Obseria vs Grafana

Feature comparison

Obseria as a Grafana alternative: the short answer

Grafana is one of the most widely adopted open source dashboarding tools, and Grafana Labs has built a full observability stack around it: Mimir for metrics, Loki for logs, Tempo for traces and Pyroscope for profiles, often called the LGTM stack. You can run it yourself or use Grafana Cloud as a managed service. Obseria takes a different approach: one integrated platform with a single store and a single query language across all signals. If you are evaluating a Grafana alternative, the core question is whether you prefer composable open source building blocks or an integrated platform. This comparison is based on Grafana Labs' public pricing page and documentation as of October 2026.

Pricing model: platform fee plus usage vs. per signal

According to its pricing page, Grafana Cloud has three tiers. The Free tier is perpetual and includes monthly allowances for metrics series, logs, traces, profiles and several products, with 14-day retention. The Pro tier combines a monthly platform fee with usage-based billing: metrics are billed per active series, logs, traces and profiles per GB with separate components for processing, writing and retention, and products such as Kubernetes monitoring, application observability, frontend observability and the Grafana Assistant have their own units, partly per host-hour, per session or per active user. Enterprise starts with an annual spend commitment and adds custom retention, premium support and deployment options including bring your own cloud.

The free tier is genuinely useful, and Adaptive Metrics and Adaptive Logs help reduce cost by aggregating unused series and dropping unused log patterns. The challenge is that cost is spread across many dimensions; active series cardinality in particular can grow quickly with labels in Kubernetes.

Obseria keeps pricing to a few dimensions:

• Logs, spans and RUM web events: $0.060 per million ingested, $0.540 per million stored, 30-day retention.
• Metric data points: $0.020 per million ingested, $0.180 per million stored, 13-month retention.
• Synthetic API check runs: $0.20 per thousand. Intelligence agent credits: $0.60 each.
• Unlimited users, no host fees, every feature included. Data filtered in Obseria Pipeline costs ingestion only.

Architecture and data model

The LGTM stack uses a purpose-built backend per signal. Mimir is a horizontally scalable Prometheus-compatible metrics store, Loki indexes only labels and stores log lines compressed in object storage, and Tempo stores traces in object storage with minimal indexing. Each is queried with its own language: PromQL, LogQL and TraceQL. Grafana then correlates them in dashboards and Explore. This design is efficient and flexible, and Grafana can also visualize many external data sources in place.

Obseria Lake stores logs, metrics, spans, profiles, RUM events and security signals in one index-free lakehouse with schema-on-read and field-level privacy. OQL works across all signals in one pipeline, and PromQL and SQL are available for teams that prefer them:

fetch logs
| filter service == "payments"
| summarize count(), by:{level}

Obseria TopoGraph builds the topology of apps and infrastructure automatically, so correlation across signals does not depend on consistent label conventions in every team.

Open source, OpenTelemetry and lock-in

Grafana Labs' core projects, including Grafana, Loki, Tempo and Mimir, are open source under the AGPLv3 license, so you can self-host them and move between self-managed and Grafana Cloud. Grafana Cloud accepts OTLP natively, and Grafana Alloy is Grafana Labs' distribution of the OpenTelemetry Collector. Running the stack yourself, however, means operating several distributed systems, including scaling, upgrades and object storage.

Obseria is a managed commercial platform, so it does not offer a self-hosted open source edition. It addresses lock-in through OpenTelemetry instead: OTLP is the native protocol, semantic conventions are the data model, and you can export data via OTLP at any time. With existing OpenTelemetry SDKs, the first trace usually arrives within 15 minutes. If your data must stay in your own environment, Obseria can run in your own cloud account (BYOC).

AI and root-cause analysis

Grafana Cloud offers Grafana Assistant, an AI assistant for querying, building dashboards and investigating issues, and an investigations capability, both billed by usage on the Pro tier.

Obseria Intelligence combines causal, predictive and generative AI on top of TopoGraph. Agentic root-cause investigations return a chain of evidence that engineers can verify step by step. Agent credits cost $0.60 each, and customer data is never used for AI training.

Data residency and compliance

Grafana Cloud is available in multiple cloud regions, including Frankfurt on AWS, and the region of a stack cannot be changed after creation. Self-hosting the open source stack gives full control over location.

Obseria stores data in the EU (Frankfurt, Dublin), the UK (London) or your own cloud account (BYOC), and is certified to ISO 27001, 27017 and 27018, SOC 2 Type II, BSI C5 for the EU region and TISAX, with support for DORA, NIS2 and GDPR, automatic PII masking, audit logs and a 99.95% SLA.

Migrating from Grafana to Obseria

If you collect with OpenTelemetry or Grafana Alloy, add Obseria as an additional OTLP destination and run both side by side. Because Obseria supports PromQL, many metric panels and alert rules carry over with little change; LogQL and TraceQL queries translate into OQL. Start with the dashboards and alerts your on-call teams depend on, validate them in parallel, then move service by service. The 14-day free trial requires no credit card.

If you self-host the LGTM stack today, factor the operational effort into the comparison: compute, object storage, upgrades and on-call time for the observability stack itself are real costs that a managed platform removes.

Which tool fits you?

Switching in three steps

1. Add an OTLP destination. Configure your OpenTelemetry Collector or Grafana Alloy to send data to Obseria in parallel.
2. Reuse PromQL. Carry over metric panels and alert rules via PromQL; translate LogQL and TraceQL queries into OQL.
3. Switch service by service. Validate on-call dashboards and alerts in parallel, then move teams over one at a time.