OpenSearch Dashboards vs. Kibana: Differences After the Fork for Magento Search Analytics

1. Background of the fork: from one open source project to a dual landscape

In January 2021, Elastic changed the license of Elasticsearch and Kibana from the Apache License 2.0 to a combination of the Elastic License and the Server Side Public License, aiming to stop commercial cloud providers from marketing the code as their own managed service without contributing back to the project. Amazon Web Services responded in April 2021 by announcing OpenSearch, a fork based on the last Apache 2.0 licensed state of Elasticsearch 7.10.2 and Kibana 7.10.2.

Since then, both projects have moved forward separately: Kibana is developed exclusively by Elastic under the new license, while OpenSearch Dashboards is maintained under the OpenSearch Software Foundation as a fully independent, fully open source project. For Magento operators that means feature sets, release cadences, and the plugin ecosystem have increasingly diverged since their shared starting point.

2. Comparing the licensing model directly

Kibana has been distributed under the Elastic License 2.0 since version 7.11, which makes the source code viewable but restricts certain uses, such as offering Kibana as a managed service to third parties. For the internal operation of a Magento project this restriction usually does not matter, but it does matter for hosting providers who want to offer Kibana as part of a managed search product.

OpenSearch Dashboards is entirely licensed under the Apache License 2.0 and is therefore free of any usage restrictions, neither for internal operation nor for redistribution as part of a commercial product. Anyone reselling a Magento hosting offering with built in search analytics avoids any license uncertainty from the outset by choosing OpenSearch Dashboards.

3. Feature divergence in visualizations since the fork

Kibana introduced Kibana Lens, a modern drag and drop visualization tool that makes complex aggregations accessible without manual DSL knowledge, and keeps extending it with new chart types and AI assisted suggestions. OpenSearch Dashboards offers a similar but functionally narrower toolbox through its classic Visualize editors, which reliably cover core chart types such as bar, line, and pie charts as well as data tables, but hit limits sooner on very complex, multi-level aggregations.

For typical Magento reports such as the most searched terms, click-through rate per search result position, or the distribution of layered navigation filters, the aggregation visualizations available in OpenSearch Dashboards are usually entirely sufficient in practice. For highly nested reports, for example combining a time series, category, and revenue dimension in a single chart, Kibana Lens offers noticeably more comfort.

4. Plugin ecosystem and compatibility between the two projects

Kibana plugins are built against the internal Kibana plugin API, which can change structurally with every major Kibana release, forcing third party plugins to be regularly adapted. The official Elastic plugin directory is extensive, though many advanced features such as machine learning based anomaly detection are part of paid Elastic subscription tiers rather than available as a free plugin.

OpenSearch Dashboards follows a similar plugin architecture, but has a smaller, fully open source ecosystem in which features like anomaly detection, alerting, and index management ship as official, free plugins from the OpenSearch Software Foundation. Kibana plugins cannot be used directly in OpenSearch Dashboards because of the API divergence since the fork, and OpenSearch specific plugins do not work in Kibana either.

5. Differences in the Dev Tools console

Both tools provide an interactive console for issuing raw query DSL requests against the cluster, which is essential for debugging Magento search queries. The Kibana console meanwhile offers more extensive autocompletion, including context aware suggestions for newer query types that first land in Elasticsearch and arrive in OpenSearch only later, if at all.

The Dev Tools console in OpenSearch Dashboards fully covers the functionality relevant to Magento, meaning bool queries, function score queries, aggregations, and mapping queries, and barely feels different from the Kibana variant during everyday debugging. Differences mostly show up around very new query features, which tend to land on the Elasticsearch side first.

6. A practical example: building a Magento search analytics dashboard

A simple search analytics dashboard starts with an index pattern pointed at the catalog index, followed by a visualization that plots the most frequent search terms as a bar chart against hit count. Combined with a second visualization for average response time per request, this results in a compact overview dashboard covering both relevance and performance aspects of storefront search.

In OpenSearch Dashboards, such a dashboard is assembled through the Visualize and Dashboard menu items, with saved objects such as index patterns and individual visualizations managed as standalone, exportable elements. This export mechanism is well suited for versioning a Magento dashboard built once as an NDJSON file and moving it to further environments.

# Export saved objects (index pattern + dashboard) from OpenSearch Dashboards
curl -s -X POST "https://dashboards.example.com/api/saved_objects/_export" \
  -H "osd-xsrf: true" -H "Content-Type: application/json" \
  -u admin:changeme \
  -d '{"type": ["index-pattern", "dashboard", "visualization"]}' \
  -o magento-search-dashboard.ndjson

7. Alerting and anomaly detection compared

OpenSearch ships with the Alerting plugin and the Anomaly Detection plugin, two fully open source components that can be used directly for Magento search monitoring, for example to automatically trigger a notification when click-through rate suddenly drops or the number of zero-result searches spikes. Both plugins are part of the standard installation at no extra cost.

Kibana offers comparable functionality through Elastic Observability and machine learning features, which are restricted or paid depending on the booked subscription tier. For Magento projects with a limited budget, where alerting mainly needs simple threshold rules rather than sophisticated anomaly detection, the functionality OpenSearch offers without licensing cost is usually sufficient.

8. The migration path between the two tools

Saved objects cannot be transferred one to one between Kibana and OpenSearch Dashboards without loss, because both projects have evolved their own internal object formats since the fork, and newer Kibana visualization types such as Lens based charts have no direct equivalent in OpenSearch Dashboards. An export and import through the NDJSON interface only works reliably in practice for older, classic visualization types still supported on both sides.

For a migration project it therefore pays to avoid blindly importing dashboards, and instead rebuild the most important Magento reports in the target tool while checking whether more modern visualization options available in that target tool allow for a better presentation than the original Kibana or OpenSearch Dashboards version.

9. Decision guide: which tool to choose for Magento search analytics

Anyone already running an OpenSearch cluster for Magento search should use OpenSearch Dashboards for consistency, since both components are developed in lockstep and version jumps happen in sync. Anyone who has already invested in an Elastic Stack with a paid subscription, for instance because of machine learning features used for other use cases, tends to benefit more from Kibana as a unified analytics surface.

For pure Magento search analytics without broader observability requirements, the feature set of OpenSearch Dashboards is usually sufficient, especially when alerting is mainly based on simple thresholds and licensing cost matters. For very demanding, highly customized visualizations, Kibana Lens remains the more comfortable tool.

10. Summary

11. FAQ: Dashboards vs. Kibana