Both OTel-native. One of us still bills you for data.
Dash0 is a fair comparison: genuinely OTel-native, transparent on pricing, billed per data point rather than raw bytes. The architectural difference is narrower than with most vendors. But Dash0 still centralizes your telemetry in their cloud and bills by volume. Randoli processes locally and bills by host, with no volume axis at all.
Where it actually differs
Ingestion pricing
$0 ingestion. Send as much telemetry as your workload produces. Volume has no billing consequence. Instrument more without a cost conversation.
Priced per data point ingested, transparent and linear, but still a volume axis. Instrument more, pay more. The pricing intent is honest; the cost structure still scales with usage.
Data residency
Local processing. Raw logs and traces never leave your cluster. Compliance and sovereignty requirements are satisfied by architecture, not by policy.
Hosted SaaS. Telemetry is processed in Dash0's cloud infrastructure. Data residency requirements need verification against their available deployment regions.
OpenTelemetry compatibility
OTel-native with standard OTLP everywhere. No proprietary formats, no vendor-specific SDKs. Your instrumentation is portable.
Genuinely OTel-native with clean OTLP support and a transparent instrumentation story. The standards approach here is a real strength, not a marketing claim.
AI incident response
Raiya correlates cross-signal incidents, runs structured root-cause analysis, and drafts runbook steps. It's an AI agent built into the observability platform.
Strong OTel observability and alerting. An AI incident response layer is not a current part of the platform offering.
Log monitoring
Logs stream through the same real-time, local pipeline as everything else. $0 ingestion, no separate log meter.
Logs are priced per data point, transparent, but still a volume axis on top of the rest of your telemetry.
The numbers
Volume assumption: ~300 GB logs/day and ~50 M trace spans/day per 100 hosts. Dash0 estimates based on published per-data-point rates for logs, spans, and metrics at standard pricing. Randoli at $0.04/host/hour.
| Scenario | Dash0 (est.) | Randoli |
|---|---|---|
| ~$1,500/mo | ~$1,440/mo | |
Randoli: $0.04/host/hr × 50 hosts × ~730 hrs/mo ≈ ~$1,440/mo Dash0 (est.): illustrative, based on that vendor's published rate card at the volume in this scenario — see the assumption note above. | ||
| ~$6,000/mo | ~$5,760/mo | |
Randoli: $0.04/host/hr × 200 hosts × ~730 hrs/mo ≈ ~$5,760/mo Dash0 (est.): illustrative, based on that vendor's published rate card at the volume in this scenario — see the assumption note above. | ||
| ~$15,000+/mo | ~$14,400/mo (volume discounts available) | |
Randoli: $0.04/host/hr × 500 hosts × ~730 hrs/mo ≈ ~$14,400/mo Dash0 (est.): illustrative, based on that vendor's published rate card at the volume in this scenario — see the assumption note above. | ||
Illustrative estimates based on published rate cards and typical usage patterns, not a quote. Confirm current numbers before publishing.
Why teams switch
Dash0 is one of the more honest vendors in this space. The OTel-native commitment is real, the data-point pricing is more transparent than raw byte charging, and the team clearly cares about getting the standards story right. If you're evaluating on those dimensions alone, Dash0 deserves genuine consideration.
The gap is architectural: Dash0 still centralizes your telemetry in their cloud and still bills by volume. For teams with data sovereignty requirements, or teams that want to eliminate the volume axis entirely so instrumentation is never a cost conversation, Randoli's local-processing model is the structural difference that matters.