# Comparison to alternatives
# Table of contents
# About MeiliSearch
There are many search engines on the web, both open-source and otherwise. Deciding which search solution is the best fit for your project is very important, but also difficult. In this article, we'll go over the differences between MeiliSearch and other search engines:
In the comparison table, we present a general overview of the differences between MeiliSearch and other search engines
Finally, we end this article with an in-depth analysis of the broader search engine landscape
Please be advised that many of the search products described below are constantly evolving—just like MeiliSearch. These are only our own impressions, and may not reflect recent changes. If something appears inaccurate, please don't hesitate to open an issue or pull request (opens new window).
# Comparison table
# General Overview
|Source code licensing||MIT (opens new window) |
|Closed-source||GPL-3 (opens new window) |
(Not open-source (opens new window))
|Built with||Rust |
Check out why we believe in Rust (opens new window).
|Data storage||Disk with Memory Mapping -- Not limited by RAM||Limited by RAM||Limited by RAM||Disk with RAM cache|
# Integrations and SDKs
Note: we are only listing libraries officially supported by the internal teams of each different search engine.
Can't find a client you'd like us to support? Submit your idea or vote for it (opens new window) 😇
|PHP client (opens new window)||✅||✅||✅||✅|
|Python client (opens new window)||✅||✅||✅||✅|
|Ruby client (opens new window)||✅||✅||✅||✅|
|Java client (opens new window)||✅||✅||✅||✅|
|Swift client (opens new window)||✅||✅||🔶 |
|.NET client (opens new window)||✅||✅||❌||✅|
|Rust client (opens new window)||✅||❌||🔶 |
|Go client (opens new window)||✅||✅||✅||✅|
|Dart client (opens new window)||✅||✅||✅||❌|
|Symfony (opens new window)||✅||✅||❌||❌|
|Django (opens new window)||❌||✅||❌||❌|
|Rails (opens new window)||✅||✅||🔶 |
|Official Laravel Scout Support (opens new window)||✅||✅||❌||❌|
|UI Search Kit (opens new window)||✅||✅||✅||✅|
|Docsearch (opens new window)||✅||✅||✅||❌|
|Strapi (opens new window)||✅||✅||❌||❌|
|Gatsby (opens new window)||🔶 |
|Firebase (opens new window)||🔶 |
# Document schema
Automatic schema detection is supported but needs to be specified
|Automatic document ID detection||✅||❌||❌||❌|
|Native document formats|| || || |
|Typo tolerant||✅||✅||✅||🔶 |
Needs to be specified by fuzzy queries
|Orderable ranking rules||✅||✅||🔶 |
Tie-breaking order is limited by a unique scoring rule
|Custom rules||✅||✅||🔶 |
Limited to one default sorting rule
Function score query
|Query field weights||✅||✅||✅||✅|
|Automatic language detection||✅||✅||❌||❌|
|All language supports||✅||✅||❌ |
Only space separated
|API Key Management||✅||✅||✅||✅|
|Tenant tokens & multi-tenant indexes||Q1 2022 |
Configurable end-user hard filters per index
Hard filters are not configurable per index for an end-user tenant key
Hard filters are not configurable per index for an end-user tenant key
|Multi-index search||Early 2022||✅||✅||✅|
|Exact phrase search||✅||✅||❌||✅|
|Sort by||✅||🔶 |
Limited to one
Does not support sort on string field
Support complex filter queries with an SQL-like syntax.
Does not support
Does not support
|Distinct attributes |
De-duplicate documents by a field value
Bucket documents by field values
|Mini Dashboard (opens new window)||✅||🔶 |
|Official 1-click deploy||✅ |
DigitalOcean (opens new window)
Platform.sh (opens new window)
Only for the cloud-hosted solution
|Official cloud-hosted solution||Join the beta (opens new window)||✅||✅||✅|
|High availability||Expected 2022||✅||✅||✅|
|Upgrade path||Documents need to be reindexed||N/A||Documents need to be reindexed||Documents need to be reindexed|
|Maximum number of indexes||No limitation||No limitation||No limitation||No limitation|
|Maximum index size||100GB default, configurable||128Gb||Constrained by RAM||No limitation|
|Maximum words per attribute||No limitation||No limitation||No limitation||No limitation|
|Maximum document size||No limitation||100KB, configurable||No limitation||100KB default, configurable|
|GitHub stars of the main project||20K||N/A||9K||57K|
|Number of contributors on the main project||75||N/A||20||1,700|
|Public Slack community size||1K||N/A||400||11K|
|Free support channels||Instant messaging / chatbox (2-3h delay), |
public Slack community,
GitHub issues & discussions,
|Instant messaging / chatbox, |
public community forum
|Instant messaging/chatbox (24h-48h delay), |
public Slack community,
|Public Slack community, |
public community forum,
|Paid support channels||Support is free!||Emails||Emails, |
|Web support, |
# Approach comparison
# MeiliSearch vs Elasticsearch
Elasticsearch has been designed as a backend search engine and, although it is not at first suited for this purpose, is commonly used to build search bars for the end-users.
Unlike Elasticsearch, which is a general search engine, MeiliSearch focuses on delivering a specific kind of features.
Elasticsearch can handle search through massive amounts of data and perform text analysis. In order to make it effective for end-user searching, you need to spend time understanding more about how Elasticsearch works internally to be able to customize and tailor it to fit your needs.
MeiliSearch is intended to deliver performant instant search experiences aimed at end-users. However, processing complex queries or analyzing very large datasets is not possible.
Elasticsearch can sometimes be too slow if you want to provide a full instant search experience. Most of the time, it is significantly slower in returning search results compared to MeiliSearch.
MeiliSearch is a perfect choice if you need a simple and easy tool to deploy a typo-tolerant search bar that provides a prefix searching capability, makes search intuitive for users, and returns them their results instantly with near-perfect relevance.
# MeiliSearch vs Algolia
MeiliSearch was inspired by Algolia's product and the algorithms behind it. We indeed studied most of the algorithms and data structures described in their blog posts in order to implement our own. MeiliSearch is thus a new search engine based on the work of Algolia and recent research papers.
It provides similar features and reaches the same level of relevance just as quickly as its predecessor.
Contrary to Algolia, MeiliSearch is open-source and written in Rust, a systems-level and modern programming language, that allows to rapidly build features. Rust also enables portability and flexibility, which makes the deployment of our search engines inside Virtual Machines, containers, or even Lambda@Edge, a seamless operation.
One of Algolia's major assets is the robust worldwide infrastructure that they offer to their customers.
MeiliSearch currently delivers a search engine and is not in a position to provide a competitive infrastructure yet. However, we aim it to be much more simple to deploy and maintain than Algolia's.
# A quick look at the search engine landscape
# Open source
Apache Lucene is a free and open-source search library used for indexing and searching full-text documents. It was created in 1999 by Doug Cutting, who had previously written search engines at Xerox's Palo Alto Research Center (PARC) and Apple. Written in Java, Lucene was developed to build web search applications such as Google and DuckDuckGo, the last of which still uses Lucene for certain types of searches.
Lucene has since been divided into several projects:
- Lucene itself: the full-text search library.
- Solr: an enterprise search server with a powerful REST API.
- Nutch: an extensible and scalable web crawler relying on Apache Hadoop.
Since Lucene is the technology behind many open source or closed source search engines, it is considered as the reference search library.
Sonic is a lightweight and schema-less search index server written in Rust. Sonic cannot be considered as an out-of-the-box solution and, compared to MeiliSearch, it does not ensure relevancy ranking. Indeed, it does not store any documents but is comprised of an inverted index with a Levenshtein automaton, which means any application querying Sonic has to retrieve the search results from an external database using the IDs that are returned and then apply some relevancy ranking.
Its ability to run on a few MBs of RAM makes it a minimalist and resource-efficient alternative to database tools that can be too heavyweight to scale.
Like MeiliSearch, Typesense is a lightweight open-source search engine optimized for speed. We are currently re-evaluating its features and functionality to better understand how it compares with MeiliSearch.
# Lucene derivatives
Solr is a subproject of Apache Lucene, created in 2004 by Yonik Seeley, and is today one of the most widely used search engines available worldwide. Solr is a search platform, written in Java, and built on top of Lucene. In other words, Solr is an HTTP wrapper around Lucene's Java API, meaning you can leverage all the features of Lucene by using it. In addition, Solr server is combined with Solr Cloud, providing distributed indexing and searching capabilities, thus ensuring high availability and scalability. Data is shared but also automatically replicated.
Furthermore, Solr is not only a search engine; it is often used as a document-structured NoSQL database. Documents are stored in collections, which can be comparable to tables in a relational database.
Due to its extensible plugin architecture and customizable features, Solr is a search engine with an endless number of use cases even though, since it can index and search documents and email attachments, it is specifically popular for enterprise search.
# Bleve & Tantivy
Bleve and Tantivy are search engine projects, respectively written in Golang and Rust, inspired by Apache Lucene and its algorithms (e.g., tf-idf, short for term frequency-inverse document frequency). Such as Lucene, both are libraries to be used for any search project; however they are not ready-to-use APIs.
Elasticsearch is a search engine based on the Lucene library and is most popular for full-text search. It provides a REST API accessed by JSON over HTTP. One of its key options, called index sharding, gives you the ability to divide indexes into physical spaces in order to increase performance and ensure high availability. Both Lucene and Elasticsearch have been designed for processing high-volume data streams, analyzing logs, and running complex queries. You can perform operations and analysis (e.g., calculate the average age of all users named "Thomas") on documents that match a specified query.
Today, Lucene and Elasticsearch are dominant players in the open-source search engine landscape. They both are solid solutions for a lot of different use cases in search, and also for building your own recommendation engine. They are good general products, but they require to be configured properly to get similar results to those of MeiliSearch or Algolia.
# Closed source
Algolia is a company providing a search engine on a SaaS model. Its software is closed source. In its early stages, Algolia offered mobile search engines that could be embedded in apps, facing the challenge of implementing the search algorithms from scratch. From the very beginning, the decision was made to build a search engine directly dedicated to the end-users, i.e., implementing search within mobile apps or websites.
Algolia successfully demonstrated over the past few years how critical tolerating typos was in order to improve the users' experience, and in the same way, its impact on reducing bounce rate and increasing conversion.
Apart from Algolia, a wide choice of SaaS products are available on the Search Engine Market. Most of them use Elasticsearch and fine-tune its settings in order to have a custom and personalized solution.
Swiftype is a search service provider specialized in website search and analytics. Swiftype was founded in 2012 by Matt Riley and Quin Hoxie, and is now owned by Elastic since November 2017. It is an end-to-end solution built on top of Elasticsearch, meaning it has the ability to leverage the Elastic Stack.
Doofinder is a paid on-site search service that is developed to integrate into any website with very little configuration. Doofinder is used by online stores to increase their sales, aiming to facilitate the purchase process.
Each Search solution fits best with the constraints of a particular use case. Since each type of search engine offers a unique set of features, it wouldn't be easy nor relevant to compare their performance. For instance, it wouldn't be fair to make a comparison of speed between Elasticsearch and Algolia over a product-based database. The same goes for a very large full text-based database.
We cannot, therefore, compare ourselves with Lucene-based or other search engines targeted to specific tasks.
In the particular use case we cover, the most similar solution to MeiliSearch is Algolia.
While Algolia offers the most advanced and powerful search features, this efficiency comes with an expensive pricing. Moreover, their service is marketed to big companies.
MeiliSearch is dedicated to all types of developers. Our goal is to deliver a developer-friendly tool, easy to install, and to deploy. Because providing an out-of-the-box awesome search experience for the end-users matters to us, we want to give everyone access to the best search experiences out there with minimum effort and without requiring any financial resources.
Usually, when a developer is looking for a search tool to integrate into their application, they will go for ElasticSearch or less effective choices. Even if Elasticsearch is not best suited for this use case, it remains a great open-source solution. However, it requires technical know-how to execute advanced features and hence more time to customize it to your business.
We aim to become the default solution for developers.