Signals, not ingestion

You don't need another dashboard. You need to know why, faster.

Q: Does any of my data leave my environment?

Raw logs, traces, and metrics stay in your environment. What reaches Randoli's control plane is the processed signal, correlation results and derived insight, not the underlying telemetry.

Q: What happens to retention?

You control retention on your own infrastructure. There's no vendor-side retention tier to negotiate or pay extra for.

Q: How much operational overhead does this add?

The local processing component runs as a lightweight collector in your cluster. Most teams have it running inside an afternoon, and there's no separate storage backend to manage.

Q: Why is ingestion $0 instead of just cheaper?

Because there's no ingestion step to bill. Processing happens where the data is generated, so the architecture doesn't have a volume-based cost to pass on.

Q: What does Raiya actually do during an incident?

It correlates the signals arriving from your environment, proposes a likely root cause with the supporting evidence, and can hand off a contextual incident report so the on-call engineer isn't starting from a blank dashboard.

Randoli's SRE agent cuts mean time to resolution by 40%. It runs locally on OpenTelemetry, with $0 ingestion cost.

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40%

faster mean time to resolution, with Raiya on call

Video: Raiya resolving an incident, 45–60s autoplay hero clip — TODO

Screenshot: architecture / control-plane view — TODO

Federated control plane

Your data plane never leaves your environment.

Local processing means logs and traces stay put. Randoli's control plane receives signals, not raw telemetry, so data sovereignty is the default rather than an add-on you configure later. It's all built on OpenTelemetry, so you're standardizing on an open spec, not a proprietary agent.

Screenshot: cost / chargeback dashboard — TODO

Kubernetes cost

Cost is a signal, not a monthly surprise.

Chargeback and cost attribution sit next to the same traces and logs you already use for incidents, so a spend spike and the deployment that caused it show up in the same place.

Screenshot: Raiya incident view — TODO

Raiya, the SRE agent

An agent that reduces MTTR by 40%, not one that adds another alert.

Raiya correlates signals across your stack and proposes root cause before a human opens a second tab. Teams using it report resolving incidents 40% faster than their pre-Randoli baseline.

How we compare

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vs Datadog

Datadog bills per host and per GB ingested. Randoli processes locally and bills $0 for ingestion, full stop.

vs Dynatrace

Dynatrace's DPS model spans host, memory, log, and RUM dimensions that compound as you grow. One rate, no compounding.

Questions we get asked

Does any of my data leave my environment?

What happens to retention?

How much operational overhead does this add?

Why is ingestion $0 instead of just cheaper?

What does Raiya actually do during an incident?

"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."

Tarun Mistry, CTO, Rail, A Ripple Company

"The Randoli Observability platform has been a game-changer for us. It provides real-time insights, proactive monitoring, and robust troubleshooting capabilities that keep our systems running smoothly 24/7. We can quickly identify and resolve issues, ensuring minimal disruption for our customers worldwide."

Charles Mimeaud, IT Director, AAJapan