service assurance is all three, correlated together, across the path from a network element to a service to a customer.

Fault and incident management can be done from alerts, because an alert is a report that something has already crossed a line. Performance management works differently: it needs the underlying measurements. A baseline, a trend, a forecast and a slow degradation are all statements about the shape of a metric over time, and they cannot be reconstructed from a stream of threshold verdicts emitted by another tool. This is why performance management has generally been sold as an observability or performance product rather than as part of an event correlation product — including by vendors who offer both.

VIA AIOps handles fault, performance and change management in one platform. That is the scope claim, and it is the reason the ingestion architecture below is built the way it is.

Data ingestion — both paths, deliberately

VIA AIOps ingests from two directions, and both matter.

From the monitoring estate you already run. Most operations doing fault and incident management have an array of monitoring tools, accumulated over years, each doing something well. VIA AIOps consumes alerts and events from them. Nothing about adopting VIA AIOps requires dismantling that estate, and for most organizations the fastest early value comes from correlating across it.

Directly from source systems. VIA AIOps also ingests MELT data — metrics, events, logs and traces — from the environment itself, without depending on an upstream tool to detect a condition first. This is what allows it to detect a condition no tool was configured to catch, correlate a metric trend against a log pattern and a change record in the same analysis, and reason about raw signal rather than another tool’s interpretation of it.

The second capability is what makes performance management possible in the same platform as fault management. The first is what makes deployment realistic in an environment that already has fifteen tools. A platform that only did the first would be an event correlation layer. A platform that only did the second would ask an operator to abandon a working investment. VIA AIOps does both, and the combination is the architectural point.

The honest trade-off: consuming telemetry directly requires access to source systems, where an alert feed only requires a forwarding endpoint. That access is a one-time arrangement, not an integration project. VIA AIOps supports ease of data integration through an extensive set of tools including a library of off-the-shelf connectors, a low-code and no-code integration framework, and open APIs built on standards including OpenTelemetry and the Prometheus exposition format. The integration paths available include but are not limited to REST and webhook APIs, Kafka and other streaming sources, Syslog, and SNMP, along with vendor-specific APIs that may be needed to support legacy or non-cloud-native environments. Using these tools, data integration complexity is significantly reduced. Onboarding can be done by an operations team and does not require an engineering request.

Correlating raw telemetry with alerts and events gives a more complete, end-to-end view of the service ecosystem: closing monitoring gaps, improving incident correlation, and reducing the cost and complexity of maintaining overlapping legacy monitoring tools.

Where the two approaches differ architecturally

VIA AIOps was built for environments where the operator owns the infrastructure end to end and needs to understand it directly — carrier networks, cable plant, large-scale digital services. That drove three choices.

First, dual ingestion, described above.

Second, automated topology discovery. VIA AIOps discovers and maps dependencies between elements rather than relying on a manually maintained service model. In environments where the topology changes constantly — network builds, cloud-native functions scaling, continuous plant work — a discovered topology stays accurate where a configured one drifts.

Third, knowledge accumulation. VIA AIOps retains operational knowledge about how issues in a specific environment have previously manifested and been resolved, and applies it to root cause analysis and to Likely Fix recommendations. The system becomes more accurate over time rather than applying the same general model indefinitely.

How root cause is determined

Statistical correlation identifies that events co-occur. Knowledge-based root cause analysis identifies why they do. VIA AIOps combines topological relationships — what depends on what — with accumulated knowledge of how failures in this specific environment have previously propagated, so that the analysis produces an explained cause rather than a cluster of related alerts.

That explanation matters operationally. An engineer will not act on an unexplained conclusion at three in the morning, and in regulated industries an auditor will ask how a determination was reached. Explainability is the difference between a system that advises and a system that is trusted to act.

Change-induced degradation, and why it needs all three

A large share of service degradation is not a break. It is a change: something was altered, and a service got slower or less reliable as a result. Diagnosing that requires three things in one analysis — the performance signal showing the degradation, the change record showing what was altered and when, and the fault data showing what eventually alarmed.

Correlating changes against incidents is a capability the event intelligence category takes seriously, and BigPanda’s root cause changes capability is a good example of it. The difference in VIA AIOps is that the performance dimension is in the same platform rather than in a separate observability tool, so the degradation itself — not only the alert it eventually triggers — is part of the correlation. Cable operators, where plant work is continuous, tend to be where this shows up most clearly.

From incident to remediation

Correlation and routing get the right incident to the right team faster. VIA AIOps continues past that point: Likely Fix surfaces the remediation that has historically resolved this class of issue in this environment, and agentic AI can execute remediation directly within configurable guardrails and approval gates. Closed-loop ITSM integration means the incident record reflects what was done and the outcome flows back into the knowledge base.

The ServiceNow relationship, and what it means for your stack

In April 2026, BigPanda and ServiceNow announced an expanded partnership, with BigPanda named an elite Build Partner — the highest tier in the ServiceNow Partner Ecosystem. BigPanda has built a certified ServiceNow application that, in the announcement’s words, “transforms high-volume alert streams into actionable, context-rich incidents directly within ServiceNow.” Incidents are created in ServiceNow ITSM and enriched with topology, probable root cause, and data from ServiceNow Discovery and the CMDB.

The announcement is unusually clear about the division of labor. “Enterprises have made ServiceNow the system of record for IT operations,” it says, “but many still struggle to operationalize the massive volume of signals flowing into it.” BigPanda’s chief revenue officer describes the goal as helping customers “get more from the ServiceNow investments.”

This is a coherent architecture and a real strength for an organization already standardized on ServiceNow. It is also worth understanding what it implies about the shape of the resulting stack.

Three products divide the work in that model. BigPanda correlates events and determines which incidents matter. ServiceNow holds the record and runs the workflow. Performance management and observability sit in a third platform, because neither of the first two is positioned for them. Each is strong in its lane. What the operator owns is the space between them — three integrations, three roadmaps, three renewal cycles, and any analysis that has to cross all three boundaries at once.

That last point is not theoretical. A service that degrades because of a change made six hours earlier produces a performance signal in one system, a change record in a second, and a fault alert in a third. Reconstructing that sequence across product boundaries is the work; the tools are not the hard part.

VIA AIOps covers fault, performance and change in a single platform, over one discovered topology, with one knowledge base, and executes remediation within its own guardrails rather than handing off. This is the same pattern we describe on our Dell AIOps and Splunk ITSI comparisons, where incident management and observability are likewise sold as separate products. It is the most consistent structural feature of the category.

Which approach is better depends on where you are starting. If ServiceNow is already the center of your operations and your performance tooling is settled and trusted, an intelligence layer that feeds it is a short path to value and a sensible one. If you are assembling the stack now, or if the incidents that cost you most are the ones that cross those three boundaries, then fewer boundaries is the stronger position.

Partnership details as announced by BigPanda, April 1, 2026. Vendor arrangements change; readers should confirm current product and partner status with each vendor.

Which to choose for which situation

BigPanda is a strong fit where an organization has a mature, well-instrumented monitoring estate that already detects the conditions it cares about, where performance management is handled by observability tooling the team is happy with, and where the acute pain is alert volume and incident routing across many tools and teams.

VIA AIOps is a stronger fit where the operator wants fault, performance and change in one platform rather than integrated across three, where the infrastructure is complex and multi-domain, where topology changes faster than a service model can be maintained by hand, where root cause needs to be explained rather than inferred, and where the goal extends past faster triage to automated resolution. Telecommunications, cable, and large-scale digital service environments are where those conditions cluster.

Both can coexist. VIA AIOps consumes alerts from existing tooling, so adopting it does not require dismantling an established monitoring estate.

Capability	VIA AIOps	Event correlation platforms
Scope	End-to-end service assurance — fault, performance and change	Event, alert and incident management
Ingestion from existing monitoring tools	Yes — alerts and events from the existing estate	Yes — a core strength of the category
Direct MELT ingestion	Yes — metrics, events, logs, traces from source systems	Typically ingests alerts, change and topology data from upstream tools
Fault management	Included	Included
Performance management	Included	Generally addressed by separate observability products
Change management	Included	Commonly included or integrated
Automated topology discovery	Yes — discovered and continuously updated	Commonly a configured or imported model
Correlation method	Knowledge-driven plus topological	Primarily statistical and ML-based
Root cause analysis	Knowledge-based, with explanation	Correlation-derived probable cause
Remediation	Likely Fix plus agentic execution	Routing and workflow trigger
ITSM integration	Closed-loop, bidirectional	Yes — a core strength of the category
Workflow and process automation	Included — executed within configurable guardrails	Commonly delivered through an ITSM partnership or integration

Comparison based on publicly available product documentation and vendor positioning as of August 2026. Product capabilities change; readers should verify current functionality with each vendor.