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Amazon OpenSearch Service vs Azure AI Search comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Jun 3, 2026

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 OpenSearch Service
Ranking in Search as a Service
3rd
Average Rating
7.6
Reviews Sentiment
6.7
Number of Reviews
13
Ranking in other categories
Application Performance Monitoring (APM) and Observability (21st), Log Management (15th)
Azure AI Search
Ranking in Search as a Service
5th
Average Rating
7.6
Number of Reviews
11
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of September 2026, in the Search as a Service category, the mindshare of Amazon OpenSearch Service is 13.0%, up from 4.5% compared to the previous year. The mindshare of Azure AI Search is 11.7%, up from 10.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Search as a Service Mindshare Distribution
ProductMindshare (%)
Amazon OpenSearch Service13.0%
Azure AI Search11.7%
Other75.3%
Search as a Service
 

Featured Reviews

Md. Shahariar Hossen - PeerSpot reviewer
Senior Software Engineer at Cefalo
Event tracking has become smoother and data analytics provide clear insights for user actions
Amazon OpenSearch Service is not providing the processing feature directly. From Amazon OpenSearch Service, we are actually maintaining the AWS SQS, the queue service, which is responsible for providing information about what data has to be modified. So using that SQS, we're actually providing it, but we're not directly using Amazon OpenSearch Service for keeping data to other data pipeline thing. So far we didn't use it for any machine learning purposes, but in future, we have plans to extend or implement this feature. Since AWS itself is secure and Amazon OpenSearch Service is a part of this entire ecosystem, it becomes much easier for security purposes. From the validation point of view, Amazon OpenSearch Service itself provides easy to communicate APIs and up-to-date documents, which is much beneficial. For example, if I'm missing anything, I can directly go and check the documentation. That is actually much easier. I would rate it as really good so far. It's much faster. For our local machine, we can also use a kind of replica of Amazon OpenSearch Service just for development purposes. That is another good feature. I would say for the encryption thing and also the user access control management, it's much faster. For some of these hashing algorithms, it also worked really well so far. To be honest, I didn't find any places where it can be improved. However, I think they could provide more abstraction. For example, still for searching, we have to write down the queries in a specific manner, such as for a specific JSON structure or in a specific way. Otherwise, they don't provide us the actual results. For at least this purpose, I think abstraction could be a bit easier or a bit improved. Other than that, right now there is the age of AI, so some kind of prompting could also work, but I'm not sure how it could be integrated. As a user, lower prices or reasonable pricing is always better. Those can be improved as well. However, it is good that most of the services including Amazon OpenSearch Service actually provide pay as you go pricing. So if there were a bit lower version or a bit less payment methodology, it might be much better.
Prabakaran SP - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"It enables us to efficiently search and retrieve our event data, offering us a versatile approach to locate specific information within these logs."
"Amazon OpenSearch Service provides a managed database solution, so we don't need to manage everything ourselves."
"The stability of the product is good."
"Regarding valuable features of the solution, we found with the process, which we have used in both cases where we used the solution that while you're seeing the streaming of data, you can analyze in the initial phase what sort of data you are streaming and whether it is valuable."
"In case there is a failure, Elastic manages everything well, and there no major downtime."
"I would definitely recommend Amazon OpenSearch Service to other professionals due to its fast and reliable search capabilities."
"The customer service is excellent, rated nine out of ten."
"This service already sorts data like vectors. They have classified the storage pre-defined."
"Since using Azure AI Search, time saving will be the main differentiator among other types of tools I've tried before, and it's also a very holistic tool, so it covers many use cases."
"By implementing Azure Search, I have been able to create immersive, feature-rich search experiences for my applications that feature helpful facets, in-line highlighting, and predictive results as user types."
"The customer engagement was good."
"The product is pretty resilient."
"The product is extremely configurable, allowing you to customize the search experience to suit your needs."
"Usually, that search functionality used to take around 10 secs to search data, and that time has been reduced to a few milliseconds now."
"The features in Azure AI Search that are most valuable include the ability to automate index creation, and you can drop in the blob storage or drop in the SQL table, which will get automatically indexed."
"The broad access capability is probably the most valuable feature, as it provides access with hardly any physical infrastructure."
 

Cons

"The pricing aspect is a concern. The service is way too costly. For the past month, I used only 30 to 40 MB of data, and the cost was $500. AWS could improve pricing."
"We faced documentation challenges during integration after migrating from Elasticsearch to Amazon OpenSearch Service. Better documentation on integration, query handling, and a more user-friendly UI could enhance the product."
"The configuration should be more straightforward because we had to select a lot of things."
"There is the problem with the database. Amazon only provides the host to run to our applications bias, but there is no option to manage the database within the Elasticsearch product."
"The price is fair yet leans towards the expensive side. I'd rate it five out of ten with respect to capabilities vs. cost."
"They can enhance data visualization."
"It would be beneficial to have some level of customization available in the managed service, tailored to the specific use cases of the end users."
"One glaring issue was with our mapping configuration as the system accepted the data we posted, but after a few months, when we attempted complex queries, we realized the date formatting had become problematic."
"For availability, expanding its use to all Azure datacenters would be helpful in increasing awareness and usage of the product.​"
"Azure AI Search could be improved regarding compatibility with Azure Blob Storage in order to keep the prompts and everything that I am using for building the tool safe."
"The initial setup is not as easy as it should be."
"The after-hour services are slow."
"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."
"The pricing is room for improvement."
"It would be good if the site found a better way to filter things based on subscription."
"Adding items to Azure Search using its .NET APIs sometimes throws exceptions."
 

Pricing and Cost Advice

"You only pay for what you use."
"There is a community edition available and the price of the commercial offering is reasonable."
"The solution is not expensive, but priced averagely, I will say."
"Compared to other cloud platforms, it is manageable and not very expensive."
"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."
"The solution is affordable."
"The cost is comparable."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Manufacturing Company
10%
Computer Software Company
8%
Outsourcing Company
6%
Computer Software Company
16%
Financial Services Firm
11%
Manufacturing Company
8%
Outsourcing Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business7
Midsize Enterprise2
Large Enterprise4
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise5
Large Enterprise4
 

Questions from the Community

What is your experience regarding pricing and costs for Amazon OpenSearch Service?
I would consider the pricing as a six based on how much data we are handling; if we handle minimal data, it's cheap, but for large data, it becomes costly. Our clients usually pay between $1,000 to...
What needs improvement with Amazon OpenSearch Service?
Amazon OpenSearch Service is not providing the processing feature directly. From Amazon OpenSearch Service, we are actually maintaining the AWS SQS, the queue service, which is responsible for prov...
What is your primary use case for Amazon OpenSearch Service?
Amazon OpenSearch Service is a user-friendly version of Elasticsearch, as per my understanding. I have been using it for our volunteer management system where around 5,000 to 6,000 users are using ...
What needs improvement with Azure Search?
Azure AI Search could be improved regarding compatibility with Azure Blob Storage in order to keep the prompts and everything that I am using for building the tool safe. Regarding needed improvemen...
What is your primary use case for Azure Search?
My main use case for Azure AI Search is the index for the customization portal that they have. It combines data sources, indexers, and skill sets, making it a well-developed component. For example,...
What advice do you have for others considering Azure Search?
The advice I would give to others looking into using Azure AI Search is to first watch the tutorials and seek information on the website, as it is very reliable. Overall, Azure AI Search is a great...
 

Also Known As

Amazon Elasticsearch Service
No data available
 

Overview

 

Sample Customers

VIDCOIN, Wyng, Yellow New Zealand, zipMoney, Cimri, Siemens, Unbabel
XOMNI, Real Madrid C.F., Weichert Realtors, JLL, NAV CANADA, Medihoo, autoTrader Corporation, Gjirafa
Find out what your peers are saying about Amazon OpenSearch Service vs. Azure AI Search and other solutions. Updated: September 2026.
913,924 professionals have used our research since 2012.