Breaking Down Observability Costs: Randoli vs. New Relic, Datadog, & Dynatrace
August 28, 2025
Tags:
Guide
Observability
OpenTelemetry
As Kubernetes adoption grows, so does the complexity and cost of observing containerized workloads. Leading observability platforms like New Relic, Datadog, and Dynatrace offer powerful tooling, but often at enterprise price points. Randoli, a Kubernetes-native observability solution, takes a different approach—one that dramatically reduces costs without compromising visibility or performance.
This article compares annual observability costs across three deployment scales: 20 nodes, 100 nodes, and 300 nodes. We highlight the underlying architectural differences that make Randoli more cost-effective and explain how its Federated Control Plane and in-cluster analytics reshape the economics of monitoring Kubernetes workloads.
Executive Summary: Annual Observability Costs by Vendor
Scenario
Randoli
New Relic
Datadog
Dynatrace
20 Nodes
~$7.5K + $600 egress
~$43.2K–$60K + $2.7K
~$54K–$78K + $2.7K
~$72K–$90K + $2.7K
100 Nodes
~$37K + $2.4K egress
~$216K–$300K + $13.5K
~$270K–$390K + $13.5K
~$360K–$450K + $13.5K
300 Nodes
~$111K + $7.2K egress
~$648K–$900K + $40.5K
~$810K–$1.17M + $40.5K
~$1.08M–$1.35M + $40.5K
Why the Cost Difference? Federated Architecture Matters
Randoli's advantage isn't just pricing transparency—it's architectural. Unlike legacy platforms that require constant data export to centralized clouds, Randoli operates a Federated Control Plane that orchestrates telemetry collection without pulling all raw data out of the cluster.
This architectural model reduces:
Egress costs: Data remains in-cluster until explicitly needed, cutting cloud transfer fees.
Data volume: Randoli retrieves only the slices of telemetry required for analysis.
Latency: On-demand access means you can inspect issues without maintaining an always-on data pipeline.
In-Cluster Log Analysis: Faster, Safer, Cheaper
Randoli's in-cluster log analysis capability avoids sending logs to the cloud. Instead, logs are queried and filtered inside the Kubernetes cluster.
This provides:
Lower cost: Logs aren't ingested into a paid cloud-tier unless necessary.
Randoli demonstrates that observability can be powerful and cost-effective. By rethinking where and how telemetry is processed—in-cluster, on-demand, federated—it offers a sustainable model for scaling observability with your Kubernetes footprint.
As organizations look to reduce cloud spend without sacrificing operational insight, Randoli provides a compelling alternative to traditional platforms. If you're operating Kubernetes at scale, the numbers speak for themselves.
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