Telemetry collection and instrumentation
Standardizing how metrics, logs, and traces are collected from containers, pods, nodes, and cloud services using OpenTelemetry Collectors as the vendor-neutral collection layer.
Cloud & Kubernetes Observability
Your Kubernetes clusters are generating telemetry. None of it is connected.
Container-based environments produce massive volumes of metrics, logs, and traces — but most organizations monitor them with tools that were never designed to correlate across clusters, clouds, and the underlying network. The result is MELT data scattered across six dashboards, no single view of service health, and incident response that starts with a 20-minute fight over whose tool has the truth.
IVI architects observability pipelines that collect, normalize, and route telemetry from Kubernetes and cloud workloads into a coherent operational model — so your team can actually answer "is it the application, the cluster, or the network?" in seconds, not hours.
Unified observability architecture for cloud-native environments with OpenTelemetry and Cribl pipelines.
The Challenge
The reality of unarchitected observability
Kubernetes changes fast. Pods spin up and disappear. Services talk to each other across namespaces, clusters, and cloud accounts. Traditional monitoring approaches — built for static infrastructure with known endpoints — were not designed for this.
What observability looks like without an architecture behind it
Most organizations end up with overlapping tools: CloudWatch for AWS resources, a separate APM platform for application traces, Prometheus or Grafana for cluster metrics, a log aggregator for container stdout, and a network monitoring tool that has no context for any of the above. Each tool sees a slice. No tool sees the whole.
No correlation between Kubernetes pod health, cluster resource state, and application performance in a single view
Alert noise from dynamic container environments creates fatigue — teams start ignoring high-severity alerts
OpenTelemetry adoption is inconsistent — some services are instrumented, most are not
Log volumes from containerized workloads overwhelm SIEM and log storage
Multi-cluster and multi-cloud deployments fragment telemetry across platforms with no unified layer
Engineers spend incident response time switching tools instead of diagnosing problems
The four layers of cloud and Kubernetes observability IVI architects
Effective observability in cloud-native environments requires getting four distinct layers right. Most organizations have partial coverage at one or two — and gaps at the others.
Observability pipeline and data routing
Cribl Stream and Cribl Edge to filter, enrich, and route data before it reaches downstream platforms. Reduces storage costs while improving signal quality.
Unified visualization and correlation
Dashboards that correlate cluster state, workload performance, and infrastructure health in a single view — not a tab for each tool.
Alerting, AIOps, and operational integration
Alert tuning to reduce noise without sacrificing coverage — grouping related signals and routing actionable alerts to the right team.
How IVI approaches a cloud observability engagement
A structured approach to building unified observability across your Kubernetes and cloud environments.
1
Discovery and telemetry inventory
Map what you are already collecting, where it is going, and what is missing. Covers cluster topology, existing instrumentation, and operational gaps.
2
OpenTelemetry Collector architecture
Design collector topology for your environment — DaemonSet collectors for node-level signals, Deployment collectors for cluster-wide data.
3
Pipeline design with Cribl
Design the observability pipeline that normalizes, filters, and routes telemetry before it reaches storage. Eliminates duplicate data and reduces costs.
4
Visualization and alert tuning
Build operational views organized around incident response questions. Tune alert thresholds and connect to ticketing systems.
What You Get
Complete observability architecture with documentation and operational handoff.
OpenTelemetry Collector architecture
Documentation with DaemonSet and Deployment topologies per cluster, including Helm chart configurations.
Cribl Stream pipeline configuration
Routing rules, suppression logic, and data reduction benchmarks with cost impact analysis.
Unified dashboards and alert runbooks
Grafana or Splunk dashboard set covering cluster health, workload performance, and multi-cloud infrastructure with tuned alerting.
Outcomes
- Single view of service health across clusters, clouds, and underlying network
- 30-60% reduction in log storage volume through intelligent pipeline filtering
- Faster incident resolution with correlated telemetry instead of tool switching
- Reduced alert noise and fatigue from container environments
- Vendor-neutral telemetry collection that survives platform changes
The Right Fit
- Run Kubernetes in production on Amazon EKS, Azure AKS, Google GKE, or on-premises
- Have multiple monitoring tools that do not correlate with each other
- Experience high alert volume and fatigue from container environments
- Generate high log volumes from containerized workloads
- Planning or mid-way through cloud migration
- Already running Aegis PM or InsightOps and want deeper cloud-native telemetry
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Why IVI
Built for cloud-native environments with enterprise operational requirements
Vendor-neutral architecture
OpenTelemetry collection layer works with your existing tools and survives platform changes.
How It Works
Standardized telemetry format means your instrumentation investment is portable across visualization platforms.
Pipeline-first approach
Cribl Stream reduces storage costs while improving signal quality before data reaches downstream platforms.
Impact
30-60% reduction in log volume with intelligent filtering and routing based on operational value.
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Related Resources
Related Resources
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FAQs
Frequently Asked Questions
Common questions about cloud and Kubernetes observability.
What monitoring tools does IVI work with for Kubernetes observability?
IVI works with the tools you already have rather than requiring a rip-and-replace. On the collection side we deploy OpenTelemetry as the vendor-neutral layer. On the pipeline side we use Cribl Stream and Cribl Edge. Downstream platforms include Splunk, Grafana, Datadog, LogicMonitor, and AWS-native tools including CloudWatch, CloudTrail, and AWS X-Ray.
What is OpenTelemetry and why does IVI use it as the collection standard?
OpenTelemetry is a vendor-neutral, CNCF-maintained framework for collecting metrics, logs, and traces from cloud-native applications and infrastructure. It produces telemetry in a standardized format that any downstream platform can consume. Using OpenTelemetry as the collection layer means your telemetry is not locked to a single vendor's agent or format.
What does Cribl do and why is a pipeline layer necessary?
Kubernetes environments generate high-volume telemetry by default. Without a pipeline in between, all of that data hits your storage platform raw, which drives up cost and buries the signals that matter in noise. Cribl Stream acts as the pipeline layer: it ingests telemetry from any source, applies filtering and enrichment logic, and routes the right data to the right destination.
How does cloud observability connect to network observability?
Application performance problems in cloud-native environments are not always application problems. A latency spike in a Kubernetes service might be caused by a network path issue, a WAN congestion event, or a DNS resolution delay. IVI connects cloud and Kubernetes telemetry to network observability through Arista CloudVision Universal Network Observability (CV UNO), Catchpoint, and NetMagus depending on the environment.
Can this feed into Aegis InsightOps?
Yes, and that is the intended architecture for organizations running InsightOps. InsightOps operates as an AI intelligence layer across your operational data — the richer and more normalized the telemetry feeding it, the higher the quality of its root cause analysis and automated response.
How long does a cloud observability engagement typically take?
Discovery and telemetry inventory typically takes one to two weeks depending on environment complexity. Collector deployment and pipeline configuration is typically two to four weeks. Dashboard build and alert tuning is one to two weeks. Most engagements run six to ten weeks end to end from kickoff to a production-ready observability layer.