Open-source Amazon OpenSearch Service alternatives

Elasticsearch is a distributed, RESTful search engine written in Java. It stores documents as JSON, indexes them so they can be searched quickly, and exposes everything through an HTTP API, which makes it a common backbone for site search, application search and log analytics. It is built on the Apache Lucene library.

Beyond full-text queries it supports filtering, aggregations for analytics and, in recent versions, vector search for semantic and AI-assisted retrieval. Data is spread across nodes in a cluster using shards and replicas, so capacity and resilience grow by adding machines. It is the core of the Elastic Stack, usually paired with Kibana for exploration and with ingestion tools for logs and metrics.

The repository describes the project as free and open source, while its license metadata is listed as 'Other', so review the current license terms before relying on it commercially. It can be self-managed on your own servers or used as a managed service through Elastic Cloud.

Typesense is an open-source search engine for building fast, typo-tolerant search experiences in websites and applications. It is pitched as an open-source substitute for Algolia and Pinecone, and as a simpler option than Elasticsearch. It keeps its index in memory and is written in C++.

The project's feature topics include typo tolerance and fuzzy matching, full-text search, faceting, geosearch, synonyms, merchandising rules and instant search, and it supports semantic and hybrid search. Public demos let you try it on large datasets, such as 32 million songs from MusicBrainz, 28 million books from OpenLibrary, a recipe set, Linux kernel commit messages, an ecommerce store experience, a geosearch listing browser and semantic search over Hacker News comments.

Typesense is licensed under GPL-3.0 and can be run on your own servers, and a managed Typesense Cloud service is also available. Documentation, a roadmap, a Slack community and video introductions are provided. It suits developers who want Algolia-style search with the option to self-host and keep costs predictable.

ZincSearch is a search engine that performs full-text indexing, written in Go with a Vue-based web interface. It is positioned as a lightweight alternative to Elasticsearch that needs far fewer resources and avoids the large number of settings that Elasticsearch tends to require tuning. The author says you can have it running within a couple of minutes.

It uses the bluge indexing library, through a fork, under the hood. For applications that simply ingest documents through an API and search them, it can serve as a drop-in replacement for Elasticsearch, though Kibana is not supported and ZincSearch ships its own UI instead. The README includes an important note: if your goal is log and security search rather than adding search to an app or website, the same author points people to the OpenObserve project, which was built for that case.

ZincSearch is at a 1.0 beta release, and its license is listed as 'Other' on GitHub, so check the repository for terms. It suits developers who want application search with a small footprint, such as site search or in-app lookup, and who are comfortable running a service themselves.

OpenSearch is an open-source search and observability suite that makes large amounts of unstructured data searchable and analyzable. It is a distributed engine with a RESTful API, written in Java, and developed in the open by the OpenSearch community under the Apache 2.0 licence.

Teams use it for full-text search, log and event analytics, and monitoring of applications and infrastructure. The project provides downloads, documentation, forums and Slack channels, and it follows a formal release and maintainer process. Because it is distributed, clusters can be scaled across multiple nodes as data volumes grow.

OpenSearch includes certain Apache-licensed code derived from Elasticsearch, as its trademark notice explains. You can run it on your own servers, or use managed services offered by cloud providers. Security issues are reported privately by email rather than through public issues, and the project maintains a code of conduct for contributors.

Manticore Search is an open-source database built specifically for search. It supports full-text queries, vector retrieval and a hybrid mode that combines the two in a single query, with real-time indexing and an SQL interface. The project is written in C++ and presents itself as an alternative to Elasticsearch.

Queries run on multiple threads and use all available CPU cores to keep response times low. Features listed in the README include full-text search that handles both small and large datasets, hybrid search for better relevance, and auto-embeddings that create vectors from text and can break long documents into chunks. Its topics reference BM25, MySQL protocol compatibility, JSON, stream filtering and a Sphinx heritage.

The project publishes reproducible benchmarks comparing it with other engines, and notes that results depend on the workload. Manticore Search is licensed under GPL-3.0, runs on your own infrastructure and is offered with documentation, courses, a forum and community chat. It suits teams building site search, log search or semantic search who want a lighter-weight engine than a full Elastic stack.

Quickwit is an open-source, cloud-native search engine designed for observability, covering log management and distributed tracing, with metrics support listed on the roadmap. It is written in Rust, builds on the Tantivy search library, and is meant to search large volumes of data kept in cheap object storage.

It provides full-text search and aggregation queries, schemaless or strict-schema indexing, and a RESTful API that is compatible with a large subset of the Elasticsearch and OpenSearch APIs, so existing clients can often be reused. It is native to Jaeger and OpenTelemetry, works as a Grafana data source and can ingest from Kafka, Kinesis and Pulsar. Compute and storage are decoupled, with stateless indexers and searchers, and data can sit on Amazon S3, Azure Blob Storage or Google Cloud Storage.

Operational features include multi-tenancy with many indexes, partitioning, retention policies and delete tasks for GDPR use cases, and a Helm chart for Kubernetes. Quickwit is licensed under Apache-2.0 and you run it yourself. It suits teams looking for lower-cost log and trace storage with a familiar search API.

Vespa is a platform for applications that must pick a subset of data from a large, changing corpus, evaluate machine-learned models over it, organize and aggregate the results and return them quickly. Typical examples are search, recommendation and personalization, and the platform adds vector search and retrieval-augmented generation as well, as shown in its topics.

Doing this over large data sets distributed across many nodes and evaluated in parallel is hard, and Vespa handles it with high availability and performance. It handles searching, running inference over and organizing vectors, tensors, text and structured data while serving. According to the README, Vespa has been developed over many years and runs behind several large internet services.

The repository contains all the code needed to build and run Vespa yourself under the Apache 2.0 licence, and new releases are made from the master branch on weekday mornings. You can deploy applications to the Vespa Cloud service, which offers a free trial, or run your own instance following the getting started guide. It is written in Java and C++ and suits teams building large-scale search and recommendation systems.

Apache Solr is a search platform from the Apache Software Foundation, built on top of Apache Lucene. It lets organizations index large collections of documents and query them quickly, and its README describes support for full-text, vector and geospatial search used by many large organizations.

Solr is a server you run yourself and talk to over HTTP. It includes example configurations to get started, a reference guide with a deployment guide and tutorials, and an administration interface available on the local server once it is running. The project topics also link it to NoSQL-style storage, information retrieval and backend search engine use cases.

The software is written in Java and released under the Apache-2.0 licence, with downloads on the Apache site. It can be installed from a distribution, run with the official Docker image, or deployed on Kubernetes using the Solr Operator. Support comes through mailing lists, Slack and IRC. It suits engineering teams adding search to websites, catalogs or internal tools without relying on a hosted search vendor.