{"id":1826,"date":"2026-10-07T08:23:10","date_gmt":"2026-10-07T11:23:10","guid":{"rendered":"https:\/\/www.linuxpro.com.br\/?p=1826"},"modified":"2026-10-07T08:23:10","modified_gmt":"2026-10-07T11:23:10","slug":"openobserve-vs-elk-kibana-beneficios-e-limites","status":"publish","type":"post","link":"https:\/\/www.linuxpro.com.br\/en\/2026\/10\/openobserve-vs-elk-kibana-beneficios-e-limites\/","title":{"rendered":"OpenObserve vs ELK\/Kibana: benefits and limits"},"content":{"rendered":"

\"Mascote<\/p>\n

A log platform should help resolve incidents, not become the team's most frequent incident. When Elasticsearch takes up a significant portion of the budget and maintaining indices, retention, and capacity requires constant attention, the OpenObserve deserves to be on the evaluation shortlist<\/strong>. Its main attractions are an architecture oriented toward object storage, an integrated interface for telemetry, and a simple path to get started on a single server.<\/p>\n

That does not mean \u201cOpenObserve is always better than ELK,\u201d nor that Kibana is just a disposable visualizer. The choice involves data, queries, integrations, security, and operational maturity. In this article, we will separate real benefits, limitations, and a proof-of-concept roadmap. We did not execute a comparative benchmark in this article<\/strong>; the characteristics were verified against the projects' documentation, and the evaluation recommendations are proposed for your environment.<\/p>\n

First: OpenObserve does not only compete with Kibana<\/h2>\n

ELK is the historical acronym for Elasticsearch, Logstash and Kibana: storage\/search, event processing and visual exploration. Today, an Elastic deployment can use Elastic Agent, OpenTelemetry and other ingestion paths, without necessarily including Logstash. It can also be self-managed, managed, or Serverless.<\/p>\n

OpenObserve combines ingestion, search, visualization and alerts focused on logs, metrics and traces. Therefore, the useful comparison is between complete observability solutions<\/strong>, not between a backend and a screen. To get to know the platform first, see our OpenObserve guide<\/a>. For the Elastic side, see the OpenTelemetry documentation<\/a>.<\/p>\n

Where are OpenObserve's most interesting benefits<\/h2>\n\n\n\n\n\n\n\n\n\n
Potential benefit<\/th>\nWhy it matters<\/th>\nWhat needs to be proven<\/th>\n<\/tr>\n<\/thead>\n
Retention with object storage<\/td>\nOpens room to keep history without concentrating all the cost on search node disks.<\/td>\nTotal cost, historical queries, traffic, and restore.<\/td>\n<\/tr>\n
Simple entry<\/td>\nA single-node deployment makes the lab and initial adoption easier.<\/td>\nCapacity limits, acceptable unavailability, and growth path.<\/td>\n<\/tr>\n
Integrated interface<\/td>\nSearch and visualization in the same product reduce initial setup.<\/td>\nThe dashboards and investigations the team actually uses.<\/td>\n<\/tr>\n
SQL in the investigation<\/td>\nLeverages existing knowledge of filters and aggregations.<\/td>\nFunction equivalence, types, query precision, and performance.<\/td>\n<\/tr>\n
Decoupled collection<\/td>\nPatterns and collectors allow evaluating another destination without rewriting the entire application.<\/td>\nProtocol compatibility, attributes, queues, and retry policy.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The word \u201cpotential\u201d matters: saving storage while losing a critical investigation is not an improvement. The decision must consider the service provided to the team, not just the number of components installed.<\/p>\n

Architecture: why storage changes the conversation<\/h2>\n

In OpenObserve, analytical data can be persisted as Parquet in object storage. Single-node can use SQLite and local disk or object storage. The documented HA design uses Kubernetes\/Helm and adds PostgreSQL for metadata, NATS for coordination, and separate roles for ingestion, query, routing, compaction, and scheduling. Official OpenObserve architecture<\/a>.<\/p>\n

\"Compara\u00e7\u00e3o
Conceptual view of a self-managed deployment, not a production recipe. Managed Elastic and Serverless may use different designs.<\/figcaption><\/figure>\n

It is not \u201call stateless.\u201d.<\/strong> Ingesters keep data in memory and WAL\/disk before forwarding; metadata remains essential. A healthy bucket does not automatically rebuild users, permissions, rules, and all operational information. Durability must be evaluated end-to-end, including the window before the data is forwarded.<\/p>\n

It would also be incorrect to say that Elasticsearch only uses local SSD. Elastic has data tiers<\/a> and searchable snapshots<\/a>, with deployment and licensing requirements. The comparison should use the configuration your team would actually purchase and operate, not the most expensive architecture on one side versus the most economical on the other.<\/p>\n

Benefit 1: reduce retention cost pressure<\/h2>\n

In many environments, only a small window of logs is queried daily, but the history needs to remain accessible for investigations. Separating query compute from storage can make this profile more economically attractive. The expected advantage of OpenObserve is especially relevant when the main pain point is keeping large volumes of telemetry for longer.<\/p>\n

However, the bucket price is only one line on the bill. Add temporary local storage, metadata, CPU, memory, cache, read and write operations, inter-zone transfer, data egress, backup, support, and labor hours. If the S3 storage is self-managed, include your disks, redundancy, upgrades, and recovery: S3 is an interface, not a promise of free infrastructure.<\/p>\n

We have already published the OpenObserve deployment on Ubuntu with external storage<\/a> and the RustFS installation<\/a>. They help set up the lab, but they do not replace a failure and restoration test.<\/p>\n

Don't compare it to an Elasticsearch frozen in the past<\/h3>\n

The logsdb<\/code> mode of Elasticsearch is aimed at log storage efficiency. The documentation states its default adoption for new data streams corresponding to logs-*-*<\/code> in eligible Elasticsearch 9.0 deployments and later. Clusters upgraded from 8.x have specific conditions; do not assume that old streams or indices have been automatically converted. Evaluate the correct mode, mapping, and retention policy before concluding that migration is the only solution. Data streams logs documentation<\/a>.<\/p>\n

Benefit 2: get started with less setup<\/h2>\n

For a small team, the time to the first useful investigation matters a lot. Installing a platform, ingesting events, and exploring them in its own interface can be more straightforward than designing a full distributed stack on day one. That is a good reason to try OpenObserve in a lab or a scoped service.<\/p>\n

The care is not to carry that initial simplicity over into a production promise. Single-node does not offer high availability just because an external bucket exists; a process failure still affects the service. In HA, the team needs to operate the dependencies and understand the behavior under failure. If nobody wants to take that on, also compare managed options from both providers.<\/p>\n

My suggestion is to record two architectures in the evaluation: the minimal one that meets the experiment and the one that would meet the real requirements. Pricing the first and promising the availability of the second produces a fictional budget.<\/p>\n

Benefit 3: SQL-oriented investigation and integrated telemetry<\/h2>\n

OpenObserve offers SQL for queries and features to work with metrics and traces. This can ease adoption by professionals used to analytical queries. For syntax and function details, use the project's SQL reference<\/a>, instead of assuming that every relational database dialect will be accepted.<\/p>\n

However, SQL is not exclusive to OpenObserve: Elasticsearch offers SQL<\/a> and the processing language ES|QL<\/a>. The advantage must be in the team's experience, the suitability of the queries, and the cost of running them\u2014not in claiming that the competitor only understands JSON.<\/p>\n

Having logs, metrics, and traces available does not automatically create correlation. Standardize the service name, environment, timestamps, and trace identifiers. If each application uses different names or removes the identifier during collection, no interface will fix that loss of context on its own.<\/p>\n

For infrastructure metrics, also check out our guide on Prometheus and Node Exporter<\/a>. A log migration does not require replacing, in the same project, all the metrics collection that already works.<\/p>\n

What \u201cwithout worrying about indexes\u201d cannot mean<\/h2>\n

OpenObserve is not a system without schema, types, or indices. There are stream configurations, schema evolution, and inverted indices with Tantivy. The benefit should be described as a possible reduction or change in administration work, not as the disappearance of decisions about data. See stream schema<\/a> and Tantivy indices<\/a>.<\/p>\n

In the proof of concept, mix valid events with inconsistent types, missing fields, large messages, and incorrect timestamps. Check what is rejected, transformed, or accepted with loss of information. A platform that appears to ingest faster because it discards part of the load is not doing the same work.<\/p>\n

Where Elastic and Kibana can still be the best choice<\/h2>\n