Table of Contents
Modern search solutions are more than just searches by the identified keywords. In the modern world, businesses (content or eCommerce) expect modern search technologies like misspelled word recognition, the identification of synonyms, semantic search, and other features.
Algolia, Azure AI Search, and ExpertRec provide modern solutions such as these. Nevertheless, although they are similar, the way they provide these services is very different.
When we refer to Algolia, we’re talking about the search engine that is popularly used; Azure AI Search is self-explanatory, as this is a cloud search platform that is designed for developers who want to create tailored search solutions – either by keywords or semantics; ExpertRec is a search solution for eCommerce sites.
However, the most important thing to clarify is the general functionality of the available solutions as well as the requirements of the company, as the standard of ‘how much search process the company wants to implement’ may vary greatly from company to company.
Algolia vs Azure AI Search vs ExpertRec
The architectural distinction is important: Azure AI Search provides a highly configurable search foundation, whereas Algolia and ExpertRec provide more of the search experience layer around that infrastructure.
Search Architecture and Indexing
An e-commerce item could include the following:
Product Name – Samsung Galaxy S25
SKU – SM-S25-256-BLK
Category – Smartphones
Brand Name – Samsung
Color – Black
Storage Space – 256GB
Cost – ?74,999
A search system organizes the stored information into an index made up of searchable fields and filterable properties, along with ranking signals and possible vector representations.
Algolia
With Algolia, the index is managed so developers simply send content/product records and set searchable fields, ranking criteria, synonyms, policies, etc., which simplifies search infrastructure management but still means that the teams have to manage the data transfer process.
Azure AI Search
Azure AI Search makes it possible to control the index structure. The developers can outline:
- fields to be searched
- fields available for filtering
- fields available for sorting
- fields suitable for faceting
- fields available for vectoring
- semantic configurations
- scoring profiles
- synonym dictionaries
This makes Azure AI Search perfect for those interested in building custom search systems and can expect no less than good engineering involvement.
ExpertRec
The ExpertRec system is geared toward ecommerce websites and their deployment. As such, it can integrate product feeds and utilize website data, as well as ecommerce platforms such as Shopify, WooCommerce, Magento/Adobe Commerce, and BigCommerce.
In terms of ecommerce catalogs, fields like SKU, product attributes, categories, tags, and metafields can enrich the search experience.
This feature is especially beneficial for cases where search queries are entered using identifiers instead of product names, for example:
“SM-S25-256-BLK”
Keyword, Semantic and Vector Search
Lexical search is effective when the terms in the query coincide with the exact terms in the index.
For instance, consider the phrase “wireless bluetooth headphones”. A consumer, however, may search for “headphones for travelling without wires.” Semantic search helps you understand the connection between the two queries, even though the words used are different.
Azure AI Search
Azure AI Search allows for hybrid search combining lexical searching with vector retrieval. Thus, results can be merged and semantically revised.
The general scheme is as follows:
This scheme is particularly important in the domains of RAG, corporate search, and custom AI systems.
Algolia
The company provides businesses with AI-based search technology that allows them to implement AI-driven query comprehension and ranking without creating retrieval components on their own.
ExpertRec
ExpertRec takes traditional search to new heights with such AI features as intent recognition, generative AI answering, visual search, and AI-based product discovery.
A search engine for e-commerce should understand:
- Category → Red running shoes
- Attribute → Red color
- The condition of price → ≤ ₹5,000
This requires more than just similarity of vectors.
AI-powered search for modern eCommerce
Build smarter ecommerce search with ExpertRec
ExpertRec provides a managed search experience for ecommerce stores with features such as AI-powered product discovery, SKU and metafield search, autocomplete, typo tolerance, filters, faceted navigation, merchandising, recommendations, and search analytics.
Shopify • WooCommerce • Magento • BigCommerce • API & Headless
Relevance Ranking and Merchandising
Searching for relevant information is not just about artificial intelligence.
Retailers have their reasons why they want their products to show high on the search list, including:
- Stock status
- Discounts
- Margins
- Seasonal organizations
- Popularity level
- Conversion ratio
Algolia offers some ranking and merchandising capabilities.
Azure AI Search provides ranking and scoring capabilities aimed at developers, but in order to employ business-oriented merchandising processes, some complexities arise.
ExpertRec has great merchandising features like fixing certain products, rules, promotional labels, visual merchandising, etc.
For instance, a merchant may organize search results as:
This shows that search is more than just a function of retrieval.
Ecommerce Search
The features of e-commerce search typically include:
- SKU search
- Autocomplete
- Typos.
- Synonyms.
- Product filtering.
- Faceted navigation.
- Attribute search.
- Rating.
- Merchandising.
- Recommendation.
- Analytics.
This is where an e-commerce-specific platform helps in simplifying implementation.
For instance, let’s suppose the e-commerce business is making ₹1.2 crore of online revenue per year, and approximately ₹10 lakhs of revenue is attributed to searches on a monthly basis.
With a hypothetical 5% increase due to search optimization.
The number is just for reference, but it shows why conversion and revenue factors must be used to measure search performance rather than just measuring search speed.
Search Analytics and Optimization
The search system in production must be evaluated all the time regarding:
- Rate of clicking search results;
- Rate of converting search requests;
- Search queries that ended up with zero results;
- Search results which are not used;
- Reformulating search queries;
- Connecting products to a cart;
- Revenue generated by every search enquiry;
- Rate of the queries that are clicked.
ExpertRec provides analytics and experimentation options for search to find the bad queries and tweak the search configurations.
Algolia also provides analytics and optimization options, depending on the plan.
When using Azure AI Search, for analytics purposes, companies will usually develop around the search service using application telemetry and other Azure or third-party systems.
As a result, three different operating models appear:
- Azure AI Search – build your own stack for search;
- Algolia – order the search infrastructure and set up relevancy;
- ExpertRec – make use of managed search for a website/ecommerce site with business-like optimizations.
Integration and Implementation
Azure AI Search is ideal for development teams that create customized applications with tightly integrated Azure architecture and services.
Algolia provides APIs and SDKs for application-level search implementation.
ExpertRec puts emphasis on quicker deployment for existing websites, eCommerce platforms, Shopify, WooCommerce, Magento/Adobe Commerce, and BigCommerce, among others, with API implementation and headless solutions.
This changes the question faced by engineering teams from:
To:
For smaller eCommerce teams, it is important to minimize the infrastructure and custom search logic they have to manage.
Cost Considerations
When it comes to search costs, one should avoid looking at subscription price only.
A better model for calculating the search cost would be as follows:
As an example, the total search costs for the business could amount to ₹1,00,000/month:
- Platform: ₹30,000
- Engineering: ₹35,000
- Infrastructure: ₹15,000
- Analytics and optimization: ₹10,000
- AI/embedding costs: ₹10,000
Thus, the total cost would equal ₹1,00,000.
The numbers here are for illustration purposes only and are not vendor prices.
Algolia minimizes infrastructure management but still has to address issues of integration and data synchronization. ExpertRec, on the contrary, aims at delivering as many e-commerce search experiences as a managed service.
Use-Case Comparison
Algolia
- It includes a sophisticated degree of relevance
- Search APIs are available
- The platform includes AI Search improvements.
- It includes merchandising
Azure AI Search
- It is possible to use vector and hybrid retrieval
- Semantic ranking is included
- Enterprise Knowledge Search should be performed
- Very high-level control for developers is needed.
ExpertRec
You may consider using ExpertRec when:
- It should have filters and faceted navigation
- It should possess merchandising capabilities
- It is required to have analytics functionality
- Product discovery based on AI is needed
- Recommendations are needed
- It is necessary to construct a simple search infrastructure.
Conclusion
While Algolia, Azure AI Search, and ExpertRec provide overlapping solutions to search challenges, these platforms are available in different models.
Algolia provides managed services for search and discovery.
Azure AI Search serves as a foundation for advanced text, vector, hybrid, and semantic systems, providing developers the utmost control.
ExpertRec focuses on delivering a solution that includes the whole spectrum of functionalities, from search and AI to filtering and analytics, thereby making this platform usable as a website or e-commerce solution.
Companies that consider all three solutions should take account of several factors: size of the catalog, number of queries, complexity of search, need for AI technologies, availability of engineering team, requirements for merchandising functionalities, integration efforts, and total cost of the solution.
The question is not simply:
But:
