A company learns to collect more data, then discovers it can no longer afford to look at it. This is an odd predicament for a business that is paying for observability. Yet it is familiar to engineers: trim the logs, sample the traces, shorten the retention period. The invoice improves. The evidence disappears. OpenObserve was built around the suspicion that this bargain is backwards.
- OpenObserve combines logs, metrics, traces and frontend monitoring in one system.
- Its Rust and Parquet architecture stores compressed telemetry in object storage.
- There is a free open-source edition, a managed cloud service and paid enterprise options.
- Its best proof is in customer migrations; the savings reported there are customer-specific.
Founder Prabhat Sharma had spent time as an AWS solutions architect, seeing customers wrestle with the familiar list: Elasticsearch shards and mappings, backups, upgrades, too many servers, and a new query language for each tool. When he started OpenObserve in 2022, his answer was unusually concrete. Put the data in inexpensive object storage, compress it, and make the engine do more of the fussy work. The first public explanation in 2023 promised a self-hosted setup measured in minutes and a platform that could search logs with SQL.
The expensive part was keeping the evidence
OpenObserve is an observability platform: software that collects the records an application leaves behind and helps teams work out why something slowed, failed or behaved strangely. Logs record events. Metrics show measurements over time. Traces follow requests through services. Browser monitoring adds the user's side of the story. Engineers can search, chart, alert on and correlate these signals in one place.
The distinguishing choice sits below the dashboard. Ingesters convert incoming data to Apache Parquet, a compressed columnar format, and write it to an object store such as Amazon S3. A querier reads it when an engineer searches. Routers, compactors and schedulers have separate jobs in the distributed version; a single-node mode is available for smaller deployments and tests. That split lets a team expand ingestion or search capacity without treating every stored byte as a reason to add another permanent search server.
collector
ingester
object storage
alert
The diagram leaves out buffering, metadata and compaction. Those details matter in production; the storage decision explains the bill.
This is a business model as much as an architecture. The open-source core can be self-hosted. OpenObserve Cloud sells managed operation, with a published pay-as-you-go price starting at $0.50 per GB ingested and $0.01 per GB queried, plus retention terms. The company also sells enterprise features and support, including options to keep data in a customer's own bucket or cloud account. Seats are unlimited on its published plans. A team deciding between it and Datadog, Splunk, Elastic, New Relic or a Grafana-based stack is therefore comparing operations and control as well as line-item prices.
A migration measured in minutes
DevZero offers a useful test because its problem was prosaic. Its engineers were using Datadog to watch globally distributed Kubernetes and inference workloads. The company says its monthly bill varied by 15% to 20% as code and logging volume changed. CEO Debo Ray described the result plainly: “You have a surprise that happens at the end of the month when you get that invoice.” A surprise invoice has a way of turning diagnostic data into a suspect.
The team exported Datadog dashboards as JSON and imported them with OpenObserve's migration tooling. It translated a few proprietary functions into SQL with help from OpenObserve staff, deployed a Helm chart, and kept both systems running while it checked the new one. DevZero reports that the move took under an hour and reduced its observability spend fourfold. That is a customer account, not a universal rate card: workload, retention, query volume and the labor of running infrastructure will change the arithmetic elsewhere.
Jidu, which works on autonomous-driving systems, faced a different failure. It says its Elasticsearch stack forced it to sample only one in ten traces. After moving, the company reported full trace capture and lower storage requirements. Its example illustrates what a cheaper archive can buy besides a smaller invoice: an engineer investigating the one anomalous request may actually find it. Uno.ai offers another lesson. Because it had instrumented with OpenTelemetry and StatsD instead of tying every signal to one vendor, its team described the switch largely as changing the destination.
“When we had to move to OpenObserve, honestly, we didn’t have to do much work.”Shashank Tiwari, Uno.ai
The question after cheaper storage
By April 2026, OpenObserve said more than 7,000 organizations were running its software and the project had crossed 19,000 GitHub stars. Nexus Venture Partners and Dell Technologies Capital led a $10 million Series A. Those are adoption and financing claims from the company, but they explain why its pitch has shifted. Once more data can be kept, somebody must make sense of it.
The 2026 release added beta AI SRE and assistant features, anomaly detection and LLM observability. The assistant can help create queries, dashboards and alerts; the incident agent is intended to investigate signals across the platform. It is a plausible next step for a company whose first argument was “keep the evidence.” OpenObserve has also described internal AI systems that draft documentation and generate tests, with human review before merging. That says something about its engineering culture: automation is welcome, but it still has to face a running copy of the software.
There are conditions under which the OpenObserve approach asks more of the buyer. Self-hosting a high-availability cluster requires Kubernetes, PostgreSQL, NATS and object storage, not just an appealing single-binary demo. Object storage can trade some read latency for durability and lower cost. Cloud users should model not only ingestion but frequent queries and extended retention. And a team dependent on proprietary dashboards or integrations may have translation work, as DevZero did. These are engineering tradeoffs, the kind best tested with one's own telemetry before signing a longer contract.
The practical lesson is modest and useful. Keep instrumentation portable. Run a parallel migration. Compare a full month of ingest, query and retention costs. Measure whether the searches that matter still return quickly enough. OpenObserve's larger provocation remains: a monitoring tool should make it easier to investigate an incident, not give the finance department a reason to erase the clues first.