Randoli is built on a fundamentally different architecture compared to Datadog and Dynatrace. Instead of ingesting and storing all telemetry (logs, metrics, traces) in a centralized platform, Randoli processes telemetry locally inside your environment. It analyzes raw data at the source and extracts only signals, insights, and metadata.
This means:
In short, we don’t charge for ingestion because we don’t depend on it.
Unlike Datadog and Dynatrace, Randoli is designed with data sovereignty as a first-class principle.
This makes Randoli ideal for:
With Datadog and Dynatrace, retention is tightly coupled to cost. Longer retention means higher bills. Randoli flips this model:
Yes, Randoli runs a lightweight data plane in your environment. But this is a feature, not a burden. With Datadog and Dynatrace, you still end up managing:
You’re already paying for long-term storage separately anyway. Randoli simply:
The net result? Lower total cost, better control, and no surprise bills.
Cloud providers (AWS, Azure, GCP) show infrastructure-level costs, but Kubernetes abstracts everything behind it. Datadog and Dynatrace offer limited or add-on visibility, but Randoli goes deeper by default.
Why this matters:
You can identify:
Observability without cost visibility is incomplete. Randoli brings both together.
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The Randoli Observability platform has proved to be indispensable. The visibility and insights it provides enabled us to reduce spend, and helped our developers to troubleshoot faster while reducing the burden on our platform team.

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