OpenObserve review

OpenObserve is an observability platform for logs, metrics, traces and AI workloads. It brings those signals into one place for searching, dashboards, alerts and incident analysis, rather than treating each data type as a separate tool. The core is open source under AGPL-3.0, with cloud and enterprise offerings also listed on the pricing page.

For logs, OpenObserve supports SQL queries and uses columnar Parquet storage. It supports Prometheus-compatible metrics and distributed traces correlated with logs. Teams can build dashboards and configure real-time or scheduled alerts across telemetry. The product also lists an AI SRE agent for incident triage, root-cause correlation and postmortem drafts, plus LLM observability for prompts, completions, token use and cost.

You can run the open-source edition yourself or use managed cloud. The pricing page lists a 14-day cloud trial; it does not publish a fixed monthly cloud price.

The main draw is consolidation: logs, metrics and traces share a platform, with SQL log search, dashboards and alerts alongside them. That can be a simpler fit for teams trying to reduce the number of observability products they operate. OpenObserve also covers AI application telemetry, including prompt and completion traces, token usage and cost, rather than focusing only on infrastructure.

The self-hosted open-source edition is listed at $0 per month and is free forever. For teams that want managed hosting, Cloud Professional is pay-as-you-go: the pricing page lists $0.50 per GB ingested with an annual commitment, $0.01 per GB queried, 15 months of metrics retention and 30 days of non-metrics retention. It includes unlimited users and a 14-day trial.

A Reddit user reported querying several days of home-network logs in roughly two to four seconds on a local VM, but that is one user’s account, not a product benchmark. The same discussion includes questions about search speed and storage-read costs. Treat the anecdote as a lead to investigate, not a performance guarantee.

Cloud costs are not just an ingestion rate. The listed $0.50 per GB includes a 30% annual-commitment discount; queries cost $0.01 per GB, and extra non-metrics retention is $0.02 per GB for each additional 30 days. The pricing page does not publish a fixed monthly price, so estimate ingestion, query volume and retention before comparing it with a flat-rate alternative.

Access controls are another plan boundary to check. The pricing page lists SSO and RBAC for Cloud Enterprise and Self-Hosted Enterprise, not the free self-hosted open-source plan. Self-Hosted Enterprise is free up to 50 GB per day; above that, pricing requires contacting sales. A Reddit thread criticizes the placement of security features behind enterprise plans, though that is a user’s view, not a full assessment of the product’s security.

There is also a storage-versus-compute question. Reddit commenters questioned whether compressed Parquet storage could mean more work or cost when searching large datasets, and one said they could not find comparative compute benchmarks. The published material does not quantify that trade-off, so do not assume the storage-savings claim translates directly into lower total cost.

OpenObserve Cloud has no published fixed monthly price. The pricing page lists a pay-as-you-go rate of $0.50 per GB ingested with an annual commitment; that rate already includes a 30% discount. Queries add $0.01 per GB, so ingestion alone is not the full bill. Cloud Professional includes 15 months of metrics retention, 30 days of non-metrics retention, unlimited users and a 14-day trial. Extra non-metrics retention costs $0.02 per GB for each additional 30 days.

Cloud Enterprise is custom-priced, with volume discounts, BYOB for infinite retention, SSO, RBAC and premium support. That is a meaningful step up if you need those controls: they are not listed in Cloud Professional. Self-hosted Open Source is free, and Self-Hosted Enterprise is free up to 50 GB per day. Above that cap, contact sales; pricing is not published. The self-hosted enterprise tier lists SSO and RBAC. Price ingestion, query volume and retention before comparing cloud costs with a fixed-price alternative.

Treat a move to OpenObserve as a telemetry-pipeline migration, not just a dashboard swap. The product combines logs, metrics and traces, with SQL log queries, dashboards and alerts, so map which data sources, queries, alerts and views must move before changing collection. The published materials do not document migration tooling, data export formats or compatibility with another platform; confirm those details with OpenObserve before planning a cutover.

OpenObserve describes its self-hosted core as open source under AGPL-3.0, which gives teams an option to run it themselves. That does not establish that existing data or dashboards can be moved out cleanly. Its log storage uses columnar Parquet, but export support is not specified on the vendor's site. Cloud pricing can also complicate a switch: ingestion, queries and extended retention are separate costs, while enterprise pricing is custom. We were not given Reddit posts or comments about why users leave, so we can’t attribute common exit reasons to Reddit. Check data portability, access-control needs and total usage costs before committing.