10 of the best Algolia alternatives [With features, pricing & more]

Enterprise search: a comprehensive guide

When your AI application starts hemorrhaging $500 daily in OpenAI API calls and users are drumming their fingers waiting 2 seconds for responses, semantic caching isn't just a nice-to-have—it's a lifeline.

While traditional caches match queries word-for-word, semantic caching understands the deeper meaning.

In this guide, you'll discover how to implement semantic caching that slashes response times and API costs. Whether you're building a chatbot, search engine, or AI-powered application, these strategies will help you create lightning-fast, cost-effective semantic search that keeps users happy and your infrastructure lean. You'll learn how to:

- Reduce response times dramatically
- Achieve massive API cost savings
- Handle similar questions without repeatedly querying language models

## Introduction to semantic caching

Semantic caching revolutionizes how we store and retrieve information in modern applications. Traditional caching relies on exact matches, while semantic caching understands the meaning behind queries. This allows systems to recognize and respond to similar questions, even if they're worded differently.

This technique is valuable for AI-powered applications. Processing similar queries repeatedly can be both time-consuming and expensive.

### What is semantic caching

Semantic caching is a sophisticated data retrieval technique. It stores both the data and the contextual meaning of queries and their results. When a user asks a question, the system analyzes its semantic meaning rather than looking for an exact match.

For example, "What's the weather in New York?" and "Tell me New York's forecast" can pull from the same cached response, saving processing time and resources.

### Comparing semantic caching vs traditional caching

![Semantic Cache](https://unable-actionable-car.media.strapiapp.com/how_to_cache_semantic_search_1996414fc7.avif)

Traditional caching and semantic caching serve different purposes in modern applications. Here's a comparison:

- **Traditional caching**: Relies on exact matches and specific keys to retrieve data. Think of it as using a filing cabinet where you need the exact file name.
- **Semantic caching**: Understands context and meaning. It's like having a smart assistant who can find related information even when you ask in different ways.

### Advantages of semantic caching

Semantic caching offers several advantages for real-world applications:

- **Reduced API costs** by avoiding redundant queries.
- **Faster response times** for similar questions.
- **More consistent answers** across related queries.
- **Better scalability** for AI-powered applications.

### When to choose semantic caching

Consider semantic caching for your application if:

- **Users might ask the same question in different ways.**
- Response consistency is crucial.
- API costs need to be optimized.
- Real-time response is essential.

## Core concepts of semantic caching

Understanding the fundamental components and applications of semantic caching is crucial for successful implementation. Let's explore how these elements work together to create efficient caching systems that enhance your application's performance.

### Key components of semantic caching systems

Several critical components form the foundation of semantic caching.

- [**Embedding model**](https://www.meilisearch.com/blog/what-are-vector-embeddings): Converts queries into vector representations capturing their meaning.
- [**Vector database**](https://www.meilisearch.com/blog/what-is-a-vector-database): Stores these vectors, allowing for quick similarity searches for new queries.
- **Cache layer**: Sits between your application and underlying systems, managing query-response pairs and their semantic meanings. It decides whether to serve cached content or process new requests.
- **Vector search mechanism**: Evaluates incoming queries against stored embeddings using similarity thresholds to determine cache hits.

### Semantic cache in RAG

**Retrieval-Augmented Generation (RAG)** systems use semantic caching to improve performance and reduce costs. When a user submits a query, the system first checks the semantic cache for similar questions before going through the full retrieval and generation process. This is especially effective for handling common queries in customer support or knowledge base applications.

The integration process involves three main steps:

1. Embed the incoming query into a vector representation.
2. Search the cache for semantically similar queries.
3. If a match is found, return the cached response immediately. If not, proceed with the traditional RAG pipeline and store the new result for future use.

By understanding these core concepts and components, you can effectively implement and optimize semantic caching in your AI applications.

## Implementing semantic caching for an e-commerce site

Semantic caching can significantly enhance how e-commerce platforms handle product searches and customer queries. By adopting a smart caching mechanism, businesses can boost search performance and improve user experience.

![how to cache semantic search](https://unable-actionable-car.media.strapiapp.com/how_to_cache_semantic_search_ba174395a9.avif)

We will go through these key steps:

1. Configure embedding model by initializing OpenAI embeddings and Meilisearch client with appropriate connection parameters.
2. Create a vector store with custom configuration, specifying index name, embedding dimensions, and text key for efficient semantic search storage.
3. [Implement semantic search](https://www.meilisearch.com/docs/guides/ai/openai) function to convert queries into vector embeddings and perform similarity-based retrieval from the vector store.
4. Develop cache management logic to handle cache hits and misses, including fallback mechanisms for database searches and optional cache updates.

### Setting up the semantic caching infrastructure

Building a semantic caching system requires careful planning and the right tools. Here's a step-by-step guide to implementation.

#### Choosing core technologies

Start by [selecting the technologies for your system](https://www.meilisearch.com/blog/choosing-the-best-model-for-semantic-search). **Meilisearch is an excellent vector search engine** for semantic caching. For this example, we'll use Python with OpenAI embeddings to create a context-aware caching solution.

#### Installing required dependencies

Set up your project environment by installing the necessary libraries:

```bash
pip install meilisearch langchain openai python-dotenv
```

This command installs essential tools like Meilisearch for vector storage and LangChain for embedding management.

#### Configuring the embedding model

The core of semantic caching is converting queries into meaningful vector representations. Below is a sample configuration using OpenAI embeddings:

```python
from langchain.embeddings.openai import OpenAIEmbeddings
import meilisearch

# Set up OpenAI embeddings
embeddings = OpenAIEmbeddings()

# Initialize Meilisearch client
client = meilisearch.Client(
    url="your_meilisearch_url",
    api_key="your_api_key"
)
```

#### Creating a vector store for semantic caching

Setting up a vector store is crucial for implementing an effective semantic cache. Here's a comprehensive approach to creating a vector store using Meilisearch and OpenAI embeddings:

```python
# Create vector store with custom configuration
vector_store = Meilisearch(
    client=client,
    embedding=embeddings,
    index_name="semantic_cache",
    text_key="content"
)

# Optional: Define embedder configuration
embedder_config = {
    "custom": {
        "source": "userProvided",
        "dimensions": 1536  # OpenAI embedding dimensions
    }
}
```

This setup allows you to create a robust semantic cache that can efficiently store and retrieve contextually similar queries and their responses.

### Implementing the semantic cache mechanism

The key to semantic caching is creating a **retrieval system that understands query intent**.

#### Creating a semantic search function

Here’s an example of a semantic search function:

```python
def semantic_cache(query, k=3):
    # Convert query to vector embedding
    query_embedding = embeddings.embed_query(query)
    
    # Perform similarity search in Meilisearch
    results = vector_store.similarity_search(
        query=query,
        k=k
    )
    
    return results
```

This function:

- Converts the query into a vector representation.
- Searches for semantically similar results in the vector store.
- Returns the most relevant matches.

#### Handling cache hits and misses

A robust caching strategy must handle both cache hits and misses effectively:

```python
def process_product_search(query):
    # Check the semantic cache
    cached_results = semantic_cache(query)
    
    if cached_results:
        # Cache hit: return cached results
        return {
            "source": "cache",
            "results": cached_results
        }
    else:
        # Cache miss: perform full database search
        full_results = perform_database_search(query)
        
        # Optionally, store new results in cache
        update_semantic_cache(query, full_results)
        
        return {
            "source": "database",
            "results": full_results
        }
```

### Performance optimization techniques

To ensure efficient semantic caching, consider these strategies:

- **Set a similarity threshold** (e.g., 0.85–0.95) to filter results.
- Limit cache size to prevent memory issues.
- Use time-to-live (TTL) to refresh outdated cache entries.
- Regularly update your embedding model for better accuracy.

### Testing your semantic cache

Testing is critical to ensure reliability. Test scenarios should include:

- Exact match queries.
- Semantically similar queries.
- Completely new queries.
- Edge cases with minimal context.

**Thorough testing ensures your system performs well across diverse search patterns.**

Meilisearch’s fast vector search capabilities make semantic caching not only feasible but also highly efficient for e-commerce platforms.

## Adding semantic caching to RAG system

Retrieval-Augmented Generation (RAG) systems can greatly benefit from semantic caching. This integration **improves efficiency, reduces costs, and enhances responsiveness** in AI applications.

### Understanding RAG and semantic caching integration

Semantic caching in RAG systems focuses on storing and retrieving query responses based on their meaning, not just exact text matches. This allows AI applications to quickly access previously generated responses while maintaining contextual relevance.

Key steps in integrating semantic caching into a RAG workflow include:

1. **Embedding Transformation**: Convert user queries into vector representations that capture semantic meaning. Use embedding models like BERT or OpenAI's embedding APIs for this transformation.

2. **Cache Lookup Mechanism**: Use a vector database for fast, similarity-based searches. **Meilisearch is a strong choice** for its efficient semantic search capabilities, enabling quick matches between incoming queries and cached responses.

### Implementing the semantic caching layer

Implementing a semantic caching layer requires creating a specialized class that manages query embeddings, similarity checks, and cached response retrieval. This code provides a practical approach to efficiently storing and accessing semantically similar query responses.

```python
class RAGSemanticCache:
    def __init__(self, embedding_model, vector_store):
        self.embedding_model = embedding_model
        self.vector_store = vector_store
        self.similarity_threshold = 0.85

    def retrieve_cached_response(self, query):
        query_embedding = self.embedding_model.embed_query(query)
        similar_results = self.vector_store.similarity_search(
            query_embedding, 
            threshold=self.similarity_threshold
        )
        return similar_results[0] if similar_results else None
```

This class:

- Embeds queries into vectors.
- Searches for semantically similar results in the vector store.
- Returns a cached response if it meets the similarity threshold.

<Callout intent="note">
	
	To ensure your semantic caching layer performs efficiently, apply these strategies:
	
	- **Dynamic Similarity Thresholds**: Adjust thresholds based on your domain and use case.
	- **Cache Maintenance**: Use expiration and refresh mechanisms to keep responses relevant.
	- **Multi-Turn Context Tracking**: For conversational systems, include conversation history in cache retrieval to maintain context.
	
	
</Callout>

### Practical implementation example

Here’s how semantic caching can be integrated into a RAG workflow:

```python
def rag_with_semantic_cache(query, rag_system, semantic_cache):
    # Check semantic cache first
    cached_response = semantic_cache.retrieve_cached_response(query)
    
    if cached_response:
        return cached_response
    
    # If no cached response, use traditional RAG
    retrieved_docs = rag_system.retrieve_documents(query)
    generated_response = rag_system.generate_response(query, retrieved_docs)
    
    # Store the new response in the semantic cache
    semantic_cache.store_response(query, generated_response)
    
    return generated_response
```

This function:

- Checks the semantic cache for a response.
- Falls back to the RAG system if no cached response exists.
- Stores new responses in the cache for future use.

### Benefits for different applications

Semantic caching is especially useful in:

- Customer support chatbots.
- Enterprise knowledge management systems.
- Educational platforms.
- Research and analytics tools.

## Optimization strategies

Once your semantic caching system is running, focus on optimization to ensure it continues to deliver maximum value. Let's explore key strategies to maintain and improve your system's performance over time.

### Ensuring accuracy & consistency

Maintaining high accuracy in semantic caching requires regular monitoring and updates.

- **Implement version control** for cached responses to track changes and ensure users receive the most current information.
- **Invalidate or update related cache entries** when source data updates to prevent serving outdated information.
- **Cache consistency** becomes particularly important in distributed systems. Use a publish-subscribe system to propagate updates across your infrastructure, ensuring all cache nodes remain synchronized.
- **Regular validation of cached responses** helps maintain quality. Set up automated checks to verify cached responses still match their original context and meaning. Periodically compare cached responses against fresh generations to identify any drift in accuracy.

### Improving performance and efficiency

**Performance optimization** starts with careful monitoring of key metrics. Track cache hit rates, response times, and system resource usage to identify bottlenecks and optimization opportunities. Use dashboards to **visualize these metrics** and establish baseline performance expectations.

Consider these advanced optimization techniques:

- **Predictive caching** for frequently accessed queries.
- **Tiered caching** with different storage solutions for hot and cold data.
- **Optimize vector similarity calculations** for faster retrieval.
- **Compress embeddings** while maintaining accuracy.

Regular performance audits help identify improvement areas. Compare your system's performance against benchmarks and adjust your caching strategy accordingly. Document performance improvements and their impact on user experience and operational costs.

### Common pitfalls and how to avoid them

**Over-aggressive caching** can lead to memory issues and degraded performance. Start with conservative cache sizes and grow based on actual usage patterns rather than theoretical maximums.

**Poor similarity threshold selection** represents another challenge. Setting thresholds too low leads to false positives and incorrect responses. Setting them too high results in unnecessary cache misses. Monitor your threshold settings and adjust based on user feedback and performance metrics.

Watch out for these additional pitfalls:

- **Neglecting cache maintenance and cleanup**.
- **Failing to handle edge cases** in query processing.
- **Ignoring the impact of cache invalidation** on system performance.
- **Not implementing proper error handling** and fallback mechanisms.

**Focusing on these optimization strategies** ensures your semantic caching system remains efficient and effective, providing consistent performance improvements over time.

## Semantic caching: the path to high performance search

**Implementing semantic caching for your search system is a powerful way to boost performance, reduce costs, and enhance user experience.**

By following this guide's systematic approach to setting up semantic caching—from infrastructure assessment to optimization strategies—you'll be well-equipped to handle growing query volumes while maintaining fast response times.

<Callout intent="tip">
	
	No search engine expertise required! [Get started with Meilisearch Cloud](https://meilisearch.com/get-started) and experience an intuitive platform that makes implementing powerful search effortless, even for developers new to search technologies.
	
	
</Callout>

How to cache semantic search: a complete guide

How personalization and recommendations enhance search and discovery

Fuzzy search: a comprehensive guide to implementation

Imagine this: You're a SaaS product manager who just left a board meeting where the CEO announced, "We need to invest in AI search!" The budget is set, the engineering team is eager, and time is running out. But as you explore **AI-powered search**, the reality strikes hard. The field is filled with buzzwords, big promises, and high costs. You start to question, "Will this really deliver the ROI we need?"

The problem is an unrealistic outlook, wrong metrics, and the "more is better" mentality regarding features. SaaS companies spend millions on AI search, only to deal with rising costs, unhappy users, and doubts about their approach.

But there's good news. With a **pragmatic, business-focused approach**, you can use AI search to achieve real results. This can be done without overspending or losing focus.

This article will cover crucial strategies for **implementing AI-powered search** effectively. Topics include budgeting, feature selection, measuring success, and future-proofing your SaaS. Prepare to move beyond the hype and truly harness AI search.

## Core features of new generation AI-powered search

### Demystifying AI search technologies

AI-powered search represents a significant leap forward from traditional keyword matching. It employs sophisticated algorithms that understand context and user intent.

At its core, this technology combines machine learning and natural language processing to interpret queries as humans naturally express them. For instance, when Bookshop.org implemented AI search, their system understood complex queries about books. It accounted for variations in titles, author names, and even thematic searches that traditional systems would struggle with.

![Differnent Layers Of Ai Powered Search](https://unable-actionable-car.media.strapiapp.com/ai_powered_search_834ac28328.avif)

### Storage layer

#### Vector database

[Vector databases](https://www.meilisearch.com/blog/what-is-a-vector-database) form the foundation of modern AI search systems. They store complex data representations that capture semantic relationships between items. Unlike traditional databases, these specialized systems can efficiently process high-dimensional data. **This enables lightning-fast similarity searches across millions of items.**

#### Multiple DBs for full-text search

The architecture of modern search systems often employs multiple specialized databases features working together. These include prefix databases for instant search suggestions, inverted indexes for rapid full-text search, and specialized stores for handling typos and variations.

### Capabilities layer

#### Full-text search

[Full-text search](https://www.meilisearch.com/blog/how-full-text-search-engines-work) remains a cornerstone of modern search systems. It provides precise lexical matching with advanced features like typo tolerance and prefix searching. This capability is especially valuable in e-commerce scenarios. Users often search for specific product codes or exact phrases.

#### Vector search

[Vector search](https://www.meilisearch.com/blog/introducing-vector-search) enables semantic understanding by converting search queries and content into mathematical representations that capture meaning, not just keywords. **This technology powers the ability to find relevant results even when queries don't exactly match the target content.** It was transformative for Bookshop.org's vast catalog of six million books.

#### Hybrid search

[Hybrid search](https://www.meilisearch.com/blog/introducing-hybrid-search) combines the precision of full-text search with the semantic understanding of vector search. It offers the best of both worlds. This approach allows systems to handle both precise queries and more exploratory searches effectively. It delivers relevant results regardless of how users express their needs.

### Application layer

The application layer transforms raw search capabilities into practical features that drive business value. It includes personalization systems that adapt results based on user behavior, [semantic search that understands natural language queries](https://www.meilisearch.com/blog/choosing-the-best-model-for-semantic-search), and recommendation engines that suggest related items.

Hugging Face leverages this layer to help users discover relevant AI models based on specific use cases and preferences. Similarly, HitPay uses it to enable merchants to prioritize specific products in search results based on business rules.

<Callout intent="note">
	
	**Want to dive deeper?** Check out our [detailed comparison of full-text vs vector search](https://www.meilisearch.com/blog/full-text-search-vs-vector-search) to understand their unique strengths and use cases.
	
	
</Callout>

## Use cases that demonstrate tangible benefits

AI-powered search is transforming how users interact with digital platforms beyond the traditional search bar.

[Bookshop.org's implementation](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases) demonstrates a compelling impact: **their conversion rate jumped from 14% to 20% after switching to AI-powered search, representing a 43% increase in search-to-purchase conversion**. This improvement came from the system's ability to understand complex book queries and handle their massive six-million-item catalog effectively.

[For Hugging Face](https://www.meilisearch.com/blog/hugging-face-democratizes-ai), **AI search solved the challenge of making 220,000 AI models, 38,000 datasets, and 60,000 demos discoverable to their technical audience**. Their implementation goes beyond simple keyword matching, allowing developers to find relevant models based on use cases, performance characteristics, and technical requirements. The system's ability to understand technical context and relationships between different AI models has made it significantly easier for users to find the right tools for their specific needs.

[HitPay's experience](https://blog.meilisearch.com/hitpay-streamlines-search/) highlights how AI search can bridge the gap between online and offline retail operations. Their point-of-sale system uses AI search to help sales assistants quickly locate specific products across multiple locations, while simultaneously powering their e-commerce storefronts. The system's ability to sync product catalogs across channels and prioritize frequently accessed items has resulted in a 50% increase in search API response speed. **This directly improves the customer experience both in-store and online.**

## Budgeting for AI-powered search

### Determining AI budget allocation

<Callout intent="note">
	
	Enterprises are rapidly adopting generative AI, [dedicating upwards of 10% of their IT budgets by 2025](https://techstrong.ai/articles/generative-ai-consuming-larger-percentage-of-it-budgets/). You can allocate between 5-15% of IT budgets to AI initiatives. the ideal investment varies based on company size, sector, and goals.
	
	
</Callout>

When budgeting for AI-powered search integration, consider these factors:

- The scale of your existing search infrastructure
- The complexity of your data and search requirements
- The size of your user base and expected growth
- Your team's existing AI and machine learning expertise
- The level of customization you require

### Breakdown of costs: R&D, implementation, ongoing optimization

#### The true cost of AI search implementation

Implementing AI-powered search requires careful consideration of both direct and indirect costs. The initial investment covers infrastructure setup, data preparation, and integration costs.

For example, Bookshop.org's transition from Elasticsearch to a more sophisticated AI search solution involved not only technology costs but also resources to properly index and configure their six-million-item catalog. Companies must also factor in ongoing costs for maintenance, updates, and infrastructure scaling as search volume grows.

<Callout intent="note">
	
	[Check out our analysis of Meilisearch vs. Elasticsearch](https://www.meilisearch.com/blog/meilisearch-vs-elasticsearch), for a detailed comparison of the two search solutions.
	
	
</Callout>

**HitPay demonstrates this approach – they began with an open-source solution to validate their use case before transitioning to a managed cloud service.** This allowed them to optimize costs while ensuring reliability for their business-critical search functions.

#### Avoiding budget black holes

The most significant hidden costs often stem from unexpected infrastructure scaling needs and ongoing optimization requirements. Companies frequently underestimate the resources needed to maintain search relevance and manage growing data volumes.

![ai powered search](https://unable-actionable-car.media.strapiapp.com/ai_powered_search_4def6c80b2.avif)

#### Infrastructure costs and scalability considerations

Infrastructure is critical for AI-powered search implementation.** Key considerations include:

- **Computing power:** AI models, especially during training, require substantial computational resources. This may necessitate investment in high-performance hardware or cloud computing services.
- **Storage:** AI systems need to process and store large amounts of data. Ensure your storage solutions can handle this increased load.
- **Network capacity:** If your AI search system processes queries in real-time, upgrade your network infrastructure to handle increased traffic.
- **Scalability:** As your user base grows, your AI search system needs to scale. Cloud-based solutions offer flexibility for scaling but may have higher long-term costs compared to on-premises solutions.

### ROI calculation framework for AI search investments

Measuring return on investment requires tracking both direct and indirect impacts of AI search implementation. Key metrics should include:

- Conversion rate improvements (like Bookshop.org's 43% increase)
- Operational efficiency gains (such as HitPay's 50% faster search response times)
- Customer satisfaction metrics
- Reduced support costs from improved self-service capabilities

For SaaS businesses, significant returns often come from improved user engagement and reduced churn. When calculating ROI, companies should consider both immediate performance improvements and long-term benefits, like increased customer lifetime value and reduced operational costs.

## KPIs for measuring success

### Beyond vanity metrics

Basic metrics like search volume and click-through rates provide some insight, but often mask deeper performance issues. The most telling indicators come from user behavior patterns and business outcomes.

For example, Bookshop.org's success wasn't measured by the number of searches performed, but by the 20% conversion rate from search to purchase. **This metric is directly tied to revenue generation.**

### Decoding search performance that actually matters

Search performance metrics should reflect real user success rates rather than just technical measurements.

Hitpay's 50% improvement in search API response speed impacts in-store customer service and online shopping experiences.

The most telling metrics often combine multiple data points to create a complete picture of search effectiveness. Consider tracking:

- Search-to-conversion ratio: Like Bookshop.org's jump from 14% to 20% in search-driven purchases
- Query refinement rate: How often users need to modify their search terms to find what they need
- Time-to-content: How quickly users find relevant information after initiating a search
- Zero-result rates: The percentage of searches that yield no results, indicating gaps in content or search capability

These metrics should be analyzed in context with your specific business objectives. For a knowledge base, success might mean reduced time-to-answer. For an e-commerce platform, it could mean higher average order values from search-driven purchases.

### Warning signs: KPIs that signal AI search failure

Several indicators can reveal problems with your AI search implementation. Watch for:

- High abandonment rates after search attempts
- Increased support tickets related to finding information
- Growing use of alternative navigation methods
- Declining engagement with search-discovered content

When Hugging Face noticed users bypassing their search functionality in favor of direct repository browsing, it signaled a need to improve their search relevance and feature set. **Monitoring these warning signs helps identify** issues before they impact user satisfaction and business outcomes.

## Selecting the right AI search features for your SaaS business

### What use cases they enable

AI search features should align with specific business objectives and user needs.

Bookshop.org's implementation demonstrates this principle perfectly. Their focus on semantic understanding and typo tolerance addressed the challenge of helping users find books across a vast catalog, even with imperfect queries. **This targeted approach led to their impressive 43% increase in conversion rates.**

### AI search feature mapping

Semantic search capabilities are valuable for content-rich platforms. Hugging Face's implementation helps users navigate their extensive repository of AI models. Users can find relevant models even when search terms don't exactly match model names or descriptions, significantly improving discovery rates.

Similarity search is particularly useful in e-commerce contexts. HitPay uses this feature to help merchants connect customers with related products. This goes beyond category matching to understand product relationships based on multiple attributes, enhancing cross-selling opportunities.

Personalization features are crucial for platforms with diverse user bases. For instance, HitPay's implementation allows merchants to pin frequently accessed products, automatically adapting search results based on business-specific patterns and preferences.

### When more features mean less value

Not every AI search feature delivers equal value for every business case. Hugging Face's experience shows the importance of prioritizing features that align with core user needs. **They focused on making their vast technical documentation and model repository discoverable rather than implementing every available search feature.**

The key to feature selection lies in understanding user behavior patterns and business objectives. For example, while multi-modal search might seem cutting-edge, Bookshop.org achieved significant results by focusing on fundamental features like semantic understanding and typo tolerance. These features directly addressed their users' primary needs.

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

## Best practices for implementing AI-powered search

### The implementation gauntlet

Successful AI search implementation requires a methodical approach that prioritizes user needs over technical complexity. HitPay's implementation journey is instructive.

They started with a simple internal dashboard search before expanding to customer-facing features. This gradual rollout allowed them to refine their approach with minimal risk. **They achieved a 50% improvement in search API response speed while maintaining system stability.**

When Bookshop.org transitioned to their new search system, they started with a test environment. This allowed them to validate their approach before full deployment. This methodical strategy enabled them to **maintain search performance during their busy holiday season** while implementing significant improvements.

### Critical decision points in AI search deployment

Key decisions during implementation should focus on:

- **Initial feature set selection based on immediate user needs**
- Data indexing strategy and update frequency
- Performance monitoring and optimization approaches
- User feedback collection and integration methods

Bookshop.org's team made their ranking rules accessible to non-developers, allowing for quick iterations based on business needs **without requiring engineering intervention**. This approach enabled them to maintain search relevance during peak periods while continuously improving the user experience.

## Future proofing your SaaS with AI-powered search

### Cutting through the AI hype cycle

The future of AI-powered search extends beyond current capabilities. Successful implementation requires distinguishing between genuine innovations and marketing hype.

Hugging Face's approach to testing vector database integration for semantic search demonstrates how companies can practically evaluate emerging technologies.

**Organizations can evaluate search solutions based on actual business needs. This approach helps avoid chasing trendy technologies that fail to deliver meaningful results.**

<Callout intent="tip">
	
	Stay informed about the latest in search technology, product updates, and industry insights. [Subscribe to our newsletter](https://share-eu1.hsforms.com/1LN5N0x_GQgq7ss7tXmSykwfg3aq) and be the first to know about Meilisearch Cloud's exciting developments.
	
	
</Callout>

### Strategic long-term AI search planning

Long-term success requires building flexibility into search implementations. HitPay's evolution from basic search functionality to a sophisticated multi-channel solution shows how search capabilities can grow alongside business needs. Their experience highlights the importance of choosing solutions that can scale and adapt as requirements change, rather than being locked into rigid implementations that limit future options.

The most sustainable approaches focus on creating adaptable search infrastructures that can incorporate new capabilities as they mature. Bookshop.org's implementation demonstrates this principle. **Their search architecture allows them to continuously refine ranking rules and add new features without disrupting core functionality.** This flexibility enabled them to achieve immediate benefits while maintaining the ability to adopt new AI capabilities as they become available.

As AI technologies evolve, AI-powered search has become a critical differentiator for SaaS businesses. Success stories of companies like Bookshop.org, HitPay, and Hugging Face show that thoughtful implementation of AI search capabilities can deliver concrete business results.

These include increased conversion rates and improved user engagement. The key lies in selecting and implementing solutions that align with specific business needs while maintaining the flexibility to adapt as technology and user expectations evolve.

<Callout intent="tip">
	
	Need a custom search solution tailored to your unique requirements? [Schedule a Consultation](https://meet.meilisearch.com/meetings/cloud/presentation?uuid=d7d3d29c-635f-4544-877d-86ecbd29e7b8) with our expert team and transform your search strategy.
	
	
</Callout>

Beyond the hype: practical AI search strategies that deliver ROI


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.com/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://www.meilisearch.com/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://www.meilisearch.com/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.com/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://www.meilisearch.com/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.com/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://www.meilisearch.com/docs/learn/indexing/tokenization?utm_campaign=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://www.meilisearch.com/docs/reference/api/settings#localized-attributes?utm_campaign=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://www.meilisearch.com/docs/guides/ai/cohere?utm_campaign=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/?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search)!

Searching across multiple languages

Searching across disparate data sources can be a real headache. But aggregating information from various databases or indices enables the creation of more relevant search experiences for users. In this guide, we'll explore what federated search is and the use cases it unlocks.

What is federated search?
-------------------------

Federated search empowers you to conduct searches across multiple systems simultaneously. In the context of a search engine, federated search allows querying numerous indices and retrieving the results in a single, consolidated list.

This differs from having a monolithic index that stores various types of resources where documents could be filtered by type. Leveraging multiple indices enables the implementation of specific ranking rules for each index.

Federated search extends the capabilities of [multi-index search](/blog/introducing-multi-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search merging results into a unified, cohesive response. This allows surfacing the most relevant results across the entire dataset, rather than just the top hits for each individual index.

Federated search is a crucial feature for SaaS applications, where users frequently need to search through all available resources regardless of type. An example you might be familiar with is searching in Slack—a single search bar to find users, messages, and shared documents.

What are the benefits of federated search?
------------------------------------------

Integrating federated search into your application can facilitate more convenient search experiences. By supporting searches across multiple sources, you can build one search bar to query all the data. This eliminates the need for multiple menus or lists of results, resulting in a more intuitive and user-friendly interface.

The search experience is further enhanced by the increased relevance of the results. The comprehensiveness of the returned resources will match items that the user may not have initially considered including in their search (e.g., discovering a prospect's email in meeting notes instead of just contact details).

Uncovering the most pertinent result can be transformative for your users and your business. In an internal tool, relevant results enable users to make more informed decisions. However, in customer-facing applications, it can be the pivotal factor between converting a sale or not.

Locating information effortlessly within your application or e-commerce platform leads to higher customer satisfaction and improved retention. This allows users to extract more value from the service, ultimately elevating the overall experience quality.

How federated search improves developer experience
--------------------------------------------------

Implementing federated search leads to a more streamlined search implementation process. With federated search, results are delivered in a single, unified list, simplifying front-end development. This goes beyond [multi-index search](/blog/introducing-multi-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) because it comes without the added complexity of implementing front-end logic to merge, sort, and paginate the results.

In this setup, the relevance of documents can be further fine-tuned thanks to granular, per-index configuration. This allows tailoring the [relevancy settings](https://www.meilisearch.com/docs/learn/relevancy/relevancy?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) to specific data types (a specific index) rather than the entire dataset (all indices). As a federated search request comes, the search engine can ensure that the most important results rise to the top.

This also simplifies extending the search functionality if new types of documents need to be included in the future. You'll only need to focus on configuring the relevancy settings for the new document type, instead of revisiting the relevancy for the entire dataset.

Conclusion
----------

Federated search enables performing searches against multiple indices of a search engine and returns a single, consolidated list of results. With a search engine that supports federated search, developers can build more relevant user search experiences without incurring additional complexity.

Federated search is available in [Meilisearch 1.10](/blog/meilisearch-1-10/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) and higher. Learn more about implementing federated search with Meilisearch by [reading the documentation](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search#federated-multi-search). Check out which search engines support federated search and other key features via our [comparison of search engines](https://www.meilisearch.com/docs/learn/resources/comparison_to_alternatives?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search).

* * *

[Meilisearch](https://meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=full-text-vs-vector) is an open-source AI-powered 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/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) or subscribe to the [newsletter](https://dub.sh/meili-newsletter?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search). You can learn more about Meilisearch by looking at the [roadmap](https://roadmap.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) and participating in [product discussions](https://github.com/orgs/meilisearch/discussions?ref=blog.meilisearch.com).

What is federated search?

  
[Semantic search](/blog/what-is-a-vector-database/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#what-is-similarity-search) is transforming search technology by providing more accurate and relevant results. However, with many embedding models available, choosing the right one can be challenging. This guide will help you understand the key factors to consider when selecting a model to build semantic search.

Overview
--------

In this guide, we will use the open-source search engine [Meilisearch](https://www.meilisearch.com?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) to perform the semantic searches. For the purpose of the tests, we're using the entry tier of [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) (i.e., the [Build plan](https://www.meilisearch.com/pricing?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) .)

This guide will cover the following models:

| Model/Service | Dimensions | Context Length |
| --- | --- | --- |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 |
| [Cohere embed-multilingual-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 |
| [Cohere embed-multilingual-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 3072 | 8192 |
| [Mistral](https://mistral.ai/product/embeddings/) | 1024 | 8192 |
| [VoyageAI voyage-2](https://voyageai.com/) | 1024 | 4000 |
| [VoyageAI voyage-large-2](https://voyageai.com/) | 1536 | 16000 |
| [VoyageAI voyage-multilingual-2](https://voyageai.com/) | 1024 | 32000 |
| [Jina Colbert v2](https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/) | 128, 96, or 64 | 8192 |
| [OSS all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 |
| [OSS bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | 1024 | 512 |
| [OSS bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 1536 | 512 |

Factors to consider
-------------------

### 1\. Results relevancy

[Relevancy](https://www.meilisearch.com/docs/learn/relevancy/relevancy?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) is crucial for effective search, as it ensures that users find the most pertinent results quickly. In the realm of semantic search, achieving a balance between relevancy and speed is essential to provide a seamless user experience. It's important to consider the tradeoffs of [vector search vs full-text search](/blog/full-text-search-vs-vector-search/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison)

When selecting a model, consider your specific use case, such as the need for multilingual support, handling multi-modal data, or addressing domain-specific requirements. If you have a highly specialized use case or need to support a particular language, it may be beneficial to explore models that can be trained on your data or opt for multilingual models.

The performance difference between a very small model and a large model is not always substantial. **Smaller models are generally less expensive and faster**, making them a practical choice in many scenarios. Therefore, it is often worth considering smaller models for their cost-effectiveness and speed.

Additionally, you should always consider the context you're providing to the model. In Meilisearch, this comes in the form of a [document template](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#embedders-object). The more accurately the template describes the data, the better the search results will be, leading to a more satisfying user experience.

### 2\. Search performance

You read everywhere now. Time is money. And the web is no different. Nowadays, [search-as-you-type](https://www.meilisearch.com/docs/learn/getting_started/what_is_meilisearch?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#features) is the baseline for customer-facing applications. Saving users time greatly enhances their satisfaction and keeps them engaged with your platform.

To achieve lightning-fast search performance, **consider using a local model** to minimize latency by eliminating the need for round trips to the embedding service. If you need to use a remote model, then hosting your search service (e.g., your Meilisearch database) in close proximity to the embedding service can significantly reduce latency.

The table below showcases latency benchmarks for various local embedding models and embedding APIs. All requests are originated from a Meilisearch instance hosted on AWS (London datacenter.)

| Model/Service | Latency |
| --- | --- |
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | ±800ms |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | ±500ms |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | ±170ms |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | ±160ms |
| [Local gte-small](https://huggingface.co/sentence-transformers/gte-small) | ±20ms |
| [Local all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | ±10ms |
| [Local bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | ±20ms |
| [Local bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | ±60ms |
| [Mistral](https://mistral.ai/product/embeddings/) | ±200ms |
| [Jina colbert](https://huggingface.co/jinac/cobert-v1) | ±400ms |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | ±460ms |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | ±750ms |
| [VoyageAI voyage-2](https://voyageai.com/) | ±350ms |
| [VoyageAI voyage-large-2](https://voyageai.com/) | ±400ms |

Here you can see that there are some clear winners in terms of latency. Unfortunately, latency is not the same as throughput, so we also need to take a close look at the indexing time.

### 3\. Indexing performance

Indexing performance is another critical aspect when [comparing search solutions](https://www.meilisearch.com/docs/learn/resources/comparison_to_alternatives?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison). The embedding model performance will directly impact the indexing speed of your search solution. And the speed at which your data can be indexed directly impacts the overall efficiency and scalability of your search solution.

Local **models without GPUs may have slower indexing** due to limited processing power. In contrast, third-party services offer varying speeds and limitations based on their infrastructure and service agreements. It is essential to evaluate these factors to ensure that your chosen model and service can meet your requirements effectively.

Several factors come into play when optimizing indexing. Again, the latency plays a big role: reducing the time taken for data to travel between your application and the model is always going to improve your experience. Additionally, the **maximum size of API calls** the API accepts, the provider's **rate limiting**, and the model's supported **number of dimensions** can all influence the efficiency and scalability of the indexing process.

The benchmark below compares the indexing of a 10k e-commerce documents (with [automatic embedding generation](https://www.meilisearch.com/docs/learn/experimental/vector_search?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#generate-auto-embeddings)):

| Model/Service | Indexation Time |
| --- | --- |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 43s |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 16s |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 95s |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 151s |
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | 152s |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 159s |
| [Jina Colbert V2](https://huggingface.co/jinac/cobert-v1) | 375s |
| [VoyageAI voyage-large-2](https://voyageai.com/) | 409s |
| [Mistral](https://mistral.ai/product/embeddings/) | 409s |
| [Local all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 880s |
| [Local bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | 3379s |
| [Local bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 9132s |

### 4\. Pricing

While local embedders are free, most services charge per million of tokens. Here's a breakdown of the pricing for each platform:

| Provider      | Price                                      |
|---------------|--------------------------------------------|
| Cohere        | $0.10 per million tokens                   |
| OpenAI        | $0.13 per million tokens for text-embedding-3-large  |
|               | $0.02 per million tokens for text-embedding-3-small  |
| Cloudflare    | $0.011 per 1,000 Neurons                   |
| Jina          | $0.18 per million tokens                   |
| Mistral       | $0.10 per million tokens                   |
| VoyageAI      | $0.10 per million tokens for voyage-2      |
|               | $0.12 per million tokens for voyage-large-2 |
|               | $0.12 per million tokens for voyage-multilingual-2 |
| Local model   | Free                                       |


As your search needs grow and scale, it may become more cost-effective to invest in your own GPU machine. By having your own hardware, you can have greater control over the performance and scalability of your search solution and potentially reduce costs in the long run.

It is often best to start with a well-known model from the list provided. They are generally easy to setup and you will easily find community resources to help you.. As the need arises, you can consider migrating the model to a cloud provider like AWS. Many services offer this option, allowing you to leverage their infrastructure for improved performance and scalability.

Alternatively, you can choose an equivalent open-source model to self-host, giving you even more flexibility and control over your search solution in the long term. Please note that optimizing local models for performance or high volume may require to scale your infrastructure accordingly.

* * *

Ready to elevate your search experience?

[Talk to a search expert](https://dub.sh/meili-sales?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison)

* * *

Going further
-------------

While this article provides a comprehensive overview, we did not delve deeply into optimization techniques. There are several additional optimizations that can be explored to further enhance the performance of semantic search.

Here is a list of additional areas to investigate when choosing a model for your search experience:

*   Experiment with different presets (query vs. document) for models that offer this option to potentially improve relevancy
*   Evaluate specialized models for specific applications to assess their performance and suitability for your use case
*   Explore models that provide a reranking function to further refine search results
*   Test higher-tier accounts on each platform to check for improved performance and reduced rate limiting
*   Investigate parameters for receiving quantized data directly from the API to optimize data transfer and processing

Conclusion
----------

| Model/Service | Dimensions | Context Length | Latency | Indexation Time | Pricing (per million tokens) |
| --- | --- | --- | --- | --- | --- |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 | ±170ms | 43s | $0.10 |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 | ±160ms | 16s | $0.10 |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | ±460ms | 95s | $0.02 |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 3072 | 8192 | ±750ms | 151s | $0.13 |
| [Mistral](https://mistral.ai/product/embeddings/) | 1024 | 8192 | ±200ms | 409s | $0.10 |
| [VoyageAI voyage-2](https://voyageai.com/) | 1024 | 4000 | ±350ms | 330s | $0.10 |
| [VoyageAI voyage-large-2](https://voyageai.com/) | 1536 | 16000 | ±400ms | 409s | $0.12 |
| [Jina Colbert v2](https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/) | 128, 96, or 64 | 8192 | ±400ms | 375s | $0.18 |
| [OSS all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | ±10ms | 880s | Free |
| [OSS bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | 1024 | 512 | ±20ms | 3379s | Free |
| [OSS bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 1536 | 512 | ±60ms | 9132s | Free |

Choosing the right model and service for semantic search involves carefully balancing several key factors: relevancy, search performance, indexation performance, and cost.

Each option presents its own set of trade-offs:

*   Cloud-based services like Cohere and OpenAI offer excellent relevancy and reasonable latency, with Cohere's embed-english-light-v3.0 standing out for its balance of speed and performance.
*   Local models provide the fastest search latency but may struggle with indexation speed on limited hardware.
*   Emerging services like Mistral and VoyageAI show promise with competitive pricing and performance.
*   Open-source models offer cost-effective solutions for those willing to manage their own infrastructure.

Ultimately, the best choice depends on your specific use case, budget, and performance requirements. For many applications, starting with a cloud-based service like Cohere or OpenAI provides a good balance of ease of use, performance, and cost. As your needs grow, consider exploring local or specialized models, or contact Meilisearch's sales team for tailored solutions.

* * *

[Meilisearch](https://www.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) 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/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) or subscribe to the [newsletter](https://dub.sh/meili-newsletter?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison). You can learn more about the product by checking out its [roadmap](https://roadmap.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) and participating in [product discussions](https://github.com/orgs/meilisearch/discussions).

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