OpenTelemetry-Native Observability Platform

$0 to ingest.
An agent that finds and fixes the root cause.

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.

Raiya correlates every signal and executes only the runbooks your team has approved.

Cuts MTTR by 40%, running locally on OpenTelemetry.

Get Started Free Book a Demo

Control Plane Overview dashboard showing service availability, cluster and host health, active incidents, and infrastructure capacity

Federated control plane

Your data 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.

Raiya SRE agent root cause analysis panel over the live incident queue, showing an 80% confidence insight tracing a SEV-1 to a missing Kafka topic

Raiya, the SRE agent

40% faster MTTR. An agent that investigates.

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.

Cost and chargeback dashboard showing monthly cost trend, cost by cluster group, and savings opportunities

Kubernetes cost

Cost, right alongside APM and security.

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.

How we compare

See all comparisons →

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