Open-source Azure AI Search 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.

Meilisearch is a search engine exposed through an API, written in Rust and designed to be dropped into applications and websites. It aims to give developers a good search experience quickly, with features that work out of the box rather than needing extensive tuning.

Its capabilities include hybrid search that combines semantic and full-text results, search-as-you-type with a target response time under 50 milliseconds, typo tolerance, filtering and faceted search, sorting and geosearch. The topics also describe it as a vector database for AI use cases. The project maintains example applications for movies, ecommerce with facets and filters, conversational home booking, personalization and a multi-tenant CRM, which show how the features fit together.

The license is listed as 'Other', so confirm the terms for your use case. Meilisearch can be run on your own infrastructure, and Meilisearch Cloud offers it as a managed service. It is a common pick for site search, product catalogs and in-app search where a lighter alternative to a large cluster is attractive.

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.

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.

ParadeDB upgrades Postgres so that application records, keyword search, vector lookups and aggregate queries can share one database. It is built as a Postgres extension, home of pg_search, and uses standard SQL and ACID transactions, so there is no separate search engine to keep in sync.

A custom index delivers full-text and vector search with BM25 scoring, top-K queries, highlighting, tokenizers and filters, along with facets, aggregations, bucket and metrics queries, joins and columnar storage. Under the hood it builds on Rust libraries: pgrx bridges Postgres and Rust, Tantivy powers search and Apache DataFusion handles OLAP processing. Integrations cover Drizzle, Django, SQLAlchemy, Rails and EF Core, AI agent tooling such as MCP, and PaaS providers like Railway, Render, Fly.io and DigitalOcean.

You can try ParadeDB locally with Docker and psql, run it self-hosted, or use ParadeDB Cloud for a fully managed experience. It is written in Rust and licensed under AGPL-3.0. Teams use it to add search to an existing Postgres setup instead of adding Elasticsearch.

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.

RediSearch is a query and indexing engine for Redis. It lets you declare indexes over data stored in Redis and then query that data with a dedicated query language, adding secondary indexes, full-text search, vector similarity lookups, geospatial queries and aggregations to the key-value store.

It uses compressed inverted indexes to keep indexing fast and memory use low, and its search features include exact-phrase matching and fuzzy search. It began as a Redis module, and the repository lists Rust as its main language. Starting with Redis 8, the Redis Query Engine is built into Redis itself, so standalone releases of the module are no longer published and you do not install it separately. The README also notes that 32-bit systems are not supported.

The code is published under terms that GitHub lists as 'Other', so review the license before commercial use. RediSearch is used by running it inside your own Redis servers, or through the managed Redis cloud service the vendor offers. It suits teams that already rely on Redis and want search or vector lookups without adding another datastore.

SeekStorm is a native vector and lexical search engine written in Rust, available as an in-process library and as a multi-tenancy search server. Development started in 2015, it has been in production since 2020, it was ported to Rust in 2023, and it was open sourced in 2024. It is a work in progress and is released under the Apache-2.0 license.

Search capabilities include full-text BM25 search, vector and hybrid search, faceting, geosearch, and real-time indexing, according to its topics. There are REST clients for Rust, Python, TypeScript, C#, and Java, and an instant search adapter that redirects an existing Algolia InstantSearch.js frontend to a SeekStorm backend without rewriting the UI, plus a widget library for building a fresh interface.

SeekStorm suits developers who need embedded search inside an application, or a self-hosted search server for multiple tenants, as an alternative to hosted services like Algolia or heavier engines.

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.