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 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.

    Comparison table

    General overview

    Source code licensingMIT
    (Fully open-source)
    (Fully open-source)
    (Not open-source)
    Built withRust
    Check out why we believe in Rust.
    Data storageDisk with Memory Mapping -- Not limited by RAMLimited by RAMLimited by RAMDisk 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 😇

    JavaScript client
    PHP client
    Python client
    Ruby client
    Java client
    Swift client
    .NET client
    Rust client🔶
    Go client
    Dart client
    Official Laravel Scout Support
    Available as a standalone module

    Available as a standalone module


    Document schema
    id field is required and must be a string
    Nested field support
    Nested document querying
    Automatic document ID detection
    Compression SupportGzip, Deflate, and BrotliGzip
    Reads payload as JSON which can lead to document corruption
    Typo tolerant🔶
    Needs to be specified by fuzzy queries
    Orderable ranking rules🔶
    Field weight can be changed, but ranking rules order cannot be changed.
    Custom ranking rules🔶
    Function score query
    Query field weights
    Stop words
    Automatic language detection
    All language supports
    Ranking Score Details
    API Key Management
    Tenant tokens & multi-tenant indexes
    Multitenancy support

    Placeholder search
    Multi-index search (Federated search)
    Exact phrase search
    Geo search
    Sort by🔶
    Limited to one sort_by rule per index. Indexes may have to be duplicated for each sort field and sort order

    Up to 3 sort fields per search query
    Support complex filter queries with an SQL-like syntax.
    Does not support OR operation across multiple fields
    Faceted search
    Faceted fields must be searchable
    Faceting can take several seconds when >10 million facet values must be returned
    Distinct attributes
    De-duplicate documents by a field value
    Bucket documents by field values
    Semantic Search🔶
    Under Premium plan
    Hybrid Search🔶
    Under Premium plan
    Embedding Generation
    REST embedders
    GCP Vertex AI
    Prompt TemplatesUndisclosed
    Vector StoreUndisclosed
    Langchain Integration
    GPU support
    Mini Dashboard🔶
    Cloud product
    Cloud product
    Search Analytics
    Cloud product

    Cloud Product

    Cloud Product
    Monitoring Dashboard
    Cloud product

    Cloud Product

    Cloud Product

    Cloud Product


    Platform Support ARM
    n/a🔶 ARM (requires Docker on macOS)
    Official 1-click deploy
    Only for the cloud-hosted solution
    Official cloud-hosted solutionMeilisearch Cloud
    High availabilityAvailable with Meilisearch Cloud
    Run-time dependenciesNoneN/ANoneNone
    Backward compatibilityN/A
    Upgrade pathDocuments are automatically reindexed on upgradeN/ADocuments are automatically reindexed on upgradeDocuments are automatically reindexed on upgrade, up to 1 major version


    Maximum number of indexesNo limitation1000, increasing limit possible by contacting supportNo limitationNo limitation
    Maximum index size80TiB128GBConstrained by RAMNo limitation
    Maximum words per attributeNo limitationNo limitationNo limitationNo limitation
    Maximum document sizeNo limitation100KB, configurableNo limitation100KB default, configurable


    GitHub stars of the main project42KN/A17K66K
    Number of contributors on the main project179N/A381,900
    Public Discord/Slack community size2,100N/A2,00016K


    Status page
    Free support channelsInstant messaging / chatbox (2-3h delay),
    public Discord community,
    GitHub issues & discussions
    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 channelsSupport is free!EmailsEmails,
    private Slack
    Web support,

    Approach comparison

    Meilisearch vs Elasticsearch

    Elasticsearch is designed as a backend search engine. Although it is not suited for this purpose, it is commonly used to build search bars for end-users.

    Elasticsearch can handle searching through massive amounts of data and performing 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.

    Unlike Elasticsearch, which is a general search engine designed for large amounts of log data (for example, back-facing search), Meilisearch is intended to deliver performant instant-search experiences aimed at end-users (for example, front-facing search).

    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. It provides prefix searching capability, makes search intuitive for users, and returns results instantly with excellent relevance out of the box.

    For a more detailed analysis of how it compares with Meilisearch, refer to our blog post on Elasticsearch.

    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:

    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 a seamless operation.


    The pricing model for Algolia 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 an open-source search engine available via Meilisearch Cloud or self-hosted. Unlike Algolia, Meilisearch pricing is based on the number of documents stored and the number of search operations performed. However, Meilisearch offers a more generous free tier that allows more documents to be stored as well as fairer pricing for search usage. Meilisearch also offers a Pro tier for larger use cases to allow for more predictable pricing.

    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:

    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. 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.


    Like Meilisearch, Typesense is a lightweight open-source search engine optimized for speed. To better understand how it compares with Meilisearch, refer to our blog post on Typesense.

    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 (for example, 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.

    Source available


    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 (for example, 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 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, specifically, 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 source available 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.