# Comparison to alternatives

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:


Please be advised that many of the search products described below are constantly evolvingβ€”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

Meilisearch Algolia Typesense Elasticsearch
Source code licensing MIT (opens new window)
(Fully open-source)
Closed-source GPL-3 (opens new window)
(Fully open-source)
(Not open-source (opens new window))
Built with Rust
Check out why we believe in Rust (opens new window).
C++ C++ Java
Data storage Disk with Memory Mapping -- Not limited by RAM Limited by RAM Limited by RAM Disk with RAM cache

# Features

# 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) πŸ˜‡

SDK Meilisearch Algolia Typesense Elasticsearch
REST API βœ… βœ… βœ… βœ…
JavaScript client (opens new window) βœ… βœ… βœ… βœ…
PHP client (opens new window) βœ… βœ… βœ… βœ…
Python client (opens new window) βœ… βœ… βœ… βœ…
Ruby client (opens new window) βœ… βœ… βœ… βœ…
Java client (opens new window) βœ… βœ… βœ… βœ…
Swift client (opens new window) βœ… βœ… πŸ”Ά
.NET client (opens new window) βœ… βœ… ❌ βœ…
Rust client (opens new window) βœ… ❌ πŸ”Ά
Go client (opens new window) βœ… βœ… βœ… βœ…
Dart client (opens new window) βœ… βœ… βœ… ❌
Symfony (opens new window) βœ… βœ… ❌ ❌
Django (opens new window) ❌ βœ… ❌ ❌
Rails (opens new window) βœ… βœ… πŸ”Ά
Official Laravel Scout Support (opens new window) βœ… βœ… ❌ ❌
UI Search Kit (opens new window) βœ… βœ… βœ… βœ…
Docsearch (opens new window) βœ… βœ… βœ… ❌
Strapi (opens new window) βœ… βœ… ❌ ❌
Gatsby (opens new window) βœ… βœ… βœ… ❌
Firebase (opens new window) βœ… βœ… βœ… ❌

# Configuration

# Document schema
Meilisearch Algolia Typesense Elasticsearch
Schemaless βœ… βœ… πŸ”Ά
Automatic schema detection is supported but needs to be specified
Nested field support βœ… βœ… ❌ βœ…
Automatic document ID detection βœ… ❌ ❌ ❌
# Relevancy
Meilisearch Algolia Typesense Elasticsearch
Typo tolerant βœ… βœ… βœ… πŸ”Ά
Needs to be specified by fuzzy queries
Orderable ranking rules βœ… βœ… πŸ”Ά
Tie-breaking order is limited by a unique scoring rule
Custom rules βœ… βœ… πŸ”Ά
Limited to one default sorting rule
Function score query
Query field weights βœ… βœ… βœ… βœ…
Synonyms βœ… βœ… βœ… βœ…
Stop words βœ… βœ… ❌ βœ…
Automatic language detection βœ… βœ… ❌ ❌
All language supports βœ… βœ… ❌
Only space separated
# Security
Meilisearch Algolia Typesense Elasticsearch
API Key Management βœ… βœ… βœ… βœ…
Tenant tokens & multi-tenant indexes βœ…
Multitenancy support
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
Meilisearch Algolia Typesense Elasticsearch
Placeholder search βœ… βœ… βœ… βœ…
Multi-index search 2023 (opens new window) βœ… βœ… βœ…
Exact phrase search βœ… βœ… ❌ βœ…
Geo search βœ… βœ… βœ… βœ…
Sort by βœ… πŸ”Ά
Limited to one sort_by rule per index. Indexes may have to be duplicated for each sort field and sort order
Does not support sort on string field
Filtering βœ…
Support complex filter queries with an SQL-like syntax.
Does not support OR operation across multiple fields
Does not support OR operation across multiple fields
Faceted search βœ… βœ… βœ… βœ…
Distinct attributes
De-duplicate documents by a field value
βœ… βœ… βœ… βœ…
Bucket documents by field values
❌ βœ… βœ… βœ…
# Visualize
Meilisearch Algolia Typesense Elasticsearch
Mini Dashboard (opens new window) βœ… πŸ”Ά
Cloud product
Cloud product

# Deployment

Meilisearch Algolia Typesense Elasticsearch
Self-hosted βœ… ❌ βœ… βœ…
Official 1-click deploy βœ…
DigitalOcean (opens new window)
Platform.sh (opens new window)
❌ πŸ”Ά
Only for the cloud-hosted solution
Official cloud-hosted solution Join the beta (opens new window) βœ… βœ… βœ…
High availability Available with Meilisearch Cloud (opens new window) βœ… βœ… βœ…
Run-time dependencies None N/A None None
Backward compatibility βœ… N/A βœ… βœ…
Upgrade path Documents need to be reindexed N/A Documents need to be reindexed Documents need to be reindexed

# Limits

Meilisearch Algolia Typesense Elasticsearch
Maximum number of indexes No limitation 1000, increasing limit possible by contacting support 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

# Community

Meilisearch Algolia Typesense Elasticsearch
GitHub stars of the main project 27K N/A 10K 60K
Number of contributors on the main project 75 N/A 20 1,700
Public Slack community size 1.5K N/A 700 14K

# Support

Meilisearch Algolia Typesense Elasticsearch
Status page βœ… βœ… βœ… βœ…
Free support channels Instant messaging / chatbox (2-3h delay),
public Slack community,
GitHub issues & discussions,
Slack Connect
Instant messaging / chatbox,
public community forum
Instant messaging/chatbox (24h-48h delay),
public Slack community,
GitHub issues.
Public Slack community,
public community forum,
GitHub issues
Paid support channels Support is free! Emails Emails,
private Slack
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 prefix searching capability, makes search intuitive for users, and returns 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.

Meilisearch provides similar features and reaches the same level of relevance just as quickly as its competitor.

If you are a current Algolia user considering a switch to Meilisearch, you may be interested in our migration guide.

# Key similarities

Some of the most significant similarities between Algolia and Meilisearch are:

  • Features such as search-as-you-type, typo tolerance, faceting, etc.
  • Fast results targeting an instant search experience (answers < 50 milliseconds)
  • Schemaless indexing
  • Support for all JSON data types
  • Asynchronous API
  • Similar query response

# Key differences

Contrary to Algolia, Meilisearch is open-source and can be forked or self-hosted.

Additionally, Meilisearch is written in Rust, a modern systems-level programming language. Rust provides speed, portability, and flexibility, which makes the deployment of our search engine inside virtual machines, containers, or even Lambda@Edge (opens new window) a seamless operation.

# Pricing

The pricing model for Algolia (opens new window) is based on the number of records kept and the number of API operations performed. It can be prohibitively expensive for small and medium-sized businesses.

Meilisearch is open-source and can be self-hosted, but also offers a cloud-hosted product analogous to Algolia's service: Meilisearch Cloud (opens new window). Unlike Algolia, pricing of Meilisearch Cloud (opens new window) follows a set hourly rate based on the computing resources chosen, with no per-record or per-search fees. You can send your server as much traffic or data as it can manage.

# A quick look at the search engine landscape

# Open source

# Lucene

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

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. Instead of storing documents, it comprises an inverted index with a Levenshtein automaton. This means any application querying Sonic has to retrieve the search results from an external database using the returned IDs 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.

# Typesense

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

# Lucene-Solr

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

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

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

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

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.

# Conclusions

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.