Amazon Kendra Vs. Elasticsearch Vs. OpenSearch

Amazon Kendra Vs. Elasticsearch Vs. OpenSearch: Key Use Cases and Pros & Cons

Amazon Kendra vs. Elasticsearch vs. OpenSearch is a tricky comparison due to a huge number of similarities between the three search services. All of them are known for their ability to improve search experiences and enhance customer satisfaction by facilitating multiple types of searches.

However, some noteworthy differences pop up when you go through their use cases. It shows that the given search services fulfill different purposes. If one of them is good for a certain purpose, the others have their own unique purpose and benefits as well.

Thus, we have created this article to highlight the same and inform you about the pros and cons of the mentioned search services. Give it a read, and you’ll surely gain clarity around the stated comparison.

Amazon Kendra Vs. Elasticsearch Vs. OpenSearch: Overview

Being similar yet different from each other, Amazon Kendra, Elasticsearch, and OpenSearch have their own identities.

What Is Amazon Kendra?

Amazon Kendra is a robust, intelligent, and flexible enterprise search service that uses advanced machine learning algorithms and natural language processing to return specific answers to your queries based on your data. Your experience with using Amazon Kendra will be quite similar to interacting with a human expert.

It supports the following types of queries:

Descriptive or detailed questions
Keyword or natural language questions
Simple what, who, where, and when questions

What Is Elasticsearch?

ElasticSearch is an open-source distributed, RESTful search and analytics engine, vector database, and scalable data store. It can address a growing number of use cases. Being an important part of the Elastic Stack, it centrally stores your data for instant search and powerful analytics.

Here are the search types Elasticsearch supports:

Piped Query Language
Unstructured
Structured
Full-text
Metric
Geo

What Is OpenSearch?

OpenSearch is a distributed, Apache 2.0-licensed, community-driven, 100% open-source search and analytics suite used for a variety of use cases such as website search, log analytics, and real-time application monitoring. It offers quick access and response to large volumes of data by using an integrated visualization tool, OpenSearch Dashboards.

The given search service has multiple search and analytics capabilities that aren’t limited to:

SQL
Trace Analytics
Full-text Search
Anomaly Detection
Machine Learning Commons
k-nearest neighbor (kNN) search

What Is the Difference Between Amazon Kendra, Elasticsearch, and OpenSearch?

The primary difference between Amazon Kendra, Elasticsearch, and OpenSearch lies in their use cases. Amazon Kendra is mostly used for enterprise search, while Elasticsearch is widely used for log and event data analysis. However, OpenSearch is used when people prefer an open-source search service.

Skipping the use cases that are common among the given search services, the following sections focus on the contrasting use cases only.

Enterprise Search

Source Credit: Amazon Kendra

After carefully comparing Amazon Kendra vs. Elasticsearch vs. OpenSearch, it’s believed that Amazon Kendra is the best choice when it comes to performing enterprise searches effortlessly. Moreover, it lowers the Total Cost of Ownership by 75% in one year as compared to traditional enterprise search tools.

Say that you are looking for precise, AI-powered results for your natural language queries. Here, you should use Amazon Kendra.

Although not like Amazon Kendra, Elasticsearch and OpenSearch can also facilitate enterprise search. All you need is more configuration and tuning in the case of Elasticsearch and OpenSearch.

Log and Event Data Analysis

Source Credit: OpenSearch

If you want to perform log and event data analysis, you can go for Elasticsearch or OpenSearch without thinking for a second. Both of these search services have the ability to handle large volumes of data with quick search capabilities.

You’d be amazed to know that Elastic Observability, a highly popular and one of the most deployed log management and search tools is built on Elasticsearch. It offers flexible and powerful log analytics and log management.

Contrary to these, Amazon Kendra won’t prove to be the best option for log and event data analysis because it’s not primarily designed for it.

Natural Language Processing (NLP)

Amazon Kendra wins in the Amazon Kendra vs. Elasticsearch vs. OpenSearch comparison when you talk about Natural Language Processing (NLP). This is because Amazon Kendra comes with built-in Machine Learning (ML) models that understand user intent and deliver relevant answers no matter how complex a natural language query you provide it with. You don’t even need to have Machine Learning (ML) expertise when using Amazon Kendra.

However, it doesn’t mean that Elasticsearch and OpenSearch won’t support NLP. It’s just that Elasticsearch requires additional integrations. It integrates with one of the most famous formats for building NLP models in PyTorch models to offer a platform that works with a wide variety of NLP use cases and tasks.

On the other hand, in OpenSearch, you must set up an ML model before using neural search. You’ll have the following options:

Use one of the OpenSearch-provided pre-trained models.
Upload a custom local model to OpenSearch.
Connect to an externally hosted foundation model.

After setting up an ML model, you should choose conversational search to ask questions in natural language and filter out the answers by asking clearer questions. That’s how you can qualify OpenSearch for NLP.

E-commerce Search

Elasticsearch and OpenSearch perform e-commerce searches quickly and accurately. Hence, the said services are widely used in e-commerce websites or search and product recommendation engines. Furthermore, OpenSearch offers more flexibility through its open-source contributions.

So, both of the said services enhance customer experience by helping the consumers find exactly what they’re looking for in no time. Moreover, they’ll allow you to display popular and relevant items and save location, etc., based on multiple factors when you link search queries to user profiles and real-time customer data.

Unlike these, Amazon Kendra isn’t designed for e-commerce searches and doesn’t help any more than finding out the products for you.

Security Information and Event Management (SIEM)

Source Credit: Elastic.co

Security Information and Event Management (SIEM) solutions often use Elasticsearch and OpenSearch. Here, Elasticsearch quickly processes, indexes, and analyzes security logs, and OpenSearch offers Security Analytics to help you find, look into, and respond to threats in real time.

However, if you ask about Amazon Kendra, then know that it doesn’t focus on SIEM as part of its key use cases.

Aggregate and Analyze Large Datasets

Considering the aggregation and analysis of large datasets, the Amazon Kendra vs. Elasticsearch vs. OpenSearch comparison gives results in favour of Elasticsearch and OpenSearch. Aggregating and analyzing large datasets becomes a lot easier while using Elasticsearch and OpenSearch as compared to Amazon Kendra.

So, it won’t be a wrong step to skip Amazon Kendra when you need to deal with large datasets.

Piped Query Language Search

Do you prefer giving multiple commands separated by the pipe “|” symbol instead of entering commands one after the other? If yes, then Elasticsearch and OpenSearch would be suitable choices for you because Amazon Kendra doesn’t support piped query language search.

In case you don’t know, the new Elasticsearch Query Language (ES|QL) is a single unified language and method to interact with data in Elasticsearch.

Here is an example of an ES|QL command:

Also, please refer to the following image to see how you can start using ES|QL in Kibana:

Source Credits: Elastic.co

Furthermore, here is a sample Piped Processing Language (PPL) command you can tweak and try executing in OpenSearch:

search source=accounts
| where age > 20
| fields firstname, address

Amazon Kendra Vs. Elasticsearch Vs. OpenSearch: Compare the Use Cases

Use Cases	Amazon Kendra	Elasticsearch	OpenSearch
Improve search experiences	Yes	Yes	Yes
Enhance customer interactions and satisfaction	Yes	Yes	Yes
Natural Language Processing (NLP)	Yes	Yes, but with the help of additional configurations or integrations.	Yes, but it requires external NLP tools.
Full-text search	Yes	Yes	Yes,
Enterprise search	Yes	Yes, but it requires extra configuration.	Yes, but it needs extra configuration.
Distributed search	Yes	Yes	Yes
Piped query language search	No	Yes	Yes
Integrate search functionality into your SaaS applications	Yes	Yes	Yes
Implement multi-tenancy and get rid of fraud and risk	Yes	Yes	Yes
Semantic search	Yes	Yes	Yes
Application search	Yes	Yes	Yes
E-commerce search	No	Yes	Yes
Log and event data monitoring	No	Yes	Yes
Aggregate and analyze large datasets	No	Yes	Yes
Security Information and Event Management (SIEM)	No	Yes	Yes

Amazon Kendra Vs. Elasticsearch Vs. OpenSearch: Pros & Cons

Turning the spotlight towards the pros and cons of Amazon Kendra, Elasticsearch, and OpenSearch, here is the table that states them one after the other. Go through it to figure out what each service helps you gain, and miss out.

Pros	Cons
Amazon Kendra
ML-powered search functionality for all unstructured data that you have stored in AWS.	It’s expensive.
A simple search API.	The Kendra index will take sufficient time to sync when you add or remove documents.
Easy and quick setup.
Easy connectivity with third-party data repositories or data sources.
Accurate and relevant answers to your queries.
Support for multilingual documents.
Support for different types of documents.
An incredibly secure enterprise search experience.
Elasticsearch
Fast speed.	It can be a bit tricky to size and scale on-premise installations.
Scalability.	If not planned well, Elastic agents can be resource-intensive.
User-friendly User Interface (UI).	A fairly steep learning curve.
Real-time search capabilities.	Complicated upgrades between major versions.
Failure detection.
Cross-cluster replication.
Affordability.
OpenSearch
Real-time security threat detection and response execution.	It requires more descriptive documentation.
Highly scalable.	A bit old User Interface (UI).
Custom pipelines for data ingestion and enrichment.	Some functionalities might not work perfectly.
Offers a quick response time, but it’s slower than Elasticsearch.
Integrates seamlessly with AWS.

Conclusion

Amazon Kendra vs. Elasticsearch vs. OpenSearch isn’t a complicated discussion anymore. Being among the best search services, these services are designed with different purposes in mind and come with their unique pros and cons. Now, here are the three important takeaways from this post:

Amazon Kendra goes well with NLP and enterprise search.
Elasticsearch and OpenSearch complement e-commerce websites and SIEM solutions.
Although Elasticsearch and OpeSearch have quite common use cases, if you consider speed, then Elasticsearch wins over OpenSearch.