When Ecommerce Catalogs Need OpenSearch or Elasticsearch

Why Your Search Bar Is Quietly Killing Conversions

Most e-commerce teams ignore anything that isn’t advertising, checkout flow, or product photography, and this negligence impacts the customer experience. Site search often fails to offer results for the thousands of shoppers who utilize the feature. An example of this is when a customer searches for “waterproof trail runners size 10 wide” and receives no relevant results or unrelated ones; that customer is very likely to leave the site. That means a lost sale that is likely never to interact with that site again.

Optimizing ecommerce catalog search becomes increasingly complex as the product catalog grows. With a few hundred SKUs, an unoptimized site search might be able to rely on a simple database query for a while, but with tens of thousands of SKUs spanning multiple categories and exhibiting sharp regional differences, a more scalable solution is warranted. This is where OpenSearch and Elasticsearch become relevant.

This guide provides an overview of what to consider when choosing a search hosting solution and how relevant e-commerce catalogs are to OpenSearch and Elasticsearch.

What Makes Ecommerce Catalog Search Fundamentally Different

A search engine used to find a blog or news article is not as precise as the one used in e-commerce. If a user is looking for a news article, they will be more patient and will scroll through the pages, maybe using a few more search synonyms. Ecommerce customers will not be as patient; they want accurate search results. They also want the search filters to work.

This means e-commerce searches must handle many complex tasks. They must be able to return search results for ‘trainers’ or ‘sneakers’ and filter by size, color, or inventory. They must also be able to rank results by search relevance and business rules and answer the query. Most search plugins or SQL queries were not designed to handle such complex queries and will degrade as the catalog or database scales.

Ecommerce customers will not return when e-commerce search is irrelevant or slow. The search failure results in revenue loss. The Baymard Institute has reported that 70% of e-commerce sites lack proper on-site search. For e-commerce sites that decide to build proper infrastructure, this is a large competitive opportunity.

OpenSearch vs Elasticsearch: Understanding the Difference

Elasticsearch

Elasticsearch is a RESTful, distributed search and analytics engine built on top of Apache Lucene. Elastic developed it, and it has been the leading enterprise search engine for about ten years. It is used to perform searches in companies like GitHub, Netflix, and even Walmart. For e-commerce, it offers deep relevance tuning, ML-based rankers, and a comprehensive ecosystem for integration.

Elasticsearch has an index-based architecture and stores its data as JSON documents. It can perform full-text searches, fuzzy matching, geo-distance queries, and complex aggregations, all of which are essential for large-scale e-commerce catalogs. The paid tiers include features such as Elastic Learned Sparse EncodeR (ELSER), which enables semantic search and vector similarity and is important for the AI-enhanced search-oriented e-commerce product experience.

OpenSearch

OpenSearch is Apache 2.0 licensed, community-driven, and based on an Amazon Web Services and Linux Foundation-maintained fork of Elasticsearch 7.10. It was developed after Elastic changed its licensing terms in 2021 and grew rapidly after its launch. As of mid 2026, development of OpenSearch 3.7.0 is current and there is continued functionality to both the 3.x and 2.x branches.

For e-commerce teams with AWS hosting, OpenSearch integrates with other AWS services, including Amazon S3, CloudWatch, and the OpenSearch Service (a managed AWS service). It fully supports the Elasticsearch core search functionalities (i.e. relevance scoring, faceted search, synonyms, and nested documents) and is completely open source with no licensing costs beyond your infrastructure.

OpenSearch and Elasticsearch share a common API, so much of the documentation, tools, and community will be applicable to both. The main deciding factors in your choice will probably be where and how you can host each option, the cost, and which of the two your engineering and development team is accustomed to.

The Real Question: When Does Your Catalog Actually Need This?

An e-commerce business doesn’t necessarily need OpenSearch or Elasticsearch. For smaller product catalogs, a Shopify search would suffice. WooCommerce would need a search plugin, but it can cater to mid-tier stores fairly well too. However, there are clear points that indicate when OpenSearch or Elasticsearch would be more appropriate.

Catalog Size and Complexity

When your catalog reaches 10,000 to 50,000 SKUs, especially with more complex attributes, standard search systems start to struggle to deliver the right answers quickly. Consider a furniture retailer. Searching for products differentiated by material, dimensions, finish, style, and room type is nearly impossible with a standard search tool. This is because the problem is not with the search tool, but rather with distributed search.

Traffic and Concurrency

Basic search systems break down during peak traffic events. A Black Friday sale or a flash sale will cause slow responses, timeouts, and abandoned sessions. This will negatively affect the conversion rate just when it matters the most. Search infrastructures built on OpenSearch and Elasticsearch can handle horizontal scaling and distribution of query load as nodes are added.

Personalization and Ranking Control

Today, e-commerce searches need to understand not only what a customer wants, but what a customer wants right now. Both platforms offer custom scoring, boosting rules, and behavior-based result ranking and machine learning model integration. If you need to promote certain customer segments while boosting available items, prioritizing high-margin items, or highlighting the latest in-demand SKUs, you’re going to need a search engine with that type of customization. Stock plugins won’t meet your needs.

Multilingual and Multichannel Catalogs

Operating across multiple markets poses language-based search challenges for brands. Both Elasticsearch and OpenSearch provide language analyzers for multiple languages. You can set up tokenization and stemming and configure stop-word filtering for various languages. If you have a unified catalog across your English, Spanish, French, and German storefronts, you must have this infrastructure.

Ecommerce Search Hosting: Managed vs. Self-Hosted

Once you’ve decided that your catalog needs OpenSearch or Elasticsearch, the next decision is how to host it. This is where e-commerce search hosting strategy matters significantly.

Managed Cloud Services

Amazon OpenSearch Service allows users to customize specific features. For e-commerce developers lacking DevOps skills, this offers a significant reduction of business running overhead. Elastic Cloud offers a similar service for Elasticsearch. Hosted services also provide analytics and machine learning services, but at an additional pricing tier.

Managed services are more expensive per compute hour than self-hosted services, but provide significant savings in overhead due to cluster management. For most e-commerce businesses, this trade-off yields greater savings with managed services.

Self-Hosted on Cloud Infrastructure

Some larger organizations choose to deploy OpenSearch or Elasticsearch on dedicated EC2 instances, GCP VMs, or even physical servers. This enables greater specification of cluster configuration, data storage locations, and overall cost efficiency as data storage grows. This is a justified choice when a company has specific needs for data storage and accessibility due to compliance requirements or other mandates, a unique data structure, or unfavorable pricing for managed services given the data traffic.

Hosting the service self-managed means a company has to handle all upgrades itself. This can lead to high operational costs as OpenSearch 1.x ended support in May 2025 and required careful planning to facilitate the upgrades. OpenSearch has published its latest compatibility and upgrade documentation, which should be reviewed before committing to the self-hosted upgrade service.

Key Features to Prioritize for Ecommerce Catalog Search

Relevance Tuning and Synonyms

A search engine that offers technically accurate, yet commercially meaningless results is not beneficial to your business. Your platforms have the same capabilities in that you can create synonym dictionaries, set field boosting, and write custom scoring functions. “Sofa” should be equivalent to “couch.” “TV” should be equivalent to “television” or “flat screen.” Offering these types of searching options is the minimum expected level of service for a product search engine.

Faceted Navigation and Aggregations

Efficient aggregation queries enable filtering results by price range, brand, color, size, rating, availability, etc., while keeping response times fast. OpenSearch and Elasticsearch handle this through their aggregation frameworks, which are capable of near-real-time processing on massive document sets. This is what makes the difference between a smooth filtering experience and a frustrating one.

Vector Search and Semantic Matching

Keyword-based searching is not efficient. Not every customer uses your classification to name a product. Both platforms are beginning to support vector search. OpenSearch’s k-NN plugin and semantic search model in Elasticsearch allow users to develop semantic search to some extent. Given that almost 60% of customers utilize AI during their shopping experience, it creates a competitive advantage.

Measuring the Impact of Search Infrastructure

After deploying your own search engine, you’ll want to evaluate its performance. Focus on the following metrics: search click-through rate, zero-result rate, search add-to-cart rate, and revenue from the search channel. Kibana, OpenSearch Dashboards, and your e-commerce analytics platform will help you here. If you want advice on how to translate your search engine investment into measurable results, we’d recommend Neil Patel’s e-commerce SEO guide, as he touches on the conversion aspect nicely.

Conclusion

Your e-commerce catalog’s search capability is not a feature — it’s infrastructure. As catalogs grow in size and complexity, the gap between basic search plugins and purpose-built distributed engines widens into a revenue gap. OpenSearch and Elasticsearch exist precisely to close that gap, offering the relevance control, scalability, and flexibility that large-scale product discovery demands.

The right choice between them depends on your cloud environment, budget, licensing preferences, and engineering capacity. What is clear is that waiting until search becomes visibly broken is the wrong trigger. The businesses gaining ground on search in 2026 are the ones that treated it as a strategic investment before they felt the pain.

Frequently Asked Questions

What is the difference between OpenSearch and Elasticsearch for e-commerce?
Both are distributed search engines built on Apache Lucene and share a similar API. Elasticsearch is a commercial product with premium ML features, while OpenSearch is fully open source under the Apache 2.0 license. For e-commerce teams on AWS, OpenSearch integrates naturally with the existing cloud ecosystem. The core search capabilities—full-text search, faceting, and relevance tuning—are comparable among them.

When should an e-commerce store switch from Shopify search to OpenSearch or Elasticsearch?
The inflection point is typically reached when catalog size exceeds 10,000 to 50,000 SKUs, when peak traffic causes search latency, or when you need advanced relevance tuning and personalization that hosted-platform search tools can’t support. If your zero-result rate is high or shoppers frequently abandon searches, that’s a signal that your current infrastructure is undersized.

What does e-commerce search hosting typically cost?
Costs vary significantly by scale and hosting model. Amazon OpenSearch Service pricing is based on instance type, storage, and data transfer. Small clusters for mid-sized catalogs can cost a few hundred dollars per month, while enterprise-scale deployments can reach several thousand dollars per month. Self-hosted configurations on cloud VMs offer more pricing flexibility but require engineering resources to manage.

Is OpenSearch still actively maintained in 2026?
Yes. As of June 2026, OpenSearch is at version 3.7.0, with active development ongoing. Both the 3.x and 2.x branches receive ongoing maintenance. OpenSearch 1.x reached end of life in May 2025 and should no longer be used in production environments.