Your K8s Clusters Are Bleeding Money on Data
Observability agents, log collectors, and cross-cluster data transfers drive up compute and egress costs. Expanso filters at the pod level - only actionable data leaves the cluster.
Why Kubernetes Costs Escalate
Clusters generate massive volumes of logs, metrics, and traces. Moving all of it to centralized platforms is expensive and unnecessary.
Observability overhead is massive
Agents Consume Resources
Prometheus, Datadog, and Fluentd agents consume CPU and memory on every node. Their scraped data generates egress costs. The observability stack can cost more than the workloads it monitors.
Cross-cluster egress adds up
Data Movement Between Clusters
Multi-cluster architectures move data between clusters for aggregation, replication, and analytics. Each transfer incurs cloud egress fees that scale with cluster count.
Pod sprawl inflates compute
More Pods, More Data, More Cost
As applications scale horizontally, each new pod generates logs, metrics, and traces. Observability costs scale linearly with pod count - even if most of that telemetry is routine.
Filter Inside the Cluster
Expanso runs as a DaemonSet or sidecar, processing data inside the cluster before it crosses any network boundary. Egress drops, observability costs shrink, and your clusters run leaner.
How Expanso Cuts Kubernetes Costs
Reduce egress, compute, and observability costs at the cluster level
In-cluster data filtering
Process Before Egress
Filter logs, metrics, and traces inside the cluster. Drop debug-level data, aggregate routine metrics, and forward only actionable signals to your observability platform.
Cross-cluster transfer reduction
Move Less Between Clusters
Pre-aggregate and filter data before cross-cluster transfers. Reduce inter-cluster egress by 80-90% while maintaining visibility across your multi-cluster environment.
Observability cost control
Same Insights, Lower Bills
Reduce the volume of data sent to Datadog, New Relic, Grafana Cloud, or Splunk. Fewer metrics and log lines means lower per-host and per-GB observability costs.
Namespace-level policies
Granular Data Control
Apply different filtering policies per namespace. Development namespaces can drop more aggressively. Production namespaces forward everything critical.
Resource rightsizing insights
Data-Driven Optimization
Expanso captures resource utilization patterns at the source. Use this data to rightsize pods, nodes, and clusters without deploying additional monitoring tools.
DaemonSet deployment
Zero Application Changes
Deploy as a DaemonSet that intercepts data flows at the node level. No sidecar injection, no application code changes, no service mesh dependency.
Proven Kubernetes Cost Reductions
Real results from organizations optimizing Kubernetes data costs
Cloud traffic reduction for automotive fleet with 15,000 distributed nodes
Annual cost avoidance from processing data at the edge instead of cloud
Distributed nodes deployed and managed with consistent data policies
From pilot to full fleet deployment across distributed infrastructure
Real-World Impact
See how organizations cut Kubernetes costs with upstream data control
15K Vehicles: 94% Traffic Reduction
A European OEM deployed Expanso across 15,000 connected vehicles running containerized workloads. Each vehicle generated intrusion detection telemetry that was being sent to the cloud in full. Expanso filtered at the edge, forwarding only security-relevant events.
O-RAN: 47% Observability Savings
A European telecom operator running O-RAN on OpenShift clusters generated massive network telemetry. Expanso filtered and aggregated at the RAN before forwarding to centralized observability, cutting costs while improving anomaly detection speed.
Why Expanso for Kubernetes
Native K8s deployment
Deploy as a DaemonSet, sidecar, or standalone pod. Integrates with your existing Kubernetes toolchain and CI/CD pipelines.
Works with any observability stack
Compatible with Prometheus, Datadog, New Relic, Grafana, Splunk, and any OTLP-compatible platform.
Multi-cluster support
Consistent data policies across clusters, regions, and cloud providers. Manage from a single control plane.
Minimal resource footprint
Less than 2% CPU and 256MB RAM per node. The DaemonSet costs less than the observability data it filters.
Frequently Asked Questions
How does Expanso deploy on Kubernetes?
Expanso deploys as a DaemonSet that runs on every node in your cluster. It intercepts log, metric, and trace data before it leaves the node. You can also deploy it as a sidecar for specific workloads. Standard Helm charts and Kubernetes manifests are provided.
Does this work with our existing observability stack?
Yes. Expanso is compatible with Prometheus, Datadog, New Relic, Grafana Cloud, Splunk, and any platform that accepts standard formats (OTLP, Prometheus remote write, Fluentd, etc.). Your existing dashboards and alerts continue working.
What's the resource overhead?
Minimal. Expanso typically uses less than 2% CPU and 256MB RAM per node as a DaemonSet. The resource cost of running Expanso is a small fraction of the savings it generates by reducing egress and observability costs.
Can we apply different policies per namespace?
Yes. Expanso supports namespace-level filtering policies. Development and staging namespaces can filter more aggressively, while production namespaces forward all critical data. Policies are defined declaratively and version-controlled.
Does this work in multi-cloud Kubernetes environments?
Yes. Expanso is cloud-agnostic and works on EKS, AKS, GKE, OpenShift, and bare-metal Kubernetes. You can apply consistent data policies across all clusters regardless of the underlying cloud provider.
Kubernetes costs scaling with your pods?
Every new pod generates logs, metrics, and traces. Filter at the node level and stop paying to move data nobody queries.