How personalization and recommendations enhance search and discovery

Fuzzy search: a comprehensive guide to implementation


Have you ever noticed how Google suggests exactly what you're looking for before you finish typing? That's predictive search in action. It revolutionizes user interaction with software applications. In this guide, we'll explore implementing predictive search in your applications.

## Introduction to predictive search

### Definition and importance of predictive search

**Predictive search is an intelligent system that anticipates user intent and suggests relevant results in real-time as users type**. Unlike traditional search methods that wait for a complete query, predictive search processes each keystroke to provide immediate, relevant suggestions.

The importance of predictive search in software engineering is significant. It bridges user intent and content discovery, reducing the time and effort users spend searching. Modern applications leverage predictive search to enhance productivity and streamline workflows.

Software engineering teams implementing predictive search can expect improved user engagement metrics. For instance, e-commerce platforms often see a 25-30% increase in conversion rates with predictive search features. Developer tools report significant reductions in time spent searching for code snippets or documentation.

### Predictive search enhancing autocomplete

Traditional autocomplete completes words based on character matching. Predictive search, however, analyzes patterns in user behavior, search history, and contextual data to offer intelligent suggestions.

The personalization aspect of predictive search sets it apart from basic autocomplete. **Predictive search creates a more tailored experience by considering factors like previous searches, user location, and trending queries**.

For example, when a developer searches for "deploy" in a cloud platform's console, the system prioritizes suggestions based on their commonly used services or recent deployments.

<Callout intent="note">
	
	For example, in an e-commerce platform, **autocomplete might suggest "laptop bag"** when a user types "lap", predictive search might also suggest "computer accessories," "mobile workstation," or specific laptop models based on browsing history and market trends.
	
	
</Callout>

## Core concepts and components

### Predictive models in software engineering

Predictive models form the backbone of modern search capabilities. **They utilize complex algorithms to analyze user input patterns and generate relevant suggestions**.

Businesses can leverage large datasets, with the specific type of data varying depending on the industry and business objectives. It is possible to use various data sources like browsing history, user interactions, and market trends to improve predictive search strategies.

![Examples Of Data Predictive Search](https://unable-actionable-car.media.strapiapp.com/predictive_search_b4778b9493.avif)

These models process queries through multiple layers of analysis, including string matching, semantic understanding, and historical pattern recognition.

Predictive models often rely on prefix trees (or radix trees) for efficient string matching and retrieval. This structure enables rapid processing of partial queries, allowing the system to generate suggestions with minimal latency. The models continuously learn from user interactions, refining their prediction accuracy over time.

### Search engines and search queries

Search engines in predictive systems employ sophisticated indexing mechanisms to aid rapid query processing. Key components include query processors, suggestion generators, and ranking algorithms.

These elements work together to produce and prioritize results based on various relevance factors.

![Layers Of Logic Search Engine](https://unable-actionable-car.media.strapiapp.com/predictive_search_2c856b34f6.avif)

Modern search queries go through steps including tokenization, spell checking, and context analysis. **This ensures that even imperfect queries can yield useful suggestions**. Balancing immediate results and accuracy often involves using caching mechanisms to optimize frequently requested queries.

### Machine learning and AI power

Artificial intelligence significantly enhances predictive search through advanced natural language processing and machine learning algorithms.

These technologies help systems understand user intent beyond literal string matching, incorporating semantic understanding and contextual relevance.

AI improves search accuracy by:

- Capturing user intent with natural language understanding
- Adapting ranking algorithms to changing user behavior patterns
- Identifying relationships between different terms through semantic analysis
- Tailoring suggestions based on user profiles and historical interactions

Combining machine learning with traditional search algorithms creates a hybrid approach that delivers both speed and accuracy.

## Capabilities required for predictive search

A robust predictive search implementation requires several key capabilities to deliver a seamless user experience. Fast indexing and efficient query processing are foundational, but there's more to consider.

### Meilisearch's approach to enabling predictive search

Meilisearch combines traditional full-text search with AI-powered capabilities. Its architecture enables fast, relevant results and maintains simplicity in implementation.

**The integration process focuses on three key areas**: data indexing, search configuration, and result optimization. Here's an example of configuring search settings:

```typescript
// config/search.ts
const searchConfig = {
  settings: {
    rankingRules: [
      'words',
      'typo',
      'proximity',
      'attribute',
      'sort',
      'exactness'
    ],
    searchableAttributes: [
      'title',
      'description',
      'category'
    ],
    displayedAttributes: [
      'title',
      'description',
      'category',
      'thumbnail'
    ],
    // Enable AI-powered search with OpenAI
    embedder: {
      type: 'openai',
      documentTemplate: "Product: {{title}}nDescription: {{description}}"
    }
  }
};

await searchClient.index('products').updateSettings(searchConfig);
```

**This configuration enables both traditional search capabilities and AI-powered features**, creating a hybrid search experience that delivers both speed and relevance. The system handles typos, synonyms, and contextual understanding while maintaining sub-50ms response times.

<Callout intent="tip">
	
	Ready to supercharge your search experience? [Explore Meilisearch Cloud](https://meilisearch.org.cn/cloud) and deliver lightning-fast search results that will keep your users engaged and boost your conversion rates.
	
	
</Callout>

## Applications and use cases

Modern software applications leverage predictive search in numerous ways, from e-commerce platforms to content management systems. Understanding these practical applications helps developers implement the right search strategies for their specific use cases.

### App search

**App search is a common implementation of predictive search in software applications**. Mobile apps benefit particularly due to limited screen space and typing constraints.

For instance, food delivery apps use predictive search to help users find restaurants or dishes quickly, considering factors like previous orders and current location.

### Site and media search

**Site and media search capabilities have evolved to handle complex content structures**. Content management systems and documentation platforms use predictive search to help users navigate large information hierarchies.

For example, technical documentation sites can suggest relevant API endpoints or code examples as developers type their queries.

<Callout intent="note">
	
	Check out how Meilisearch powers the discovery of 300,000+ AI models on [Hugging Face](https://meilisearch.org.cn/blog/hugging-face-democratizes-ai), demonstrating the power of predictive search in organizing and accessing vast AI resources.
	
	
</Callout>

### Ecommerce search

E-commerce platforms showcase the potential of predictive search through sophisticated product discovery. Online stores suggest not only product names but also categories, brands, and specific features. **This capability extends to handling complex queries like "red summer dress under $50" accurately**.

<Callout intent="note">
	
	For another compelling example of predictive search in action, explore [Meilisearch's case study with Bookshop.org](https://meilisearch.org.cn/blog/bookshop-increases-search-based-purchases), where they achieved a remarkable 43% increase in search-based purchases by enhancing their book discovery platform.
	
	
</Callout>

### Enterprise search

[Enterprise search](meilisearch.com/blog/enterprise-search) presents unique challenges because it handles diverse data sources while maintaining security constraints. Predictive search in enterprise environments must consider user permissions, department-specific terminology, and internal document hierarchies.

For instance, a company's internal tools might suggest different results for the same query based on the user's role and access level.

## Techniques and methodologies

### Genetic programming and test models

Genetic programming enhances predictive search capabilities by **evolving and refining search algorithms**. These techniques mimic natural selection to optimize search patterns and improve relevancy.

The system dynamically generates search algorithm variations. It then evaluates these algorithms by analyzing user interactions. Based on performance metrics, the system selects the most effective algorithm combinations to enhance future search experiences.

Test models developed through genetic programming adapt to changing search patterns. For instance, e-commerce search systems can automatically adjust prediction weights based on seasonal trends, user behavior shifts, and new product introductions. **The evolutionary nature of these algorithms ensures relevant search suggestions** as user preferences and patterns evolve.

### Base software and search base

Establishing a robust search base requires careful consideration of data structure and indexing strategies.

<Callout intent="note">
	
	For deeper insights into optimizing search indexing, check our comprehensive guide on [best practices for faster indexing](https://meilisearch.org.cn/blog/best-practices-for-faster-indexing).
	
	
</Callout>

Typically, it consists of an inverted index system enabling quick lookup of terms while maintaining relationships between search elements. **This base structure must support both exact matches and fuzzy searching capabilities**.

Integration of predictive search within software frameworks demands:

- Efficient data storage and retrieval mechanisms to handle large volumes of search queries
- Flexible indexing strategies accommodating both structured and unstructured data
- Scalable architecture maintaining performance under increasing load
- Real-time update capabilities to reflect changes in underlying data

The search base must also handle edge cases and special scenarios. For example, with multilingual search capabilities, the base system must account for different character sets, writing systems, and language-specific patterns. **This ensures reliable and effective predictive search functionality across diverse use cases** and user requirements.

## Capabilities

Low latency is crucial for predictive search systems because users expect instant feedback while typing. Modern search implementations must maintain response times under 100ms to feel instantaneous. This speed requirement becomes even more critical when handling complex queries across large datasets.

Several optimization techniques help achieve these performance goals. **Intelligent caching strategies store frequently accessed results and partial queries**, significantly reducing database load and response times for common searches. Here's how response times typically break down:

| Operation | Target Latency | Optimization Method |
| --- | --- | --- |
| Query Processing | &lt; 20ms | In-memory processing |
| Index Lookup | &lt; 30ms | Cached indices |
| Result Ranking | &lt; 30ms | Pre-computed scores |
| Network Transfer | &lt; 20ms | Edge caching |

Ensuring reliability involves implementing robust error handling and failover mechanisms. **Search systems must gracefully handle scenarios like network issues, invalid queries, or backend service disruptions**. Implementing circuit breakers and fallback options helps maintain service availability.

Performance monitoring is vital for maintaining optimal search functionality. Modern search implementations use real-time metrics tracking to identify bottlenecks and optimization opportunities.

Key performance benchmarks for predictive search systems include:

- **Query response time:** 95th percentile under 200ms
- **Suggestion relevancy:** Minimum 80% accuracy rate for top 5 suggestions
- **System throughput:** Ability to handle 1000+ queries per second per node
- **Cache hit ratio:** At least 85% cache effectiveness

<Callout intent="tip">
	
	Meilisearch Cloud includes [built-in monitoring tools](https://meilisearch.org.cn/docs/learn/analytics/monitoring) that enable real-time tracking of search performance metrics.
	
	
</Callout>

The architecture should scale horizontally to handle increasing load while maintaining performance. This typically involves:

1. Distributed index sharding for better resource utilization
2. Load balancing across multiple search nodes
3. Asynchronous index updates to prevent blocking operations
4. Smart query routing based on user location and server load

Reliability also extends to data consistency and accuracy. Search systems must handle index updates and real-time data changes without compromising search quality or availability. **This often requires sophisticated versioning and synchronization mechanisms** to ensure users always see the most relevant and up-to-date results.

## Future directions and innovations

As predictive search technology continues to evolve, several emerging trends and innovations are reshaping the future of search systems.

### Integration with advanced technologies

The future of predictive search is being shaped by **advancements in artificial intelligence and machine learning technologies**. Large language models (LLMs) are transforming how search systems understand and process user queries. These models can interpret natural language with high accuracy, enabling more conversational and context-aware search experiences.

Integration with advanced technologies is already showing promising results. Some systems are experimenting with multimodal search capabilities, processing text, images, and voice inputs simultaneously. This enables more intuitive search interfaces that match how humans naturally communicate.

### Trends in predictive search

LLMs are revolutionizing predictive search capabilities by fundamentally transforming how we interact with search engines. These sophisticated systems can now comprehend complex queries and generate natural, contextually appropriate suggestions that feel remarkably human-like.

Modern search systems now interpret both technical and informal language, moving beyond pattern matching to understand the true meaning behind user queries.

This advancement has enabled several crucial improvements in search functionality:

- More precise interpretation of user queries
- Sophisticated handling of ambiguous searches
- Enhanced recognition of specialized terminology
- Intelligent, context-aware result ranking

**These innovations are already being implemented in various forms**. Some search engines use hybrid approaches that combine traditional search algorithms with [AI-powered semantic understanding](meilisearch.com/blog/ai-powered-search). This allows them to maintain the speed and reliability of conventional search while leveraging advanced AI capabilities.

## Beyond horizons: the transformative potential of predictive search

Predictive search has evolved from a convenient feature to an essential component of modern software applications.

Successful implementation requires careful consideration of architecture, performance optimization, and user experience design.

The integration of advanced AI technologies and mobile-first approaches will continue to push the boundaries of what's possible in predictive search. This makes it an exciting area for continued innovation and development.

<Callout intent="tip">
	
	Unlock advanced search potential with customizable relevancy, typo tolerance, and more. [Enhance Your Search Strategy](https://meilisearch.org.cn/features) with Meilisearch Cloud's powerful search capabilities.
	
	
</Callout>

Software engineering predictive search: a complete guide

In today's interconnected world, many applications need to handle content in multiple languages. Meilisearch offers powerful capabilities for multilingual search, combining intelligent language handling with specialized models for optimal relevance. This guide will walk you through setting up multilingual search with Meilisearch, covering both full-text and semantic search approaches.

Why multilingual search matters
===============================

Before we dive into the technical details, let's consider why multilingual search is crucial:

1.  Global reach: Expand your audience by catering to users in their native languages.
2.  Improved user experience: Provide relevant results regardless of the language used in queries or content.
3.  Increased engagement: Users are more likely to interact with content they can easily find and understand.

Now, let's explore how Meilisearch makes multilingual search a breeze.

Full-text search: intelligent language handling
-----------------------------------------------

At its core, Meilisearch excels at handling diverse languages out of the box. Our [advanced tokenizer](https://meilisearch.org.cn/docs/learn/indexing/tokenization#vector-search&utm_source=blog&utm_medium=multilingual_search) is designed to work with:

*   Languages that use whitespace to separate words (like English, Spanish, and French)
*   Languages with different writing systems (such as Chinese, Japanese, and Thai)

### Automatic language detection

When you add documents to your Meilisearch index, the engine automatically detects the language of each field. This means you don't need to specify languages manually in most cases - Meilisearch will handle it for you.

For example, if your documents contain a mix of English and Spanish content:

```json
[
  {
    "id": 1,
    "title": "The quick brown fox",
    "description": "El zorro marrón rápido salta sobre el perro perezoso"
  },
  {
    "id": 2,
    "title": "La luna y las estrellas",
    "description": "A beautiful night sky filled with twinkling stars"
  }
]
```

Meilisearch will automatically detect and properly handle the different languages in each field, ensuring accurate search results across your multilingual content.

### Specifying languages for specific fields

While automatic detection works well in most cases, you might want more control over language handling for certain fields. Meilisearch allows you to [specify languages](https://meilisearch.org.cn/docs/reference/api/settings#localized-attributes#vector-search&utm_source=blog&utm_medium=multilingual_search) for particular attributes using the `localizedAttributes` setting.

Here's how you can set it up:

```json
{
  "localizedAttributes": [
    {
      "locales": ["eng"],
      "attributePatterns": ["title_en", "*_english"]
    },
    {
      "locales": ["spa"],
      "attributePatterns": ["description_es", "*_spanish"]
    }
  ]
}
```

This configuration tells Meilisearch to treat fields ending with `_en` or `_english` as English, and fields ending with `_es` or `_spanish` as Spanish. This approach is particularly useful when you have a consistent naming convention for your multilingual fields or when you want to ensure specific treatment for certain attributes.

Semantic search: Enhancing multilingual relevance
=================================================

While full-text search works well for many multilingual scenarios, semantic search can provide even better results, especially for complex queries or when dealing with nuanced meanings across languages.

Meilisearch supports various embedding models for semantic search, offering powerful auto-embedding capabilities. This means you can easily set up semantic search without having to manage the embedding process yourself. Meilisearch handles the generation and storage of embeddings automatically, seamlessly integrating them into your search experience.

For multilingual applications, we recommend using specialized multilingual models rather than relying on general-purpose models. These models are designed to understand and represent meaning across multiple languages, leading to more relevant search results in multilingual contexts.

One excellent option for multilingual semantic search is the [Cohere Multilingual embedding model](https://docs.cohere.com/docs/multilingual-language-models). It offers a great balance of speed and relevance across a wide range of languages. Cohere's model is particularly adept at understanding context and nuance in different languages, making it an ideal choice for diverse, multilingual content. Follow [the guide](https://meilisearch.org.cn/docs/guides/ai/cohere#vector-search&utm_source=blog&utm_medium=multilingual_search) if you want to do it now.

To explore different models and their performance, you can use the [Meilisearch playground](https://playground.meilisearch.com). This interactive tool allows you to test various embedding models and see how they perform with your specific multilingual queries and content.

Conclusion
----------

We encourage you to experiment with different settings and models to find the optimal configuration for your specific use case. And remember, the Meilisearch community is always here to help - join us on [Discord](https://discord.meilisearch.com/) or check out our documentation for more detailed information and support.

Ready to get started with multilingual search? Try Meilisearch today [with a 14 day trial](https://cloud.meilisearch.com/#vector-search&utm_source=blog&utm_medium=multilingual_search)!

Searching across multiple languages

Deciding between Algolia and Meilisearch for your search solution? We're here to help! In this article, we'll compare their features, capabilities, pricing models, and overall strengths and weaknesses.

What is Algolia?
----------------

Founded in 2012, Algolia is a hosted search engine that provides full-text and AI-powered search capabilities. It offers an API that integrates fast, customizable search experiences into websites and mobile applications.

Algolia is known for its remarkable speed and scalability, making it a go-to choice for enterprises looking for sophisticated search functionalities. However, these premium features come with a higher cost, Algolia tends to be more suitable for larger businesses with bigger budgets.

What is Meilisearch?
--------------------

Launched in 2018, [Meilisearch](https://meilisearch.org.cn/#comparisons&utm_source=blog&utm_medium=vs-algolia) is an open-source search engine that features full-text and AI-powered search capabilities Designed to be developer-friendly and plug-and-play, it addresses most common use cases straight out of the box. Built with Rust, it ensures exceptional performance, security, and flexibility.

Known for its lightning-fast search speeds and modern development environment, Meilisearch is an excellent choice for small-to-medium growing businesses seeking high-performance search functionalities.

Unlike Algolia, Meilisearch is [open-source](https://github.com/meilisearch/meilisearch). You can self-host it or use the [managed Meilisearch Cloud service](https://meilisearch.org.cn/cloud#comparisons&utm_source=blog&utm_medium=vs-algolia), allowing for flexibility and cost-efficiency.

Algolia vs Meilisearch - Key features compared
----------------------------------------------

### Developer experience

Algolia and Meilisearch both prioritize ease of use, offering straightforward setup processes and comprehensive documentation. These search engines work out-of-the-box, providing great relevancy in most cases without the need for additional configurations. They feature intuitive REST APIs and place a strong emphasis on the developer experience. Meilisearch's open-source nature is a significant asset, as it enables better feedback collection and faster implementations.

When comparing Meilisearch Cloud and Algolia dashboards, Algolia’s interface stands out for its advanced and comprehensive UI. This is understandable, given that Algolia is a fully-hosted search engine that has been in the market for over a decade. In contrast, Meilisearch, which launched its cloud offering much more recently, is still developing its dashboard.

Overall, the Algolia dashboard feels more beginner-friendly and integrated, guiding users smoothly from data addition to frontend integration. Meilisearch, on the other hand, partially relies on its [documentation](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#comparisons&utm_source=blog&utm_medium=vs-algolia) to guide users through the process. However, it’s important to note that Meilisearch’s dashboard is continually evolving based on user feedback, showing significant potential for improvement.

### SDKs and integrations

Both Algolia and Meilisearch provide [API clients](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/sdks#comparisons&utm_source=blog&utm_medium=vs-algolia) for a variety of popular programming languages, including JavaScript, .NET, Python, PHP, and more, ensuring developers can easily integrate search functionalities into their applications.

Algolia offers a significantly broader selection of SDKs and integrations, particularly in the realm of specialized e-commerce platforms such as Shopify and Adobe Commerce (formerly Magento). This extensive range of integrations makes Algolia a versatile choice for businesses with specific platform requirements.

### Relevance and typo tolerance

Meilisearch and Algolia both deliver excellent relevancy and typo tolerance right out of the box, with options for customization to fine-tune search results. They offer similar features, including synonyms, deduplication, sorting, filtering, faceting, and geosearch.

However, configuring some of these features in Meilisearch can feel more straightforward. Certain functionalities, such as [sorting](https://meilisearch.org.cn/docs/learn/fine_tuning_results/sorting#sorting#comparisons&utm_source=blog&utm_medium=vs-algolia), require fewer configuration steps compared to Algolia. This simplicity makes Meilisearch an attractive option for developers looking for ease of setup and use.

Moreover, unlike Algolia, Meilisearch does not require replicating or copying indexes for each sort criterion, making cost management much more manageable and fair for customers.

Algolia, on the other hand, provides a more extensive feature set with advanced configuration options. Features like filter weighting offer more customization, but this can also add complexity. While Algolia's features can sometimes offer more advanced configuration, Meilisearch is rapidly closing the gap, [regularly launching new features](https://github.com/meilisearch/meilisearch/releases#comparisons&utm_source=blog&utm_medium=vs-algolia) that enhance its capabilities.

### AI-powered search

Both Algolia and Meilisearch offer advanced semantic search capabilities, but with notable differences in accessibility and customization.

Algolia provides NeuralSearch, its approach to hybrid search, which combines neural search and full-text search results. Algolia leverages AI at multiple levels, including search, ranking, and query suggestions. This feature allows you to choose between keyword search and neural search modes. When using Algolia, text embedding is managed by their proprietary model, which is automatically applied to the documents. However, NeuralSearch is not widely available; it is reserved for the highest plan (Elevate) and requires event data from the front end. In practice, many users have yet to see a running instance of NeuralSearch. 

Meilisearch, on the other hand, offers a more customizable [AI-powered search](https://meilisearch.org.cn/solutions/vector-search#comparisons&utm_source=blog&utm_medium=vs-algolia) experience. It uses state-of-the-art large language model (LLM) providers such as OpenAI and HuggingFace to generate vector embeddings. Additionally, Meilisearch allows you to configure multiple embedders for an index and supports custom embeddings, ensuring flexibility and adaptability. Its [hybrid search](https://meilisearch.org.cn/solutions/hybrid-search#comparisons&utm_source=blog&utm_medium=vs-algolia) approach is highly customizable, enabling you to adjust the semantic ratio for each hybrid search query, ranging [from full semantic to full-text search](/blog/full-text-search-vs-vector-search/#comparisons&utm_source=blog&utm_medium=vs-algolia Meilisearch's AI-powered search is available both when self-hosting and on Meilisearch Cloud, making it accessible to a wide range of users.

### Analytics & data visualization

Both Meilisearch and Algolia provide robust analytics and data visualization tools to help you monitor and optimize search performance, but they offer these insights in slightly different ways.

Meilisearch offers a [comprehensive set of analytics](https://meilisearch.org.cn/docs/learn/cloud/analytics#comparisons&utm_source=blog&utm_medium=vs-algolia), including metrics such as total searches, user activity, and key search effectiveness indicators like no result rate and click-through rate. You can visualize search trends over time and gain insights into user behavior, including the most searched queries and geographic distribution of searches.

Algolia, on the other hand, provides a more extensive suite of analytics and data visualization tools. Its overview dashboard includes similar key metrics like total searches, user activity, and conversion rates. However, Algolia goes further by offering advanced analytics providing deeper insights into popular filter values and top-performing results.

A notable feature that sets Algolia apart is its A/B testing capability. This allows you to compare an index and its replica with different settings, evaluating metrics like click-through rates and conversion rates. 

In summary, while both Meilisearch and Algolia offer essential analytics tools, Algolia includes more detailed and advanced options, including A/B testing, which can be particularly beneficial for optimizing user interactions in ecommerce use cases. Meilisearch focuses on core metrics and delivers a straightforward visualization of search trends and user behavior.

### Community and support

Algolia offers a dedicated Discord community with 507 members, accessible to all users, providing a platform for discussions, questions, and support. For more personalized assistance, email and website support are available starting from the Grow plan. Additionally, Algolia provides Standard and Extended Support options, though pricing is not disclosed and is only available through sales inquiries.

Meilisearch, being an open-source search engine, has fostered a robust community. Its [Discord community](https://discord.meilisearch.com/#comparisons&utm_source=blog&utm_medium=vs-algolia) boasts 2,441 members, offering a vibrant space for open-source enthusiasts and users to engage, share knowledge, and seek help. For Cloud users, Meilisearch ensures comprehensive support tailored to each plan: the Build plan includes Discord support, the Pro plan offers chat and email support, and the Custom plan provides priority support with dedicated channels. This tiered support structure reflects Meilisearch's commitment to accessible and responsive customer service.

### Pricing and commercial offerings

Algolia offers a mix of free, pay-as-you-go, and custom pricing plans. The Free Plan includes up to 10,000 search requests per month and 1 million records, while the Grow Plan features a clear pay-as-you-go structure with specific costs for additional search requests and records. However, their Premium and Elevate Plans do not have disclosed pricing and require users to request details from sales.  

In contrast, Meilisearch Cloud has fixed monthly pricing for its Build and Pro plans, priced at $30 and $300 per month, respectively, with specified features and resources included. For larger projects, Meilisearch has a Custom Plan that requires a custom quote.

While Meilisearch does not offer a free tier, it starts at a relatively low monthly cost with substantial resources and support. Additionally, it includes a 14-day free trial without requiring a credit card, making it easy for potential users to explore its features. Moreover, Meilisearch being open-source, it can also be [self-hosted for free](https://meilisearch.org.cn/docs/learn/getting_started/installation#local-installation#comparisons&utm_source=blog&utm_medium=vs-algolia).

Although Algolia might save some costs in specific scenarios, its complexity and potential hidden costs can lead to higher expenses in practice. Meilisearch, with its clear and predictable [pricing model](https://meilisearch.org.cn/pricing#comparisons&utm_source=blog&utm_medium=vs-algolia), is often more attractive for businesses that prefer straightforward budgeting and cost transparency.

### Global coverage

Meilisearch Cloud has server regions in major cities such as San Francisco, New York, London, Frankfurt, and Singapore, strategically positioning itself in key commercial hubs across North America, Europe, and Asia. This coverage allows Meilisearch to provide reliable performance and data residency options in these important regions.

Algolia has a much broader network with clusters deployed in 16 regions globally. This extensive coverage spans the US, Europe, Asia, and additional regions like Brazil, Japan, and Australia. Algolia’s expansive network allows for greater flexibility in data localization, enhancing performance and compliance with local regulations.

Looking ahead, Meilisearch plans to expand its reach by adding more server regions to its network. This expansion will broaden its global coverage, further enhancing its service offerings.

Algolia challenges
------------------

Algolia faces several notable challenges, one of which is its high cost. While Algolia does offer a free tier, it is limited, and scaling up can quickly become expensive. Algolia's powerful features are well-suited for large enterprises, but smaller businesses might find these advanced features and the associated costs overwhelming. The lack of transparency for its Premium and Elevate plans further complicates budgeting, as potential customers must engage with the sales team to get detailed pricing information, which can deter smaller businesses that need to understand costs upfront. 

Additionally, fully leveraging Algolia’s extensive features and capabilities often requires significant customization and technical expertise. This can be a barrier for businesses without dedicated technical teams. 

Furthermore, Algolia's reliance on its proprietary models for AI features means users are dependent on Algolia's technology stack. This dependency can limit businesses looking for more flexibility or integration with other AI solutions.

Meilisearch challenges
----------------------

Meilisearch, despite its strengths, also faces its own set of challenges. One notable challenge is the absence of a completely free tier in the managed cloud version, which might deter very small businesses or startups looking to minimize costs. While Meilisearch's pricing is transparent and reasonable, the lack of a free option in the managed cloud version can be a disadvantage compared to competitors like Algolia. However, being open-source, Meilisearch provides a free, self-hosted option that ensures cost savings for those willing to manage their own infrastructure.

While Meilisearch offers essential and robust features, it might lack some of the advanced features and integrations that Algolia provides, particularly for specialized use cases such as advanced e-commerce applications.

When should you consider Algolia over Meilisearch?
--------------------------------------------------

If your business requires sophisticated e-commerce search functionalities, such as merchandising tools, product recommendations, and advanced filtering options, Algolia is currently better equipped to handle these needs. The platform's features are designed to improve product discovery and conversion rates.

For businesses that want to optimize their search configurations through A/B testing, Algolia provides robust tools to compare different search setups. This is especially beneficial in e-commerce, where fine-tuning search performance can significantly impact sales and customer engagement. 

Algolia’s infrastructure is also optimized for global reach, offering high availability and low latency across different regions. If your business operates internationally and requires consistent performance worldwide, Algolia is a strong contender.

In summary, Algolia excels in delivering advanced e-commerce functionalities, comprehensive analytics, and a globally optimized infrastructure. While it may come with higher costs and a steeper learning curve, these features make it particularly suitable for large enterprises and businesses with complex, high-demand search requirements.

When should you consider Meilisearch over Algolia?
--------------------------------------------------

Meilisearch is an excellent choice for businesses that prioritize simplicity, transparency, and flexibility. Its straightforward setup process and developer-friendly design make it ideal for teams seeking a hassle-free implementation. With robust core features that work out of the box, Meilisearch ensures high performance, fast response times, and excellent relevancy without requiring extensive customization or technical expertise.

For organizations that value open-source solutions, Meilisearch is particularly appealing. It benefits from active community contributions and maintenance, fostering a collaborative environment that accelerates innovation. This allows businesses to adapt and extend the search engine according to their specific requirements, providing greater flexibility and control over their search infrastructure.

The open-source model also ensures the search engine remains aligned with user needs, adding a layer of transparency and flexibility. As a result, customers gain better confidence in their ability to shape the product and steer it in the direction they need, enhancing their overall control and adaptability.

Regular updates every two months ensure that Meilisearch users can benefit from the latest advancements and enhancements, keeping the search engine up-to-date with evolving needs and technologies. This continuous improvement cycle makes Meilisearch a strong contender for businesses looking for a dynamic and easy-to-use search solution.

In summary, choose Meilisearch over Algolia if you need a flexible, developer-friendly search engine with transparent pricing and the benefits of an open-source community. Meilisearch’s focus on simplicity, high performance, and continuous innovation makes it a compelling option for businesses of all sizes.

* * *

Learn more about what Meilisearch can bring to your business.

[Talk to a search expert](https://dub.sh/meili-sales#comparisons&utm_source=blog&utm_medium=vs-algolia)

* * *

Conclusion
----------

Both Algolia and Meilisearch offer robust search solutions with a developer-friendly experience and high performance.

Algolia is particularly strong in the e-commerce space, offering advanced analytics and features tailored to that use case. Its extensive feature set and flexible scalability options make it a great choice for larger enterprises looking for comprehensive search capabilities.

Meilisearch excels with its advanced AI capabilities, fast development cycle, and transparent pricing, making it an excellent choice for tech teams and app builders who want to be efficient and achieve fast ROI while benefiting from a secure, scalable search solution.

If you're considering making the switch, check out our [Migrating from Algolia to Meilisearch](https://meilisearch.org.cn/docs/learn/update_and_migration/algolia_migration#comparisons&utm_source=blog&utm_medium=vs-algolia) guide for a smooth transition.

You can see Meilisearch in action using this online [demo](https://where2watch.meilisearch.com/#comparisons&utm_source=blog&utm_medium=vs-algolia). Also, feel free to check out its [public roadmap](https://roadmap.meilisearch.com/#blog.meilisearch.com) and [GitHub community](https://github.com/orgs/meilisearch/discussions#blog.meilisearch.com) to see what new and upcoming features you can look forward to.

<Callout intent="tip">

Check out our detailed comparison of the best [Algolia alternatives](https://meilisearch.org.cn/blog/algolia-alternatives) in 2025.

</Callout>

Meilisearch vs Algolia

Navigating today’s ocean of information can be both a marvel and a maze. Full-text and vector search enable us to build search experiences empowering users to find relevant products, content, and more. As our quest for precision and context in search evolves, a question arises: can we balance the lexical flexibility of full-text search with the semantic depth of vector search?

Let’s explore together the pros and cons of each solution and discover the synergies that are redefining modern search & discovery experiences.

Full-text search
----------------

[Full-text search](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#vector-search&utm_source=blog&utm_medium=full-text-vs-vector#features) refers to matching some or all of a text query with documents stored in a database. Compared to traditional database queries, full-text search provides results even in case of partial matches. It allows building more flexible search interfaces for users, thus enabling them to find accurate results more quickly.

In practice, an efficient full-text search solution comes with tolerance to typos, synonyms, prefix search, and fuzzy matching. These features enable end users to find what they’re looking for even when the information at their disposal is limited. Allowing searches with incomplete information makes discovery faster.

Although modern SQL and NoSQL databases come with some full-text search capabilities, they often fall short compared to search-focused databases. For a deeper dive on this topic, consider reading on areas where [Postgres falls short for full-text search](/blog/postgres-full-text-search-limitations/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector

<Callout>
  Learn about the [inner workings of a search engine](/blog/how-full-text-search-engines-work/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector) in our dedicated article.
</Callout>

Vector search
-------------

[Vector search](https://meilisearch.org.cn/solutions/vector-search#vector-search&utm_source=blog&utm_medium=full-text-vs-vector) is the AI-powered approach to search. Instead of looking for documents matching a text query, it allows finding documents that have a similar _semantic_ meaning. This is achieved by building upon the semantic understanding of text that [Large Language Models](https://en.wikipedia.org/wiki/Large_language_model) (LLMs) provide.

LLMs can process database records and generate [vector embeddings](/blog/what-are-vector-embeddings/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector)—the numeric representation of the semantic meaning of documents. These embeddings are then stored in [vector databases](/blog/what-is-a-vector-database/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector As the name suggests, vector search works by comparing similar vectors.

By performing vector comparisons, vector search effectively enables [semantic search](/blog/what-is-a-vector-database/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector#what-is-similarity-search Despite relying on different underlying technologies, it also solves a similar purpose as full-text search: empowering users by improving the discovery aspect of search. It notably enables multi-modal search—searching with non-textual input, like in Google Images.

Comparative analysis: full-text search vs vector search
-------------------------------------------------------

Full-text search works by finding words in documents. That makes it good at understanding the **lexical intent** behind a user query. But it falls short when it has to work with vague queries that cannot be fuzzy-matched. The lack of semantic understanding of documents makes it unable to understand context and make generalizations.

To summarize, **full-text search**:

*   ✅ Corrects typos — e.g. `reutrn of the jedi`
*   ✅ Handles precise queries — e.g. `an exact product name`
*   ✅ Handles incomplete queries — e.g. `return of the j`
*   ❌ Cannot handle vague queries — e.g. `folks fighting with lightsabers`
*   ❌ Cannot understand context — e.g. `winter clothes`

On the other hand, vector search works by finding documents with similar meanings. That makes it good at understanding the **semantic intent** behind a user query. It’s great at understanding synonyms and descriptions of documents. It’s also adapted to the discovery of similar documents.

To summarize, **vector search**:

*   ✅ Handles vague queries — e.g. first released star wars movie
*   ✅ Understands context — e.g. winter clothes
*   ✅ Can suggest similar documents
*   ❌ Cannot handle precise queries
*   👎 Requires more resources

As we can see, neither solution is perfect. They each come with clear advantages for given use cases. They complement each other. Building state-of-the-art search experiences would require combining the benefits of both full-text and vector search.

![“Why don't we have both?“ meme](https://unable-actionable-car.media.strapiapp.com/why_dont_we_have_both_1e31f67a5c.gif)

Indeed, can we have both?

The case for hybrid search
--------------------------

Hybrid search combines the strengths of full-text search and vector search. It builds upon the accessible, search-as-you-type experience of full-text search and integrates the enhanced discovery capabilities that AI search enables.

Solutions like Meilisearch’s [hybrid search](https://meilisearch.org.cn/solutions/hybrid-search#vector-search&utm_source=blog&utm_medium=full-text-vs-vector) bring the best of both worlds without compromise. This allows developers to use a unified API for all types of searches. The code sample below demonstrates a usage of such API.

```js
// Hybrid search with Meilisearch

const results = await client.multiSearch({
	queries: [{ 
		indexUid: 'movies', 
		q: 'batman',
		hybrid: { embedder: 'default', semanticRatio: 0.5 }
	}]
})

```
<Callout>
  Not using JavaScript? Meilisearch supports [multiple SDKs](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/sdks#vector-search&utm_source=blog&utm_medium=full-text-vs-vector).
</Callout>

This code uses a Meilisearch client to search for the `batman` query in the movies index. It uses hybrid search, which is configured via the `hybrid` parameter. Let’s review the fields in the `hybrid` object and see what they enable.

The `embedder` field allows choosing which embedder will process the query (Meilisearch allows [configuring multiple embedders](https://meilisearch.org.cn/docs/learn/experimental/vector_search#vector-search&utm_source=blog&utm_medium=full-text-vs-vector#generate-vector-embeddings) for a given index.)

The `semanticRatio` field allows us to balance the importance of results coming from semantic search. A higher value will allow documents that are further distanced in the [semantic space](/blog/what-are-vector-embeddings/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector#what-is-the-semantic-space to make it to the final search results.

The `semanticRatio` value works as follows:

*   Semantic ratio = 0 — it’s a full-text search
*   Semantic ratio = 1 — it’s a vector search
*   0 < semantic ratio < 1 — it’s a hybrid search ✨

As semantic search will always return _some_ documents, it’s important to strike a good balance to keep the overall search results relevant. The demo below allows you to search for movies and play with the semantic ratio to see how it affects results.

[![Searching for movies using hybrid search](https://unable-actionable-car.media.strapiapp.com/hybrid_search_gif_cfa6283c31.gif)](https://where2watch.meilisearch.com/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector)

Searching for ”green ogre in fairy tale parody” ([Live demo](https://where2watch.meilisearch.com/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector))

* * *

Upd: Hybrid search is now generally available on Meilisearch Cloud.

[Try it now](https://meilisearch.org.cn/cloud)

Conclusion
----------

As engineers will tell you, the question of “What is the best tool for X?” is always: _it depends_. On their own, neither full-text search nor vector search can meet all the criteria to build a fast, relevant search experience. Hybrid search merges the accessibility of full-text search with improved discovery that AI enables.

**Hybrid search** is the modern approach to search, unifying behind a single API the most advanced search capabilities. This hybrid approach blends full-text search with semantic search, enhancing both the accuracy and comprehensiveness of search results. It is a modern, flexible solution adapted to today’s search use cases.

* * *

[Meilisearch](https://meilisearch.org.cn/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector) is an open-source search engine enabling developers to build state-of-the-art experiences while enjoying simple, intuitive DX. 

For more things Meilisearch, you can join the community on [Discord](https://discord.meilisearch.com/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector) or subscribe to the [newsletter](https://dub.sh/meili-newsletter#vector-search&utm_source=blog&utm_medium=full-text-vs-vector). You can learn more about the product by checking out its [roadmap](https://roadmap.meilisearch.com/#vector-search&utm_source=blog&utm_medium=full-text-vs-vector) and participating in [product discussions](https://github.com/orgs/meilisearch/discussions).

Full-text search vs vector search

Meilisearch vs Typesense

Full text search refers to matching some or all of a text query with documents stored in a database. Compared to traditional database queries, full text search provides results even in case of partial matches. It allows building more flexible search interfaces for users, thus enabling them to find accurate results more quickly.

From simple in-app search to browsing huge ecommerce catalogs, full text search use cases are numerous. It is so common that Postgres and other relational databases include dedicated APIs for full text search. Unfortunately, Postgres falls short of search-focused databases in multiple areas.

1\. Complex setup
-----------------

To provide relevant results, full text search should tolerate typos, allow synonyms, and allow partial matches. Additionally, results ranking needs to be highly customizable to adapt to a business’s specific needs. Configuring full text search on Postgres comes at the cost of [comprehensive configuration](https://rachbelaid.com/postgres-full-text-search-is-good-enough/) and often requires extensions that can’t be used when using managed Cloud services.

Creating database indexes, writing queries, and ranking algorithms quickly overstep domain knowledge and demand expertise in searching, indexing, and linguistics. Optimizing for performance gets even harder when working around constraints from mixed-and-matched extensions aimed at coping with Postgres full text search limitations.

Conversely, a search-focused database comes with [state-of-the-art features](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#features) like typo tolerance, prefix search, fuzzy matching, synonyms, and customizable rankings out of the box.

2\. Faceted search
------------------

[Faceted search](https://meilisearch.org.cn/docs/learn/fine_tuning_results/faceted_search) allows users to refine search results by broad categories. It’s often used in ecommerce applications. For example, a clothing shop can implement filtering by facets such as brand, size, or rating range.

[![Faceted search on an ecommerce website](https://unable-actionable-car.media.strapiapp.com/ecommerce_demo_4_final_1_48b9d742ac.gif)](https://ecommerce.meilisearch.com/)

Faceted search on an ecommerce website ([see demo](https://ecommerce.meilisearch.com/))

Implementing filtering is tricky enough for a single facet. But facets can take many shapes: category tags, price ranges, or minimum rating. Implementing filtering for all types is very challenging. By all means, the trickiest query to implement is aggregating results to build facet counts. This becomes very resource intensive on large datasets.

The complexity of implementing faceted search with Postgres grows exponentially with the number of facets. Faceted search alone makes a strong selling point for search engines like [Elasticsearch or Meilisearch](/blog/why-should-you-use-meilisearch-over-elasticsearch/)hey come with optimized, first-class APIs to handle facets filtering and counting.

3\. Typo tolerance
------------------

By default, Postgres full text search cannot handle misspellings. Users commonly install the `pg_trgm` extension to work around this limitation. (Again, this solution is not always available in managed Postgres.) This extension notably introduces new operators to compare similarity between strings along with search-optimized GIN and GIST indexes.

The new indexes allow more configuration for full text search, but choosing between GIN and GIST indexes is not always trivial. Moreover, the new operators do not take into account word proximity, space separators, or word size. This, in particular, makes it hard to achieve real fuzzy matching with Postgres.

Ideally, a search-focused database should allow configuring different rules for single versus multiple words queries. It is the case of Meilisearch, which allows disabling typos entirely for specific fields. This enables users to search via unique identifiers like a book’s _International Standard Book Number_ (_ISBN_).

[![Example of typo tolerance](https://unable-actionable-car.media.strapiapp.com/typo_tolerance_2_gif_4409419422.gif)](https://typo-tolerance.meilisearch.com/)

Typo tolerance is disabled for the ISBN field ([see demo](https://typo-tolerance.meilisearch.com/))

4\. Language support
--------------------

Linguistic specificities vary a lot between languages using the Latin alphabet and others like Arabic or Chinese. As of Postgres 15, full text search dictionaries are unavailable for Simplified and Traditional Chinese, Korean, and Japanese, among others. This means resorting to specific implementations for different languages.

<Callout>
  To see the [list of languages supported by Postgres full text search](https://gist.github.com/Strift/3155b9238bac6495d4cd57b83d78415f), you can run the \`\\dFd\` command in Postgres.
</Callout>

Language support constraints are amplified in managed environments like Amazon RDS where users are unable to access the filesystem. This restricted access prevents them from implementing custom dictionaries, stemmers, synonyms, and more.

Meilisearch comes with [optimized language support](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/language) for Chinese, Japanese, Korean, Hebrew and more, on top of all languages using whitespace to separate words.

<Callout>
  Thanks to its vibrant open-source community, Meilisearch benefits from native speakers' contributions to [improve language-specific features](/blog/improving-meilisearchs-language-support/)
</Callout>

5\. Paying the backend price
----------------------------

Postgres is a database intended to communicate with server-side languages. When building a public-facing client application, it means building an API on top of the database to communicate with clients. On top of additional development time, creating such a proxy entails further problems.

First comes the issue of latency: making requests to an API that queries the database before returning results is bound to take some time. This does not affect dedicated search engines, as they come with a public API intended to deliver data to end users.

Now comes the second issue: security. Search engines APIs are designed, from the start, for public consumption. And security’s baked in for that use case. By default, API keys restrict search requests, while advanced features like [tenant tokens enable multi-tenancy](https://meilisearch.org.cn/docs/learn/security/tenant_tokens#tenant-token-payload-reference).

[![Example of tenant tokens usage](https://unable-actionable-car.media.strapiapp.com/meili_create_token_c6fb7330a6.gif)](https://tenant-token.meilisearch.com/)

Using tenant tokens to restrict access to documents ([see demo](https://tenant-token.meilisearch.com/))

6\. Scaling limitations
-----------------------

There is a legitimate motive for wanting to keep all data in a single database. But having search-related data in the main database comes with great technical consequences. Full text search queries on large dataset become costly with Postgres, especially when ranking results and computing facet counts.

Monolithic databases often become the bottleneck of applications that need to scale. Don’t add unnecessary search-related costs to this resource when you can avoid it. These costs are only multiplied when building user-facing applications with high traffic.

Unlike relational databases, full text search engines like [Meilisearch use inverted indexes](/blog/best-practices-for-faster-indexing/)his data structure creates information redundancy to allow quicker information retrieval. It is designed to perform search operations, and will naturally outperform relational databases on large datasets. And, when search usage spikes, there is a single service that needs scaling.

7\. Relevancy
-------------

As we mentioned earlier, relevant search requires typo tolerance, custom rankings, and synonyms. In modern applications, users expect results to update on each keystroke, which requires [prefix searching](https://meilisearch.org.cn/docs/learn/advanced/prefix). But Postgres full text search `ts_rank` function only allows attribute weighting. When using the `pg_trgm` extension, developers are left to implement their own sorting based on similarity.

In search-focused databases, the concepts of result ranking, attribute priority, numbers of word matched, and exactness of the query are first-class concepts. They match high-level APIs that allow explicit fine-tuning of search behavior. This makes it easier to make these concepts available to non-tech, business stakeholders. This was cited as a key reason for [why Bookshop chose Meilisearch](/blog/bookshop-increases-search-based-purchases/)or their ecommerce search.

8\. Missing out on InstantSearch libraries
------------------------------------------

When it comes to search experience, websites and applications often implement the same user interface patterns: a textual search bar, checkbox lists of facets, range sliders, sorting menu, page navigation, etc. The open-source InstantSearch library comes with implementations of all these features in the form of widgets available via SDKs in JavaScript, iOS, and Android.

When time to market is paramount, it’s hard to pass on such niceties. Supported by Algolia, the InstantSearch libraries enjoy wide adoption and several search engine databases come with [InstantSearch-compatible APIs](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch). Read our Nuxt guide for ecommerce search to learn [how to implement InstantSearch widgets with Vue](/blog/nuxt-ecommerce-search-guide/)Callout>
  Prefer React? Read our tutorial on [using InstantSearch with React](/blog/instant-meilisearch/)/Callout>

9\. Limited Cloud support
-------------------------

Outsourcing the provisioning, maintenance, and scaling of servers is a common strategy in the Cloud era. Instead of managing servers, teams can focus on delivering value to their users. Postgres, like other databases, is available in a wide range of Cloud offerings for managed services. Unfortunately, managed services often come with limitations.

In the case of Postgres, implementing state-of-the-art full text search requires installing extensions. Moreover, fine-tuning language dictionaries and more configuration require access to the file system. Unfortunately, this means that many features are unavailable in Cloud environments.

To enable delegation of infrastructure, search engines often come with [dedicated cloud services](https://meilisearch.org.cn/cloud). Those bespoke platforms do not compromise and allow using the full palette of search features. Moreover, customers can benefit from premium SLAs, support, and other enterprise services tailored to their searching use cases.

* * *

Postgres is a great, flexible database that allows the implementation of many custom, all-in-one solutions. Its full text search features might be sufficient for basic search, but it falls short when it comes to real-time search with relevance concerns. These limitations become worse on large datasets. It’s only natural, because Postgres is a database, not a search engine.

Meilisearch is an open-source search engine to build fast and relevant search experiences. It aims at providing state of the art experiences for end users while providing a simple and intuitive developer experience. You can try it by [running Meilisearch locally](https://meilisearch.org.cn/docs/learn/getting_started/quick_start) or creating an account [for free on Meilisearch Cloud](https://cloud.meilisearch.com/login).

* * *

Learn more about what Meilisearch can bring to your business

[Talk to a search expert](https://dub.sh/meili-sales#comparisons&utm_source=blog&utm_medium=vs-postgres)

* * *

For more things Meilisearch, you can join the community on [Discord](https://discord.meilisearch.com/) or subscribe to the [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). You can learn more about the product by checking the [roadmap](https://roadmap.meilisearch.com/) and participating in [product discussions](https://github.com/meilisearch/product/discussions).

When does Postgres stop being good enough for full text search?

Are you wondering if Meilisearch is the right choice for you? Well, don't worry! We're here to help make that decision a bit easier for you. In this article, we'll break down the main strengths and weaknesses of both search engines.

Why should I consider Meilisearch over Elasticsearch?
-----------------------------------------------------

If you are looking for a search engine that attempts to do everything from app search to log analytics, and you don’t mind spending the time and effort necessary to make it work for your search needs, then Elasticsearch might be suitable for you.

But if ease of use, performance, and relevancy are important to you, Meilisearch was made for you. Exhausted from scouring documentation pages to configure an extensive list of parameters just to deliver a decent search experience? We understand your frustration.

Let’s be honest, even if you have the most flexible and powerful tool in the world, it doesn’t count for much if it’s a pain to use and understand. That's why we made Meilisearch: to give developers the tool they need to create remarkable search experiences effortlessly.

Before making your decision, you need to understand the differences between the two engines and what you plan to use them for.

Understanding the key differences and their importance
------------------------------------------------------

Since its creation in 2010, Elasticsearch has quickly become a popular option in the search engine market, finding use in all kinds of applications and datasets. However, while it is undeniably a powerful tool for heavy full-text search, the need for extensive configuration before use means that all that power comes with a high resource cost.

Elasticsearch's open-source nature has played a significant role in its popularity and adoption. And, because historically there haven't been many open-source alternatives, developers continue to go for Elasticsearch as the default solution and wind up losing a disproportionate amount of time on setup and training.

This is where Meilisearch comes in. It’s an open-source, easy-to-use, fast, relevant, and typo-tolerant search engine that fits effortlessly into any app, website, and workflow. It works out-of-the-box with default settings that meet the needs of most projects, while also being highly customizable to meet individual needs.

What is Elasticsearch made for?
-------------------------------

Shay Bannon created Elasticsearch, a distributed search and analytics engine based on the [Apache Lucene library](https://lucene.apache.org/core/). Elasticsearch is now developed alongside other tools for data collection, analysis, and visualization. These products are designed to be used as an integrated solution called the "Elastic Stack".

### Key strengths

Elasticsearch’s main strengths lie in its flexibility and scalability, as well as its capabilities for aggregation and analysis.

**Flexibility**

Elasticsearch provides search and analytics for all types of data. It has extensive use cases spanning everything from simple site search, management and analysis of log data to endpoint security.

**Scalability**

Elasticsearch’s distributed architecture enables scalability by adding or removing nodes from the cluster, making it a suitable choice for managing large volumes of data.

**Aggregation and analysis**

Elasticsearch provides advanced aggregation and analytics capabilities that enable users to extract insights from their data and perform sophisticated analysis operations.

### Challenges

Elasticsearch is a very powerful tool, but it’s also a very complex one. It can be arduous to set up and maintain. Since there are no defaults for implementing a basic site or app search, it needs to be configured. While Elasticsearch has extensive documentation; there is a lot to learn before jumping into implementation, which can be daunting, especially for new users.

Before you can perform your first search, there are several parameters you'll need to set up, including relevancy or typo tolerance, a must-have in a modern app or site search. It can take some time to become familiar with the process.

Let's look at an example. Given [a dataset of news headlines](https://www.kaggle.com/datasets/rmisra/news-category-dataset), the request below asks for results containing “Khloe Kardashian Kendall Jenner” in the headline:

```JSON
GET news_headlines/_search
{
 "query": {
   "match": {
   "headline": {
       "query": "Khloe Kardashian Kendall Jenner"
   }
   }
 }
}
```

Here is Elasticsearch’s default response:

```JSON
{
   "_index": "news_headlines",
   "_type": "_doc",
   "_id": "E9CoJ3cBwsjPafpAr39g",
   "_score": 17.15928,
   "_source": {
       "_date": "2015-05-16",
       "short_description": "",
       "@timestamp": "2015-05-16T00:00:00.000-06:00",
       "link": "https://www.huffingtonpost.com/entry/kendall-jenner-kourtney-kardashian-height-difference_n_7297968.html",
       "category": "ENTERTAINMENT",
       "headline": "Kendall Jenner And Kourtney Kardashian Are Not Quite Twins In Hilarious Instagram Photo",
       "authors": "Lily Karlin"
   }
}
```

Contrary to what one might expect, the first result returned by Elasticsearch is not the most relevant; it only contains some of the search terms. As you can see in the \`headline\` field: it contains “Kendall Jenner”, which is expected, but instead of “Khloe Kardashian”, we find “Kourtney Kardashian”.

When a query contains multiple terms, users typically expect the search engine to prioritize results that contain all the terms rather than just some of them. In other words, users generally expect an "AND" operation rather than an "OR" when searching for multiple terms.

Elasticsearch’s match query uses the "OR" logic by default. If a document contains one of the search terms, Elasticsearch will consider that document as a hit. If we want all the query terms to be present, we have to add the [“AND” operator](https://elastic.ac.cn/guide/en/elasticsearch/reference/current/query-dsl-match-query.html#query-dsl-match-query-boolean) to the query, but we’ll get far fewer results.

The other option Elasticsearch provides to tweak the relevancy is to specify the minimum number of terms a document should have to be included in the search results with the \`minimum\_should\_match\` parameter.

That’s a lot of effort for something that should be quick and painless. And we are only talking about the very basics of search.

Moreover, querying for “Kardasian”—without the \`h\`— returns no results. This is because Elasticsearch doesn’t allow any typos unless we set an [extra parameter](https://elastic.ac.cn/guide/en/elasticsearch/reference/current/query-dsl-match-query.html#query-dsl-match-query-fuzziness) called \`fuzziness\`.

We sent the same query—with a typo—to Meilisearch:

```JSON
POST /indexes/news_headlines/search
{ 
   "q": "Khloe Kardasian Kendall Jenner" 
}
```

With zero configuration, the first result returned by Meilisearch was:

```JSON
{
   "link": "https://www.huffingtonpost.com/entry/khloe-kardashian-licks-kendall-jenner-abs_us_559d7e27e4b05b1d028f98cf",
   "headline": "Khloe Kardashian Licks Kendall Jenner's 'Irresistible' Abs In New Instagram Photo",
   "category": "ENTERTAINMENT",
   "short_description": "Khloe and Kendall shared quite the intimate moment at the gym!",
   "authors": "Julia Brucculieri",
   "date": "2015-07-08",
   "id": 94869
}
```

As we saw earlier, achieving this level of relevancy with Elastic required adjusting several search parameters. So, just because you can do anything with Elasticsearch, doesn’t mean you should. In many cases, Elasticsearch might just be overkill.

What is Meilisearch made for?
-----------------------------

Meilisearch is an open-source, user-focused search engine written in Rust. It was created in 2018 to provide an accessible search solution designed to meet a vast majority of needs, with zero configuration required and a high degree of customization.

Meilisearch delivers an instant search experience, including powerful built-in capabilities such as [search-as-you-type](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#comparisons&utm_source=blog&utm_medium=vs-elasticsearch&utm_content=search-as-you-type#features), [typo tolerance](https://docs.meilisearch.com/learn/configuration/typo_tolerance.html#comparisons&utm_source=blog&utm_medium=vs-elasticsearch), [faceted search](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#comparisons&utm_source=blog&utm_medium=vs-elasticsearch&utm_content=faceted-search#features), [geo search](https://docs.meilisearch.com/learn/advanced/geosearch.html#release-post&utm_id=v1-announcement&utm_content=geosearch), and [multi-tenancy](https://meilisearch.org.cn/docs/learn/security/tenant_tokens#comparisons&utm_source=blog&utm_medium=vs-elasticsearch). Its [first major version was released](/blog/v1-enterprise-ready-stable/#comparisons&utm_source=blog&utm_medium=vs-elasticsearch)n early 2023, making it completely stable, forward-compatible, and enterprise-ready.

### Key strengths

Meilisearch focuses on simplicity, relevancy, and performance.

**Simplicity**

Meilisearch aims for a simple and intuitive experience for developers. It works out of the box with no configuration needed. But you can easily tailor it to your needs in just a few minutes, so you don't have to be a search expert to get the most out of Meilisearch.

**Relevancy**

Meilisearch provides a pre-configured set of ranking rules, delivering high relevancy right out of the box. These default settings were developed iteratively by working directly with the community and are suitable for most use cases. These rules can be customized to suit individual requirements.

**Performance**

Meilisearch aims to provide a search-as-you-type experience, returning results within a maximum of 50 milliseconds.

Challenges
----------

While it can handle a considerable amount of data, the open-source version of Meilisearch (self-hosted) may not be the best fit for applications requiring a distributed architecture to handle massive amounts of data across multiple nodes or clusters.

Meilisearch Cloud, on the other hand, already provides scalability and high availability with its [Pro and Enterprise plans](https://meilisearch.org.cn/pricing#comparisons&utm_source=blog&utm_medium=vs-elasticsearch).

However, our team is currently [gathering feedback](https://github.com/meilisearch/product/discussions/617) to implement a distributed open-source Meilisearch in the near future.

Meilisearch is primarily designed to deliver performant instant-search experiences aimed at end-users and does not provide comprehensive data analysis features. While Meilisearch can help you search through your data and retrieve relevant results instantly, it has yet to offer advanced data analysis capabilities such as data visualization or statistical analysis. Meilisearch Cloud plans to provide similar features down the line, as you can see in its [roadmap](https://roadmap.meilisearch.com/c/129-search-analytics).

Meilisearch or Elasticsearch?
-----------------------------

Hopefully, it’s clear by now that one search engine is not better than the other. The most important factor lies in the purpose you are using it for. Both search engines have their strengths and weaknesses, and it is essential to understand them to make the right choice for your particular use case.

Meilisearch is not made to search through billions of large text files or parse complex queries. This kind of searching power would require a higher degree of complexity and lead to slower search experiences, which runs against our instant search philosophy. For those purposes, look no further than Elasticsearch; it’s an excellent solution for companies with the necessary resources, whether that be the financial means to hire consultants or the time and money required to implement it themselves.

Meilisearch is a perfect choice if you need a developer-friendly tool to effortlessly deploy a typo-tolerant search, intuitive for users and return results instantly with excellent relevance out of the box. It's ideal for on-site and in-app search, including [e-commerce](/blog/minipouce-top-grade-customer-experience/) [documentation](/blog/symfonys-solution-for-documentation-search/) and [content search](/blog/hugging-face-democratizes-ai/) * *

Learn more about what Meilisearch can bring to your business

[Talk to a sales expert](https://dub.sh/meili-sales#comparisons&utm_source=blog&utm_medium=vs-elasticsearch)

* * *

Conclusion
----------

To recap, we have addressed both search engines' main strengths and weaknesses, but we have only scratched the surface. The capabilities of both search engines go far beyond what this article has covered. For a comprehensive comparison of Meilisearch with Elasticsearch and other search engines, please refer to the [dedicated section](https://docs.meilisearch.com/learn/what_is_meilisearch/comparison_to_alternatives.html#comparisons&utm_source=blog&utm_medium=vs-elasticsearch#comparison-table) in our documentation.

Do you want to see Meilisearch in action? Try our [demo](https://where2watch.meilisearch.com/#comparisons&utm_source=blog&utm_medium=vs-elasticsearch). You can also run it and start searching through your data in less than five minutes by following our [quick start guide](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#comparisons&utm_source=blog&utm_medium=vs-elasticsearch).

We continuously collaborate with the open-source community to develop new features and provide the best developer and search experience. See what's next on our [public roadmap](https://roadmap.meilisearch.com/)!

If you have any feedback or suggestions, please join our [Discord server](https://discord.gg/meilisearch) and have a chat with us!

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