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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Monitor Arista VeloCloud SD-WAN performance with Datadog
2026-01-20 · via Datadog | The Monitor blog

As organizations grow their cloud environments and branch office networks, maintaining reliable connectivity and application performance becomes more complex. VeloCloud SD-WAN provides dynamic, policy-based routing to help ensure that your connectivity is dependable and cost-efficient, and that your applications perform consistently.

Datadog’s new VeloCloud SD-WAN integration provides unified visibility into the networks that connect your branches, data centers, and cloud services, including real-time link performance and edge device resource usage. This integration collects metrics and logs directly from the VeloCloud Orchestrator, displaying this crucial WAN data within Datadog alongside your existing performance, reliability, and security monitoring. This consolidated view allows you to proactively manage WAN health, detect network issues faster, and gain insights that can help you operate your WAN reliably. And you can use Datadog Network Device Monitoring (NDM) to gain a device-oriented view that makes it easy to explore and investigate the performance of every layer of your network hardware.

In this post, we’ll look at how Datadog enables you to:

Datadog dashboard showing a VMware VeloCloud SD-WAN overview, including counts of links and edges that are up or down, active alerts, and time-series charts for edge CPU, memory, and latency.

SD-WAN dynamically routes traffic across multiple paths based on network conditions, and visibility into each link’s performance is essential for diagnosing slow or unreliable connections. Datadog provides this insight by collecting key metrics directly from the VeloCloud Orchestrator. The out-of-the-box (OOTB) dashboard helps you track performance across all sites in your SD-WAN environment, helping you correlate application health with key metrics, including:

  • Link availability and status
  • Latency, jitter, and packet loss per transport type
  • Bandwidth usage and throughput
Datadog dashboard displaying SD-WAN link performance, with tables of top link routes and applications per link, plus metrics for latency, jitter, packet loss, throughput, and bandwidth by interface.

You can also monitor the resource usage and availability of your VeloCloud SD-WAN edge devices, which route traffic between branch locations, data centers, and the cloud. If an edge node becomes overloaded or unavailable, application traffic may be disrupted. Datadog enables you to easily determine whether edge device performance is a factor in network disruption by surfacing metrics such as:

  • CPU and memory usage
  • Device uptime and availability
  • Interface traffic and bandwidth consumption
Datadog dashboard showing VMware VeloCloud edge device health, including edge status counts, CPU and memory usage over time, latency charts, and a table of traffic volume by edge.

You can add VeloCloud metrics to existing dashboards to add context, and create your own dashboards to customize your WAN performance data visualization. To minimize and even prevent network disruptions in your SD-WAN environment, you can set up monitors to automatically alert you if a device fails or exhibits high resource usage that could affect network performance or signal a pending failure.

Detect and troubleshoot connectivity issues

VeloCloud provides connectivity across multiple transport types, including MPLS, broadband, and LTE. This enables you to manage a cost-efficient WAN environment that provides fast transport across a potentially wide and varied geography. But that environment can become complex, making troubleshooting a challenge. To effectively diagnose and fix SD-WAN performance issues such as high packet loss, congestion, and unstable links, teams need the ability to analyze the performance history of each device and detect anomalies in real time.

The VeloCloud integration helps teams quickly pinpoint and resolve network issues before they impact users by detecting sudden spikes in latency, jitter, and packet loss. You can then create monitors to alert you when link quality degrades, helping you to address the underlying issue before network disruptions occur. Datadog’s machine learning–powered anomaly detection identifies underperforming transport links automatically, accelerating troubleshooting and providing the information you need to optimize routing policies.

Monitor VeloCloud with Datadog NDM

While the OOTB and custom dashboards provide high-level views of your VeloCloud metrics, Datadog NDM allows you to dive deeper and track the performance of every individual network component within your SD-WAN environment and beyond. With NDM, you can easily inspect each network device in your fleet, including your VeloCloud Edges. You’ll see a summary of each device’s interface states (such as up, down, or admin down) alongside crucial metrics like uptime and total inbound and outbound throughput. By surfacing status and performance data, NDM helps you see whether a specific VeloCloud Edge or other network device may be responsible for network disruptions.

Furthermore, NDM provides granular performance details down to the individual interface level. For every interface on a monitored device, you can view critical data such as error counts and the rate of data sent and received. This level of detail is invaluable for spotting bottlenecks, identifying oversaturated interfaces that could be impacting VeloCloud path quality, and gaining a precise understanding of traffic flow and potential issues within specific segments of your network infrastructure.

Gain end-to-end visibility into your SD-WAN infrastructure

Together, NDM and the VeloCloud SD-WAN integration give you end-to-end visibility into network performance across distributed environments. By monitoring link quality, edge health, and transport behavior in real time, teams can reduce downtime and enhance the reliability of their WAN connectivity. See the documentation to learn more about the VeloCloud SD-WAN integration. If you’re not using Datadog yet, you can start a free 14-day trial.