Why Splunk Is Not a Network Monitoring Tool And Why That Matters

The Problem Engineers Keep Running Into

If you have spent any meaningful time operating an enterprise network, you have probably lived this moment.

A user reports that an application is slow. Not down, slow. You jump into the usual workflow: latency, packet loss, jitter, utilization, asymmetric routing, or congestion somewhere between endpoints. You want answers quickly, ideally in seconds, not after a week of dashboard building.

This is where the disconnect with Splunk consistently shows up.

Splunk is frequently positioned, sometimes implicitly and sometimes explicitly, as a replacement for traditional network monitoring platforms. I have seen organizations attempt to replace tools like SolarWinds Orion, PRTG, or even ThousandEyes with Splunk dashboards built on SNMP, NetFlow, and syslog ingestion. The result is almost always the same: massive effort, limited fidelity, and frustrated engineers.

This post explains why that happens, not from a licensing or UI perspective, but from an architectural one.

I am not arguing that Splunk is a bad product. Quite the opposite. Splunk is excellent at what it was designed to do. The problem is expecting it to solve problems it was never architected to solve.

What Network Monitoring Actually Requires

Before discussing Splunk, it is important to be precise about what we mean by network monitoring. In real operational environments, network monitoring typically includes:

Continuous polling of device state such as interfaces, CPU, memory, and buffers
Time series performance metrics such as latency, jitter, and packet loss
Topology awareness including Layer 2 and Layer 3 relationships and routing paths
Flow visibility using NetFlow, IPFIX, or sFlow
Threshold based alerting with low latency
Path visualization across multiple hops and domains

Most of these capabilities depend on stateful, real time data collection. SNMP polling intervals, flow caches, routing tables, and telemetry streams are not logs. They are living data sets that change every few seconds.

This distinction matters more than most people realize.

Splunk’s Core Architecture and Its Strength

Splunk was built as a log ingestion and search platform. Its core strengths include:

Schema on read indexing
High volume log ingestion
Powerful ad hoc search using SPL
Correlation across heterogeneous data sources
Long term retention and forensic analysis

Architecturally, Splunk excels at event based data:

Syslog
Application logs
Security events
Audit trails
Error messages

These are discrete events that occur at a point in time and are best analyzed retrospectively or correlatively.

Network monitoring data, however, is fundamentally state based and time series driven.

Trying to force time series telemetry into a log centric engine introduces friction at every layer including ingestion, indexing, storage, visualization, and alerting.

SNMP and Flow Data Are Not Logs

This is where many implementations go off the rails.

Yes, you can ingest SNMP traps, SNMP poll results, and NetFlow into Splunk. That does not mean you should treat them as primary monitoring inputs.

Consider a simple SNMP polling model:

Router > SNMP Poller > Metrics Database > Alert Engine > User Interface

Traditional network management platforms store this data in optimized time series databases with rollups, retention tiers, and native understanding of counters versus gauges.

Now contrast that with a Splunk based model:

Router > SNMP Poller > Log Events > Indexer > Search > Dashboard

Every poll becomes an event. Every counter delta becomes a calculated field. Every alert requires SPL logic. Every dashboard requires manual aggregation.

At scale, this becomes operationally expensive and brittle.

Real Time Visibility Versus Forensic Analysis

Network operations are primarily reactive in real time:

A circuit starts dropping packets
Latency spikes during peak traffic
A routing change introduces asymmetry

Engineers need immediate answers, not historical searches.

Splunk shines when the question is:

What happened over the last 24 hours across these systems?

Network monitoring tools shine when the question is:

What is happening right now, and where?

Trying to collapse these two use cases into a single platform usually degrades both.

Topology Awareness as the Missing Piece

One of the most underappreciated aspects of network monitoring is topology context.

Good network monitoring platforms understand:

Which interfaces connect to which devices
Which VLANs span which links
How routing tables and IGP or BGP paths intersect

This enables capabilities such as:

Root cause analysis
Impact analysis
Hop by hop path tracing

Splunk has no native concept of network topology. Any attempt to model it requires external data sources, custom lookups, and ongoing maintenance.

At that point, you are effectively rebuilding a network monitoring system inside a log analytics platform.

NetFlow Where the Cost Really Shows Up

NetFlow is often cited as justification for using Splunk as a monitoring platform.

In practice, this is where costs and complexity increase dramatically.

High volume flow data:

Generates massive event counts
Consumes significant license volume
Requires aggressive filtering and summarization

Traditional flow tools summarize data at the collector level. Splunk often ingests raw or partially processed flows, pushing aggregation downstream into SPL searches.

The result is delayed insights and expensive licenses.

A Real World Scenario and Lessons Learned

In one environment, a team attempted to replace SolarWinds with Splunk for WAN monitoring.

They:

Ingested SNMP poll data every 60 seconds
Ingested NetFlow from edge routers
Built custom dashboards for latency and utilization

The outcome:

Dashboards took weeks to build
Alerts lagged by several minutes
Engineers stopped trusting the data
SolarWinds was quietly reintroduced

Splunk remained, but only as a log analytics platform where it actually added value.

Where Splunk Does Belong in Network Operations

This is the important part.

Splunk is extremely valuable in network environments when used correctly:

Syslog analysis for firewalls, routers, and switches
Change correlation between configuration changes and incidents
Security event analysis
Cross domain troubleshooting across network, application, and authentication systems

For example:

Correlating a BGP flap with a firewall policy change
Tracing authentication failures across ISE, Active Directory, and VPN gateways

This is where Splunk acts as the translator between operational domains.

Cisco, Splunk, and the Data Fabric Direction

Cisco’s acquisition of Splunk reinforces this reality.

The emerging Cisco Data Fabric positions Splunk as a data substrate, not a monitoring replacement. Telemetry still belongs in purpose built systems such as ThousandEyes, AppDynamics, and traditional network monitoring platforms. Splunk sits above them and correlates outcomes.

That architectural separation is intentional and correct.

Practical Guidance for Architects

If you are designing or rationalizing a monitoring stack:

Use purpose built network monitoring tools for real time visibility
Use Splunk for logs, events, and cross domain correlation
Do not replace polling engines with log platforms
Avoid NetFlow ingestion into Splunk unless it is strictly necessary

Think in terms of roles, not consolidation.

Key Takeaways

Splunk is not a network monitoring platform by design
Network monitoring requires real time, stateful, topology aware systems
Forcing telemetry into log analytics platforms introduces cost and complexity
Splunk excels as a correlation and forensic analysis tool
Architectures work best when tools are used according to their strengths

If you treat Splunk as a monitoring replacement, you will fight the platform.
If you treat it as a data correlation engine, it becomes indispensable.

That distinction is the difference between operational clarity and perpetual frustration.