Amazon Kendra vs Azure AI Search comparison

Amazon Web Services (AWS) and Microsoft are both solutions in the Search as a Service category. Amazon Web Services (AWS) is ranked #5, while Microsoft is ranked #4 with an average rating of 7.0. Amazon Web Services (AWS) holds a 6.7% mindshare in SS, compared to Microsoft’s 9.0% mindshare. Additionally, 100% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 66% of Microsoft users who would recommend it.

Amazon Kendra

Read 2 Amazon Kendra reviews

1,226 Views
1,226 Comparison Views

100% willing to recommend

Azure AI Search

Read 9 Azure AI Search reviews

1,110 Views
1,110 Comparison Views

66% willing to recommend

Amazon Kendra

Azure AI Search

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Executive SummaryUpdated on Nov 2, 2025

Azure AI Search and Amazon Kendra compete in the AI-powered search services domain. Amazon Kendra appears to have the upper hand due to its comprehensive features and ease of deployment.

Features: Azure AI Search includes cognitive search capabilities, enriched search experiences, and built-in AI skills. Amazon Kendra delivers precise answers through natural language processing, customizable relevance tuning, and integration with various data sources.

Ease of Deployment and Customer Service: Amazon Kendra offers a streamlined deployment process with strong support options. Azure AI Search requires more initial configuration but provides flexible deployment and comprehensive support.

Pricing and ROI: Azure AI Search is known for competitive pricing, resulting in lower setup costs and faster ROI. Amazon Kendra may have higher initial expenses but offers significant ROI through its advanced functionalities and user satisfaction.

To learn more, read our detailed Amazon Kendra vs. Azure AI Search Report (Updated: December 2025).

Buyer's Guide

Amazon Kendra vs. Azure AI Search

December 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Amazon Kendra

Ranking in Search as a Service

5th

Average Rating

7.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Azure AI Search

Ranking in Search as a Service

4th

Average Rating

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Search as a Service category, the mindshare of Amazon Kendra is 6.7%, down from 18.4% compared to the previous year. The mindshare of Azure AI Search is 9.0%, down from 14.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Search as a Service Market Share Distribution
Product	Market Share (%)
Azure AI Search	9.0%
Amazon Kendra	6.7%
Other	84.3%

Search as a Service

Featured Reviews

Ashutosh Moondra

Architect at IGT Solutions

Kendra has a nice AI built-in, enhancing the search experience and highly stable solution

There are many valuable features. For example, there are many documents that contain a lot of legal information. So we want to understand whether all the documents have the required complaint-related information or not, and whether they are following the standard policies of documentation. We have multiple documents, so we don't know which document has the sought-after information. Therefore, we want to perform an enterprise search on it. So there are a lot of use cases we are trying to build using these newer technologies, specifically Kendra. Moreover, Kendra has AI, which has an upper edge, and that is really helpful. It has a nice AI inbuilt, which improves the search part of it.

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Prabakaran SP

Software Architect at a financial services firm with 1-10 employees

Automated indexing has streamlined document search workflows but semantic relevance and setup complexity still need improvement

We used the semantic search capabilities of Azure AI Search, but we haven't gotten good results in the semantic search. So we are exploring with ChromaDB, and Cosmos is having the capability of doing the semantic search as well. We are exploring that. A few queries we use analytics search, which works and is good. Analytics search is good. We are trying the ML capabilities of the product since we are using Databricks and other tools for building the models, MLflow, and related items. We are still working on proof of concepts, which could be better with ChromaDB or Cosmos or vector search or inbuilt Databricks vector stores. Language processing is not about user intention; it's about the context. If there is a document and you want to know the context of a particular section, then we would use vector search. Instead of traversing through the whole document, while chunking it into the vector, we'll categorize and chunk, and then we'll look only at those chunks to do a semantic search. When comparing Azure AI Search, I'm doing a proof of concept because with ChromaDB I can create instances using LangChain anywhere. For per session, I can create one ChromaDB and can remove it, which is really useful for proof of concepts. Instead of creating an Azure AI Search instance and doing that there, that is one advantage I'm seeing for the proof of concept alone, not for the entire product. I hope it should support all the embedding providers as well. Is there a viewer or tool similar to Storage Explorer? We are basically SQL-centric people, so we used to find Cosmos DB very quick for us when we search something and create indexes. I guess there is some limitation in Azure AI Search. I couldn't remember now, such as querying limitations. I'm not remembering that part.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"We have good use cases where stability is everything. So it's a stable solution."

"Provides flexibility to tune the relevance and ranking of results."

"The product is pretty resilient."

"Azure Search is well-documented, making it easy to understand and implement."

"The product is extremely configurable, allowing you to customize the search experience to suit your needs."

"Offers a tremendous amount of flexibility and scalability when integrating with applications."

"It provides good access capabilities to various platforms."

"The amount of flexibility and agility is really assuring."

"Because all communication is done via the REST API, data is retrieved quickly in JSON format to reduce overhead and latency."

"Creates indexers to get data from different data sources."

More Azure AI Search pros

Cons

"The time it takes for indexing documents could be reduced."

"There are some token limits."

"For availability, expanding its use to all Azure datacenters would be helpful in increasing awareness and usage of the product."

"The after-hour services are slow."

"The solution's stability could be better."

"The initial setup is not as easy as it should be."

"It would be good if the site found a better way to filter things based on subscription."

"The pricing is room for improvement."

"For SDKs, Azure Search currently offers solutions for .NET and Python. Additional platforms would be welcomed, especially native iOS and Android solutions for mobile development."

"They should add an API for third-party vendors, like a security operating center or reporting system, that would be a big improvement."

More Azure AI Search cons

Pricing and Cost Advice

"The pricing falls in the medium range."

"I would rate the pricing an eight out of ten, where one is the low price, and ten is the high price."

"I think the solution's pricing is ok compared to other cloud devices."

"For the actual costs, I encourage users to view the pricing page on the Azure site for details."

"When telling people about the product, I always encourage them to set up a new service using the free pricing tier. This allows them to learn about the product and its capabilities in a risk-free environment. Depending on their needs, the free tier may be suitable for their projects, however enterprise applications will most likely required a higher, paid tier."

"The solution is affordable."

"The cost is comparable."

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Top Industries

By visitors reading reviews

Computer Software Company

17%

Financial Services Firm

16%

Manufacturing Company

11%

Retailer

Computer Software Company

20%

Financial Services Firm

12%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	4

Questions from the Community

Ask a question

Earn 20 points

What needs improvement with Azure Search?

See all answers

What is your primary use case for Azure Search?

Our use case for Azure AI Search is that we earlier thought to build a vector search and used to have the vector search query in Azure AI Search. Earlier, when it was a search service, we used to l...

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What advice do you have for others considering Azure Search?

I can answer a few questions about Azure AI Search to share my opinion. I am still working with Azure and using Azure solutions. We haven't used Cognitive Skills in Azure AI Search. We also got a d...

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Comparisons

Amazon OpenSearch Service vs Amazon Kendra

Compared 21% of the time

Amazon Athena vs Amazon Kendra

Compared 18% of the time

Elastic Search vs Amazon Kendra

Compared 12% of the time

Glean Platform vs Amazon Kendra

Compared 10% of the time

Algolia vs Amazon Kendra

Compared 9% of the time

More Amazon Kendra Competitors

Elastic Search vs Azure AI Search

Compared 30% of the time

Amazon OpenSearch Service vs Azure AI Search

Compared 15% of the time

Algolia vs Azure AI Search

Compared 11% of the time

Solr vs Azure AI Search

Compared 10% of the time

Xapien vs Azure AI Search

Compared 8% of the time

More Azure AI Search Competitors

Product Reports

Buyer's Guide

Search as a Service

January 2026

Download Amazon Kendra product report

Buyer's Guide

Azure AI Search

January 2026

Download Azure AI Search product report

Overview

Amazon Kendra is a highly accurate and easy to use enterprise search service that’s powered by machine learning. Kendra enables developers to add search capabilities to their applications so their end users can discover information stored within the vast amount of content spread across their company. This includes data from manuals, research reports, FAQs, HR documentation, customer service guides, and is found across various systems such as file systems, web sites, Box, DropBox, Salesforce, SharePoint, relational databases, Amazon S3, and more. When you type a question, the service uses machine learning algorithms to understand the context and return the most relevant results, whether that be a precise answer or an entire document. For example, you can ask a question like "How much is the cash reward on the corporate credit card?” and Kendra will map to the relevant documents and return a specific answer like “2%”. Kendra provides sample code so that you can get started quickly and easily integrate highly accurate search into your new or existing applications.

Amazon Web Services (AWS)

Azure Search is a search-as-a-service cloud solution that gives developers APIs and tools for adding a rich search experience over your data in web, mobile, and enterprise applications. Functionality is exposed through a simple REST API or .NET SDK that masks the inherent complexity of search technology. In addition to APIs, the Azure portal provides administration and prototyping support. Infrastructure and availability are managed by Microsoft.

Microsoft

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

XOMNI, Real Madrid C.F., Weichert Realtors, JLL, NAV CANADA, Medihoo, autoTrader Corporation, Gjirafa

Buyer's Guide

Amazon Kendra vs. Azure AI Search

December 2025

Free Report: Amazon Kendra vs. Azure AI Search

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