Algolia vs Klevu vs Expertrec: Feature Comparison

Exact capabilities and limits depend on the product tier and implementation.

Algolia: API-First Search Infrastructure

Algolia is constructed according to an API First architectural approach. All product and content data gets indexed and queried by means of API and SDKs, providing developers with high degrees of freedom in implementing search.

Algolia also provides such features as autocomplete, typo tolerance, synonyms, ranking rules, analytics, etc.

The technical advantage of the API First approach is flexibility, which enables a developer to decide upon the searchable attributes, ranking parameters, filtering logic, etc.

For instance, the product record would include:

• Product ID
• Title
• SKU
• Brand
• Category
• Description
• Tags
• Price
• Colour
• Size
• Availability

Search applications can find specific importance measures of each attribute used in the product record.

Nevertheless, the total costs of the implementation should not be limited to subscription fees alone. Development costs associated with pipeline indexing, frontend integration, ranking configuration, analytics, etc., are also included in the total cost of ownership of any search platform.

This makes Algolia especially interesting to companies with their own engineering teams that are willing to have a good deal of control over the search infrastructure.

Klevu: Ecommerce Search and Merchandising

Klevu’s perspective on search revolves predominantly around e-commerce discovery and merchandising.

In addition to search, it also offers functionalities related to product recommendation, category merchandising, personalisation, and search analytics.

Take a fashion retailer with:

• 50,000 SKUs
• 20 product categories
• ₹250 million in annual online revenue
• Frequent seasonal marketing efforts

A search term like “winter jackets” should not be restricted to products containing that term; they also want to have parameters such as stock, popularity, relevance, and promo policy to affect product listings.

This is why merchandising comes into play.

The search function relies on metrics such as search volume, CTR, product clicks, and sales in order to identify areas for improvement in the current search performance.

Expertrec: Search without Building the Entire Search Layer

Expertrec provides ecommerce search solutions along with a greater focus on minimizing the search resources businesses need to develop and maintain.

Its features include semantic searches, autocomplete capabilities, spelling correction, synonyms, search weights, filtering, merchandising, recommendations, and analytics.

Of particular note is Expertrec’s search field management.

The e-commerce catalog may provide data in different forms, including titles, SKUs, tags, collections, products, variants, and metafields. Treating all these fields equally can lead to low relevance.

Consider this query:

“Nike Air Max 270 black 9”

The e-commerce search should be able to extract the following data from this query:

• Brand: Nike
• Product: Air Max 270
• Color: Black
• Size: 9

This is not just about checking if the whole query exists in one of the product descriptions.

Hence, it is especially advantageous for Shopify stores where some of the important product data could be hidden in titles, SKUs, tags, collections, variants, and metafields.

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Why Search Relevance Depends on More than Keywords

User Query → Query Understanding → Candidate Retrieval → Relevance Ranking → Business Rules → Personalization → Results

For instance, if a user wants to search for:

“office chair under ₹10,000 with lumbar support,”

The search engine may return office chairs using a simple keyword search.

However, a more sophisticated solution would yield:

• Product = office chair
• Price < ₹10,000
• Feature = lumbar support

This is where semantic retrieval, structured attributes, faceted filtering, and ranking signals come into play.

Another issue is the use of synonyms. In some cases, a shopper may search for “sofa,” but the catalogue states “couch.” Without coming up with the right match, a search engine may return irrelevant results.

Lastly, searches for SKU or model numbers pose yet another technical problem. For queries like “SM-A556E” and “iPhone 15 128GB,” it is essential to match the right fields of a structured catalogue, instead of relying solely on the natural-language search results.

Search Analytics: The Missing Feedback Loop

Search optimization is a never-ending journey.

A good example of an ecommerce search funnel is this:

Search Volume → Zero-Result Rate → CTR → Add-to-Cart Rate → Conversion Rate → Search Revenue

Let’s say there’s an online shop that receives 200k searches a month and shows an 8% zero-result rate.

This means that roughly:

16k searches/month have no results.

This information can be broken down by query, device, category, location, product availability, and conversion behavior.

The high-volume search query that returns no results might signal:

• Lack of synonyms
• Incorrect product indexing
• Poor attribute mapping
• SKU mismatch
• Out-of-stock products
• Spelling mistakes
• Missing catalogue data

All this information can be utilized for changing the ranking rules, the synonyms used, searchable fields, or even the catalogue structure.

Expertrec offers search analytics and experimentation features that make it possible to complete this cycle of optimization. There are also Klevu and Algolia, providing analytics solutions, although their functionality differs depending on their implementation and plan.

What Does Search Cost?

The price should take into consideration the volume of searches, the size of the catalog, and the implementation effort instead of simply comparing subscription prices.

As an illustration, let’s assume that an e-commerce business makes 500,000 searches each month with a catalog of 100,000 products.

The relevant consideration should be:

Cost of the platform + cost of using the search + implementation and engineering costs + maintenance costs.

In case improved search offered a boost in revenue from ₹20 lakh to ₹23 lakh, the additional ₹3 lakh should also be included in the overall cost of deploying search.

Add to that the fact that businesses in India can convert subscription and usage costs into INR which makes it easier to make comparisons.

Which Platform Should You Evaluate?

Algolia, Klevu, and Expertrec all deal with similar issues, but address them through different technical means.

Algolia offers a powerful API-oriented search framework which places great emphasis on developer independence.

Klevu focuses on different aspects of ecommerce, which mainly include discovery, merchandising, and personalization.

Expertrec adds AI and semantic search technologies to the mix and introduces special ecommerce-related parameters such as searchable fields, ranking, types of promotions, and reports. It can work with many platforms, including Shopify and headless systems.

Financial leaders ought to focus not on the list of features this or that platform has but ask:

1. What is the monthly search volume?
2. What is the current zero-result percentage?
3. Do customers use SKU or model number for searching?
4. Will the company require merchandising policies?
5. Can search analytics be linked to business sales?

Final Takeaway

The gap between e-commerce search solutions is increasingly found in the technological background of the search process.

For an effective search process, more than just simple keyword matching is required. Correct indexing, query understanding, semantic retrieval, relevance ranking, filtering by various criteria, merchandising, and continuous analysis—all these processes are necessary for a high-quality search experience.

When comparing Algolia with Klevu and Expertrec, one must start with such criteria as catalogue structure, search behavior, and the company’s business goals.