Elasticsearch Alternatives and Competitors Compared

Elasticsearch is used for three different jobs, and the alternatives split along those lines. Decide which job is yours before comparing engines.

It depends on what the licence was blocking. Elastic changed Elasticsearch and Kibana from Apache 2.0 to dual licensing under SSPL and the Elastic License in early 2021, and AWS responded with OpenSearch. On 29 August 2024 Elastic announced that it would add AGPL, an Open Source Initiative approved licence, as another option next to ELv2 and SSPL, saying it was adding an option and removing nothing. Elastic's download page for the current release, 9.5.6, still lists the Elastic License for the default distribution, and its subscriptions page notes that select features are also available under SSPL or Apache 2.0. Which release first shipped the AGPL option is something to confirm with Elastic.

In practice there are three cases. If you only run Elasticsearch internally, the Elastic License never stopped you. If you modify the source and offer it over a network, AGPL brings obligations that your counsel should read. If you offer Elasticsearch to others as a managed service, the Elastic License prohibits it, and that is the case the 2021 change was aimed at. None of this is legal advice.

OpenSearch kept the REST API and index format of Elasticsearch 7.10.2, so a 7.10-era application usually needs a client and endpoint change. Later estates are the harder case. Data written by Elasticsearch 8.x or 9.x normally moves by reindex, and the official Elasticsearch clients refuse to talk to anything that does not identify as Elasticsearch, so applications switch to OpenSearch clients. The shippers in front of the cluster need the same check: Logstash and Beats versions, not just the application. Saved dashboards, alerting rules and ingest pipelines are exported and tested, not assumed. ES|QL queries have no equivalent and are rewritten, in Query DSL, SQL or PPL.

OpenSearch is the right answer when the driver is the Apache 2.0 licence, Amazon OpenSearch Service, or an open security stack, and the workload is mostly Query DSL. It is the wrong answer if the team depends on Kibana's Observability or Security solutions or on Elastic's machine learning, because those do not follow. Amazon OpenSearch Service also charges extended support for old engine versions, so a hosted move is not free of a version clock.

Solr is the oldest of the options and still the best-understood one for catalogue and enterprise text search with faceting, highlighting and heavy analyzer chains. It is Apache 2.0, SolrCloud runs on ZooKeeper, and the current line is 10.1.0. The cost of choosing it from Elasticsearch is a rewrite: mappings become a schema, Query DSL becomes Solr query parsers or the JSON Request API, and aggregations become facets. Treat it as a re-platform of the search layer, with relevance tests that compare ranked results before and after, not as a configuration change.

Solr is a poor fit for log analytics, where the ecosystem around Elasticsearch, OpenSearch and ClickHouse is far ahead. Teams already on Solr 8 should read the version path before anything else, because Solr 8 is end of life and the next step is a planned jump.

Many Elasticsearch clusters hold logs, events or metrics that nobody ranks by relevance. They are filtered by time and field, grouped, counted and graphed. ClickHouse, an Apache 2.0 column-oriented SQL database, is built for that, and the move changes the question from "which search engine" to "which table design". The work is in the sorting key and partitioning, the ingest path (Kafka, a log shipper or a collector), the retention policy, and rebuilding dashboards on SQL. Do not expect a straight copy of an index mapping to perform, and do not use it where users type free text and expect ranked results.

A common pattern is staged: new data goes to ClickHouse while the Elasticsearch cluster ages out, and only the saved searches that still matter are rebuilt.

These appear on every shortlist, so they are here for completeness. AceMQ does not support them, and the licence notes come from each project's own repository.

• Meilisearch: a search engine for full-text, semantic and hybrid search with typo tolerance. The community edition is MIT; the enterprise edition is under a commercial licence or BUSL 1.1 and is not allowed in production without a commercial agreement.
• Typesense: an in-memory search engine, also typo tolerant. The server is GPL-3.0 and its FAQ says memory use grows with the indexed fields, so size it against your dataset.
• Algolia: hosted search as a service, with no cluster to run.
• Grafana Loki (AGPL-3.0-only) and VictoriaLogs (Apache-2.0): log-only stores built for cheap retention, not general search.
• Quickwit: a search engine for logs and traces on object storage, Apache-2.0 on its repository. Check the project's current maintenance status before you commit to it.

Meilisearch and Typesense suit a product or site search box on a dataset one server can hold. They are not substitutes for a large, sharded cluster.

1. Queries. Count them, rank them by traffic, and rewrite the top ones first. Relevance changes even when the syntax maps cleanly.
2. Mappings and analyzers. Custom analyzers, synonyms and multi-fields are the least portable part.
3. Clients and shippers. Application libraries, Logstash, Beats and agents.
4. Dashboards and alerts. Export what is used, retire what is not.
5. Lifecycle and security. Index lifecycle policies, roles, TLS and snapshots, each with a restore test.
6. The parallel run. Dual-write or replay traffic, compare results, and keep the old cluster until the comparison is boring.

A cost estimate that skips the first two items is usually wrong. AceMQ's assessment work produces this list per estate.

• You use Elastic's solutions. Kibana's Observability and Security apps, ES|QL and Elastic's machine learning do not follow you to any alternative here.
• You are on 8.x or 9.x and the cluster is healthy. A supported line with a licence you can live with is not a problem to solve.
• The motive is cost, not licence. Look at shard sizing, replicas, data tiers and retention first. Those change the bill more than the engine does.
• You want Elastic to run it. Elastic Cloud is the managed route, and Elastic's own support comes only from Elastic.

If you stay, you still choose how it is supported. AceMQ supports self-managed Elasticsearch from 7.x to 9.x.

AceMQ is an independent support, consulting and managed services company. It is not an Elastic or AWS partner and cannot sell or stand in for an Elastic subscription, so if you need Elastic's own support entitlement or want its paid features, that route is Elastic. AceMQ does not run a hosted search SaaS, and it does not rewrite application code, which stays with your developers or a code-modernization partner. What AceMQ does is assess the estate, recommend a destination with the reasoning, plan and run the parallel migration with your team, and support or operate the result at Tier 1 to Tier 4.

We compared the four engines AceMQ supports on eight criteria: what it is, licence, current line, best fit, what changes coming from Elasticsearch, main risk, hosting and AceMQ cover. We did not benchmark the engines, and nothing here is a performance claim. Versions, licences and dates change, so confirm them for the release you run.

Sources, all checked on 4 November 2026: Elastic's licence announcement, downloads and support-policy pages; OpenSearch's releases and about pages; the Apache Solr downloads page; and the GitHub repositories for ClickHouse, Meilisearch, Typesense and Quickwit.