# OpenObserve vs Dash0

> OpenObserve vs Dash0: both OTel-native, but OpenObserve is open source, self-hostable, with object-storage economics and no per-signal pricing. Try free.

Source: https://openobserve.ai/dash0-alternative/
Competitor: Dash0

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Open source. Self-hostable. Object-storage economics with no per-signal pricing. The OpenTelemetry-native platform you can run anywhere.

- **140x** Lower storage costs compared to Elasticsearch
- **21,000+** Github Stars
- **9,000+** Companies trust us

High-volume teams outgrow per-signal pricing. OpenObserve prices by volume on object storage. [See volume-based pricing →](/pricing/)

*Dash0 publishes consumption pricing per million metric data points, spans, and log records. Actual savings depend on your telemetry mix, volume, and retention needs.*

- [Request Demo](/demo/)
- [Start Free](https://cloud.openobserve.ai/web/login)

## Why teams switch from Dash0

Both platforms speak OpenTelemetry. Here is where they diverge.

### No Per-Signal Pricing

Dash0 bills per million spans, log records, and metric data points. OpenObserve prices by volume ingested, so chatty services don't inflate your bill.

### Retention on Your Terms

Dash0 retains logs and traces for 30 days. OpenObserve stores everything on your object storage (S3, GCS, Azure) with retention you configure.

### Self-Host or Cloud

Dash0 is SaaS-only. OpenObserve runs as a single binary, an HA Helm cluster in your VPC, air-gapped, or as our managed cloud.

### Open Source, Not Just Open Standards

Dash0 embraces open standards but the platform itself is closed source. OpenObserve's code is open; inspect it, extend it, run it yourself.

### Data Residency & Compliance

Regulated teams can keep telemetry inside their own cloud account or data center: no data ever has to leave your environment.

## See how OpenObserve replaces Dash0

Get a personalized walkthrough and see what volume-based pricing on object storage means for your telemetry bill.

- 30-minute personalized walkthrough
- No credit card required
- See your real migration path from Dash0

## Feature comparison

Two OpenTelemetry-native platforms, two very different models

| Feature | Dash0 | OpenObserve | Reference Links |
| --- | --- | --- | --- |
| Feature parity: logs, metrics, traces, dashboards, alerts | ✓ | ✓ | [Logs](/docs/user-guide/data-exploration/logs/#overview), [Metrics](/docs/user-guide/data-exploration/metrics/), [Traces](/opentelemetry/), [Dashboards](/docs/user-guide/analytics/dashboards/dashboards-in-openobserve/), [Alerts](/docs/user-guide/analytics/alerts/), [Pipelines](/docs/user-guide/data-processing/pipelines/pipelines/) |
| OpenTelemetry-native (OTLP ingestion) | ✓ | ✓ | [Send OTLP directly, no proprietary agent required](/opentelemetry/) |
| Open Source | ✗ Closed-source SaaS built on open standards | ✓ Source available on GitHub |  |
| Self-hosting / on-prem | ✗ SaaS only | ✓ Single binary, Helm HA cluster, or managed cloud | [Learn more about Self-hosting / on-prem](/docs/architecture/) |
| Query language | SQL + PromQL | SQL for logs/traces + PromQL for metrics | Familiar SQL: no query-language retraining |
| Pricing model | Per-signal: per million spans, log records, and metric data points | Volume-based ingestion, no per-signal or per-seat fees | [Learn more about Pricing model](/pricing/) |
| Log & trace retention | 30 days | Configurable: months or years on object storage | [Learn more about Log & trace retention](/docs/administration/maintenance/storage-management/) |
| Storage backend | Vendor-managed SaaS storage | Your own S3/GCS/Azure bucket (Apache Parquet) | [Learn more about Storage backend](/docs/administration/maintenance/storage-management/) |
| Ingest pipelines & transformation | Filtering and spam suppression controls | Full real-time and scheduled pipelines with VRL functions | [Learn more about Ingest pipelines & transformation](/docs/user-guide/data-processing/pipelines/pipelines/) |
| Frontend monitoring (RUM) | ✓ | ✓ | [Real User Monitoring](/docs/user-guide/rum/) |
| IAM & SSO | ✓ | ✓ | [SAML, OIDC, LDAP, role-based access](/docs/user-guide/account-administration/identity-and-access-management/) |
| Air-gapped / data residency deployments | ✗ | ✓ Runs entirely inside your environment |  |

## Migrating from Dash0

Because both platforms are OpenTelemetry-native, migration is mostly an endpoint change, not a re-instrumentation project.

### Repoint your OpenTelemetry collectors and SDKs

Your instrumentation already speaks OTLP. Update the exporter endpoint and auth headers in your OpenTelemetry Collector or SDK config to send to OpenObserve; dual-ship to both platforms during the transition. No code changes required.

### Rebuild dashboards and alerts

Both platforms support PromQL and SQL, so your metric queries and log/trace views largely carry over with minimal rewriting. Rebuild alert checks with equal or better granularity using our alerting engine.

### Cut over and set retention on your terms

Validate parity while running in parallel, then shift production traffic fully to OpenObserve. Configure object-storage retention for months or years of history instead of a fixed 30-day window. Our team can help accelerate this process.

> OpenObserve is super fast, definitely very lightweight, and you can get started with an initial POC in two to three minutes to be honest.
> - Ajith Natarajan, Lead Software Engineer, Radius.ai

- [Talk to Our Migration Team](#demo-form)
- [Read Migration Stories](/customer-stories/)

## Frequently Asked Questions

Common questions about switching from Dash0 to OpenObserve

### Is OpenObserve a good Dash0 alternative?

Yes, if you want the same OpenTelemetry-native approach with more control. Both platforms ingest OTLP directly and avoid proprietary agents. The differences: OpenObserve is open source, can be self-hosted anywhere (single binary to HA Kubernetes cluster), stores data on your own object storage in Apache Parquet, and prices by volume ingested rather than per million spans, logs, or metric data points. If you specifically want a fully managed SaaS and are happy with 30-day log/trace retention, Dash0 is a polished product; the trade-off is control and long-term economics.

### How does OpenObserve pricing compare to Dash0's?

Dash0 publishes consumption pricing per signal: per million metric data points and per million spans or log records, with no seat or base fees. That model is transparent, but costs scale with event counts; high-cardinality metrics, verbose logs, and chatty traces add up fast. OpenObserve prices by data volume ingested, backed by low-cost object storage, with no per-signal, per-seat, or per-host fees. For high-volume workloads, volume-based pricing on object storage is typically the more predictable and cheaper model. Run your own numbers on our pricing page.

### How hard is it to migrate from Dash0 to OpenObserve?

Easier than most migrations, because both platforms are OpenTelemetry-native. Your instrumentation doesn't change; you update the OTLP exporter endpoint and auth headers in your OpenTelemetry Collector or SDK configuration. Most teams dual-ship to both platforms for a few weeks, rebuild key dashboards and alerts, validate parity, then cut over. Both platforms support PromQL and SQL, so your queries largely carry over with minimal rewriting.

### Can I self-host OpenObserve? Does Dash0 offer self-hosting?

Dash0 is a SaaS-only product; there is no self-hosted option. OpenObserve runs anywhere: a single binary on a laptop, Docker, an HA cluster via Helm on Kubernetes, air-gapped environments, or our managed cloud. Data lives on your own S3, GCS, Azure Blob, or MinIO bucket, which matters for data residency, compliance, and regulated industries.

### What about data retention?

Dash0 retains spans, logs, and web events for 30 days (metrics for 13 months). OpenObserve stores telemetry in compressed Apache Parquet on object storage, so retention is a setting, not a pricing tier; keeping months or years of logs and traces costs object-storage rates, not premium retention fees.

### Will I lose features moving from Dash0 to OpenObserve?

Core observability is covered: logs, metrics, traces, dashboards, alerts, RUM, and ingest pipelines. Dash0 has strengths worth acknowledging: a clean OTel-first UX, Perses-compatible dashboards, and built-in synthetic monitoring. If synthetic checks are critical, you'd pair OpenObserve with a dedicated synthetics tool. In exchange you gain SQL querying, full pipelines with VRL transforms, self-hosting, configurable long-term retention, and open source.

### Is OpenObserve enterprise-ready?

Yes. OpenObserve is SOC2 Type II certified and ISO 27001 compliant. We serve thousands of deployments, including Fortune 100 enterprises; our largest single customer ingests more than 4 PB of data per day, with individual clusters sustaining over 1 PB/day. Enterprise features include RBAC, SSO, sensitive data redaction, and dedicated support.

## Ready to See the Difference?

Get a personalized demo based on your current Dash0 usage

## OpenObserve: the open-source Dash0 alternative

An open-source, OpenTelemetry-native observability platform you can run anywhere, with object-storage economics and no per-signal pricing. Radius.ai got started with a working POC in minutes, not months. Also evaluating other tools? See how OpenObserve compares to [Datadog](/datadog-alternative/), [Honeycomb](/honeycomb-alternative/), [SigNoz](/signoz-alternative/).

- Volume-based pricing: no per-span or per-log-record fees
- Retention you control on your own object storage
- Self-hosted or cloud: your data, your control