Network Performance Monitoring Use Cases
Reviewed for technical accuracy by: Eric Hian-Cheong, Senior Product Marketing Manager at Kentik, specializing in network monitoring, AI-assisted operations, and flow analytics.
Network performance monitoring (NPM) refers to the process of measuring, diagnosing and optimizing the service quality of a network as experienced by users. NPM requires multiple types of measurement or monitoring data on which engineers can perform diagnoses and analyses, such as:
-
Bandwidth: Measures the raw versus available maximum rate that information can be transferred though various points of the network, or along a network path.
-
Throughput: Measures how much information is being or has been transferred.
-
Latency: Measures network delays from the perspective of clients, servers and applications.
-
Errors: Measures raw numbers and percentages of errors such as Bit Errors, TCP retransmissions, and out-of-order packets
(For a deeper look at how these measurements relate, see Latency vs. Throughput vs. Bandwidth, Understanding Latency, Packet Loss, and Jitter, and the full guide to Network Performance Monitoring Metrics.)
NPM Use Cases at a Glance
- What NPM is for: turning network measurements — bandwidth, throughput, latency, loss, errors — into answers about service quality as users experience it.
- The classic use cases: application performance optimization, data center traffic management, internet performance monitoring, cloud and multicloud visibility, change validation, SLA compliance, capacity planning, security anomaly detection, and cost management.
- The newer use cases: AI data center fabrics (microbursts, elephant flows, job completion time), SD-WAN and WAN edge, Kubernetes and microservices networking, and remote workforce performance.
- The architectural shift: from costly appliance-based packet capture at a few network points to SaaS-based monitoring that combines flow telemetry, device metrics, synthetic testing, and host agents everywhere.
About Kentik: Kentik is an end-to-end network performance monitoring platform that combines SNMP and streaming telemetry, flow data and cloud flow logs, and synthetic testing to monitor performance across hybrid networks. Kentik AI features help teams triage incidents faster by turning telemetry into clear, actionable explanations.
Learn how AI-powered insights help you predict issues, optimize performance, reduce costs, and enhance security.
Common Network Performance Monitoring Use Cases
Network Performance Monitoring (NPM) is an important tool that provides insights into the health, performance, and efficiency of a network. Using network performance monitoring tools and techniques, NetOps professionals can ensure optimal service quality, identify and resolve issues proactively, and make informed decisions about network operations. Here are some key use cases:
Application Performance Optimization
Monitor and troubleshoot performance issues for networked and distributed applications:
- Monitor HTTP and database calls for three-tier networked applications. Understand whether application performance issues are related to network factors. Resolve performance issues.
- Evaluate complex network API communications for highly distributed applications:
– recognize and diagnose emergent performance issues;
– measure relative performance of API partners for vendor selection. (See API Monitoring for the full practice.) - Guide decisions on distributed application architecture, such as when to locally cache network API calls.
For how NPM and application performance monitoring intersect, see Network Observability and APM: How They Work Together.
Data Center Traffic Management
Monitor intra- and inter-data center performance issues. Isolate and troubleshoot infrastructure root causes. East-west traffic visibility is the foundation here: flow telemetry reveals which workloads and conversations dominate fabric links, while sub-second device telemetry catches the microbursts that cause packet loss on links whose average utilization looks healthy. (See also Kentik’s Optimize Data Center Networks solution.)
AI Data Center and Fabric Monitoring
AI training and inference fabrics are the most demanding NPM environment in modern networking: synchronized collective operations stall thousands of GPUs when any single link degrades, and job completion time (JCT) — the metric that translates directly into GPU cost — is gated by the slowest path. NPM for AI fabrics means continuous visibility into elephant flows, microbursts, loss hotspots, and jitter at the resolution these events actually occur. See AI Networking 101 for the full picture of what AI workloads demand from networks.
Internet Traffic Management and Internet Performance Monitoring
Make efficient routing decisions by monitoring performance across hops (first, second, and third) and destination ASNs and geographies. Quickly and cost-effectively bypass network roadblocks by serving traffic from alternate PoPs or via alternate first-hop ASNs.
Internet performance monitoring extends this discipline beyond your own edge: continuously measuring the latency, loss, and path behavior of traffic as it crosses transit providers, peers, and content networks you don’t control. Because the internet is now part of every application’s delivery path, hop-by-hop path analysis from distributed vantage points — correlated with BGP routing data — is what turns “the internet is slow” into a specific provider, path, or routing change you can act on, and network traffic engineering is how you act on it. (See also Kentik’s Understand Internet Performance solution.)
Cloud Networking, Hybrid and Multicloud
Monitor relative quality of IaaS and other cloud providers to guide network connectivity architecture, vendor selection, and contract negotiation. Cloud environments require their own measurement approach — VPC flow logs instead of router counters, synthetic tests between regions and clouds — covered in depth in Cloud Network Performance Monitoring and Multicloud Networking.
SD-WAN and WAN Edge Monitoring
Validate that SD-WAN overlays are delivering on their promise: per-application path selection that actually improves experience. NPM at the WAN edge means measuring underlay circuit quality (MPLS, DIA, broadband) alongside overlay tunnel performance, verifying policy changes before and after rollout, and confirming that branch sites reach critical SaaS applications over optimal paths. See SD-WAN Analytics: What It Is, Key Metrics, and Monitoring Use Cases and Kentik’s SD-WAN Monitoring product.
Kubernetes and Microservices Networking
Distributed applications turn the network into part of the application: service-to-service calls cross pod networks, overlay CNIs, clusters, and clouds, and a slow hop between microservices reads as application latency. NPM here means visibility into traffic between services and clusters — who talks to whom, with what latency and loss — correlated with the infrastructure underneath.
Remote Workforce Performance Monitoring
With users working from home networks and last-mile ISP connections the business doesn’t control, NPM extends to measuring the experience from where users actually sit: synthetic tests toward the applications they depend on, visibility into VPN and ZTNA infrastructure performance, and the ability to distinguish a user’s ISP problem from a corporate infrastructure problem in minutes rather than ticket cycles.
Network Change and New Deployment Validation
Provide instant visibility for network changes and new deployments when building or changing applications, servers, network elements, circuits, or peering/transit. Synthetic monitoring is the natural validation tool here: tests that exercise the changed path before, during, and after the change prove the rollout did what it promised.
Service Provider SLA Monitoring
Continuously measure service quality to ensure compliance with Service Level Agreements. For providers, this is a revenue-protection discipline as much as an operational one — see Kentik for Service Providers.
Network Capacity Planning
Anticipate future network requirements based on current performance trends. Network capacity planning turns those trends into upgrade decisions, and bandwidth utilization monitoring supplies the trend data that makes them defensible.
Network Security and Anomaly Detection
Identify unusual traffic patterns that might indicate potential security threats or DDoS attacks. The same flow telemetry NPM relies on doubles as a security signal: network security monitoring builds baselines of normal behavior, and flow-based DDoS detection catches volumetric attacks against those baselines. (See also DDoS Detection.)
End-User and Digital Experience Monitoring
Evaluate and improve the quality of the end-user’s performance experience. End user experience monitoring and digital experience monitoring extend NPM with the user’s-eye view, measuring application reachability and responsiveness from the locations and networks where users actually are.
Network Cost Management
Strategize and manage expenses related to network bandwidth, peering, and transit. The foundational economics live in the transit-versus-peering decision; at the practice level, network cost intelligence connects connectivity spend to the traffic driving it, so pricing, peering, and routing decisions are made on unit economics rather than invoices. (See also Kentik’s Reduce Network Spend solution.)
Moving Past Traditional NPM Approaches
Network performance monitoring solutions have traditionally utilized an appliance deployment model. An appliance-based PCAP probe with one or more interfaces connects to router or switch span ports or to an intervening packet broker device (such as those offered by Gigamon or Ixia). The appliance records all packets passing across the interface into memory and then into longer-term storage. In virtualized data centers, virtual probes may be used, but they are also dependent on network links in one form or another.
Physical and virtual appliances are costly from a hardware and (in the case of commercial solutions) software licensing point of view. As a result, in most cases, it is only fiscally feasible to deploy PCAP probes to a few, selected points in the network. In addition, the appliance deployment model was developed based on pre-cloud assumptions of centralized datacenters holding relatively monolithic application instances. As cloud and distributed application models have proliferated, the appliance model for packet capture is less feasible, because in many cloud hosting environments, there is no way to deploy even a virtual appliance.
A cloud-friendly and highly scalable SaaS model for network performance monitoring splits the monitoring function from the storage and analysis functions. Monitoring is accomplished with the deployment of lightweight monitoring software agents that export PCAP-based statistics gathered on servers and open source proxy servers such as HAProxy and NGNIX. Exported statistics are sent to a SaaS repository that scales horizontally to store unsummarized data and provides big data-based analytics for alerting, diagnostics and other use cases. While host-based performance metric export doesn’t provide the full granularity of raw PCAP, it provides a highly scalable and cost-effective method for ubiquitously gathering, retaining and analyzing key performance data, and thus complements PCAP.
Modern NPM platforms combine this host-based approach — increasingly built on eBPF in cloud-native environments — with flow telemetry exported by the network itself, device metrics, and active synthetic testing — the three pillars of modern network monitoring — so performance can be measured everywhere traffic flows rather than only where a probe could be installed. For the full arc of how monitoring evolved from SNMP polling to this model, see The Evolution of Network Monitoring.
About Kentik’s Network Performance Monitoring Solution
Kentik offers the industry’s only big data-based, SaaS network observability and network performance monitoring solution that integrates network agent performance metrics with billions of NetFlow, sFlow, IPFIX, cloud flow log, and BGP records matched with geolocation and other forms of enrichment data. Kentik’s NPM solution also incorporates synthetic monitoring, synthetic testing, and digital experience monitoring features that allow for proactive monitoring of all types of networks.
Kentik’s comprehensive network performance monitoring solution delivers:
- Deep Internet Insights: Enables visibility into the performance, uptime, and connectivity of widely-used SaaS applications, clouds, and services.
- Intelligent Automation: Offers valuable insights without overwhelming users with unnecessary alerts.
- Comprehensive Data Understanding: Integrates SNMP, traffic flows, VPC logs, host agents, streaming telemetry and synthetic monitoring for a holistic view of network performance.
- Multi-cloud Performance Monitoring: Monitors network traffic performance across hybrid and multi-cloud environments.
- Rapid Troubleshooting: Kentik’s network map visualizations enable swift issue isolation and resolution.
- Proactive Quality of Experience Monitoring: Optimizes application performance and detects potential issues in advance.
- Enhanced Collaboration Features: Promotes seamless coordination across network, cloud, and security teams through robust integrations.
- AI-Driven Insights: Kentik delivers AI-driven insights, enabling you to detect degrading performance, possible attacks, and traffic changes early, helping you stay ahead of potential issues.
- Answer Any Question: Kentik allows you to ask any question about your network and receive answers quickly, with powerful natural language querying, filtering, and visualization capabilities.
Kentik offers a suite of advanced network monitoring solutions designed for today’s complex, multicloud network environments. The Kentik Network Intelligence Platform empowers network pros to monitor, run and troubleshoot all of their networks, from on-premises to the cloud. Kentik’s network monitoring solution addresses all three pillars of modern network monitoring, delivering visibility into network flow, powerful synthetic testing capabilities, and Kentik NMS, the next-generation network monitoring system.
Related Network Performance Monitoring Topics
- Network Performance Monitoring (NPM): Tools, Metrics & Best Practices
- Network Performance Monitoring Metrics
- Cloud Network Performance Monitoring
- Network Capacity Planning
- Network Path Analysis: Tools, Traceroute Variants, and Continuous Path Monitoring
- Microburst Detection: How to Catch Sub-Second Traffic Spikes That Standard Monitoring Misses
- SD-WAN Analytics: What It Is, Key Metrics, and Monitoring Use Cases
- Synthetic Monitoring: What It Is, How It Works, and Key Use Cases
- Digital Experience Monitoring (DEM): Tools, Workflows, and Best Practices
- Network Observability and APM: How They Work Together
- Network Intelligence: 10 Critical Use Cases
- Best Network Monitoring Tools for 2026: Enterprise, Cloud, MSP & Open-Source Options
FAQs about Network Performance Monitoring Use Cases
What is network performance monitoring (NPM)?
Network performance monitoring is the practice of measuring, diagnosing, and optimizing the service quality of a network as experienced by users and applications. It combines measurements like bandwidth, throughput, latency, packet loss, and errors — gathered from flow telemetry, device metrics, host agents, and synthetic tests — so teams can find where performance degrades, prove whether the network is at fault, and fix problems before users feel them.
What are the most common use cases for network performance monitoring?
The classic use cases are application performance optimization, data center traffic management, internet traffic and routing optimization, cloud and multicloud visibility, change and deployment validation, SLA compliance monitoring, capacity planning, security anomaly detection, end-user experience monitoring, and network cost management. Newer environments have added their own: AI data center fabrics, SD-WAN edges, Kubernetes and microservices networking, and remote workforce performance.
What is internet performance monitoring?
Internet performance monitoring is the continuous measurement of how traffic performs as it crosses networks you don’t control — transit providers, peers, ISPs, and content networks — using distributed synthetic tests, hop-by-hop path analysis, and BGP routing data. It matters because the internet is part of every modern application’s delivery path: when users report slowness, internet performance monitoring identifies the specific provider, path, or routing change responsible, so teams can reroute, escalate to the provider, or serve traffic from an alternate PoP. Kentik supports this by correlating synthetic path measurements from global agents with flow and BGP data, so internet-side degradation is attributed to its cause rather than reported as a mystery.
What KPIs matter most for modern network performance monitoring?
The foundational KPIs are latency (including its variance, jitter), packet loss, throughput against available bandwidth, and error rates such as TCP retransmissions — measured per path and per application, not just per interface, and tracked at percentiles rather than averages, since user pain lives in the tails. Mature programs add experience-level KPIs (application response time from user vantage points), change-detection KPIs (path and routing stability), and business-facing measures like SLA compliance. See our full guide to network performance monitoring metrics for definitions and targets.
What features should I look for in a multi-vendor NPM solution?
Look for telemetry breadth that covers every vendor and environment you run: flow protocols (NetFlow, sFlow, IPFIX), SNMP and streaming telemetry across device makers, cloud flow logs, host agents, and synthetic testing — normalized into one data model so dashboards and alerts don’t fragment by vendor. Beyond ingestion, the differentiators are correlation across telemetry types, full-fidelity data retention for ad hoc investigation, alerting that scales without noise, and increasingly, AI-assisted analysis that turns telemetry into explanations. Kentik was built vendor-neutral on exactly this model, ingesting telemetry from any network and unifying it in the Kentik Data Engine.
How do I forecast bandwidth demand and plan capacity upgrades?
Forecasting starts with trend data: per-interface and per-path utilization tracked over months, decomposed into organic growth, seasonality, and step changes from new applications or sites, then projected against the thresholds where performance (or billing commits) degrade. The discipline is network capacity planning — pairing bandwidth utilization trends with traffic composition, so upgrades target the links that need them and the traffic that justifies them. Kentik supports this by trending utilization across every interface and correlating it with the flow data that explains what’s driving growth.
How do I track SLA compliance for managed network services?
Track the same metrics the SLA defines — availability, latency, loss, jitter, time-to-restore — continuously and from the customer’s perspective, using synthetic tests that exercise the delivered service alongside passive measurement of real traffic. The key is independent, timestamped evidence: per-service measurement against SLA thresholds, alerting before breach rather than after, and historical reporting that settles disputes with data. Kentik supports this with continuous synthetic testing, per-customer traffic visibility, and SLA-aligned alerting and reporting for service providers.
How can I monitor network performance for Kubernetes and microservices?
Microservices turn service-to-service network calls into application latency, so monitoring requires visibility inside and between clusters: which services talk to which, over what paths, with what latency and loss — across pod networks, CNIs, clusters, and clouds. The practical approach combines flow-level telemetry from the container environment with synthetic tests between services and clusters, correlated with the underlying infrastructure so a slow service call can be traced to the network segment responsible. Kentik supports this by extending flow visibility into Kubernetes environments and correlating container traffic with the rest of the network in one platform.
How do I monitor performance for remote workforces and home networks?
Measure from where users actually are: deploy synthetic tests that exercise the applications remote workers depend on from representative locations and ISPs, monitor the VPN concentrators or ZTNA infrastructure that remote traffic traverses, and baseline per-ISP and per-region experience so a degradation can be classified quickly — user’s home network, their ISP, the middle mile, or your infrastructure. The goal is answering “is it us or their connection?” in minutes. Kentik supports this with global and private synthetic agents, VPN infrastructure monitoring, and the path analysis to show where between the user and the application performance breaks down.
What’s the difference between network performance monitoring and application performance monitoring?
NPM measures the network’s contribution to service quality — paths, latency, loss, throughput between the components of an application — while APM instruments the application itself: code execution, transactions, database queries, and service dependencies. They answer complementary halves of the same incident: APM shows that a transaction is slow, NPM shows whether the network between its components is why. Mature operations correlate both; see Network Observability and APM for how the two practices fit together.
How is SaaS-based NPM different from appliance-based monitoring?
Appliance-based NPM deploys hardware or virtual packet-capture probes at selected network points — expensive enough that coverage is always partial, and impossible to deploy in many cloud environments. SaaS-based NPM inverts the model: lightweight collection everywhere (flow export from the network itself, host agents, cloud flow logs, synthetic agents) with storage and analytics centralized in a horizontally scalable platform. The result is coverage across environments appliances can’t reach, full-fidelity retention without on-prem storage limits, and analysis that correlates every vantage point instead of a few.
How do you monitor network performance in AI data centers?
AI fabrics require NPM at higher resolution than traditional environments, because their failure modes are sub-second and their cost of degradation is measured in idle GPU time. The practice combines streaming telemetry for second-scale device and queue metrics, flow telemetry to attribute congestion to specific workloads, and continuous tracking of the elephant flows and microbursts that stall synchronized collective operations — all tied back to job completion time, the metric that translates network behavior into training cost. See AI Networking 101 for the full treatment.
See every use case on your own network
Kentik is the network intelligence platform that delivers all three pillars of modern NPM — flow visibility, synthetic testing, and next-generation NMS — across data center, cloud, edge, and internet.
- Request a personalized demo — see your highest-priority NPM use case running on real telemetry.
- Start a 30-day free trial — connect your flows, devices, and clouds and measure performance everywhere.
- Explore Kentik’s NPM solution — the full picture: flow, synthetics, NMS, and AI-driven insights in one platform.
