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Azure AI Search vs Elastic Search comparison

Microsoft and Elastic are both solutions in the Search as a Service category. Microsoft is ranked #4 with an average rating of 8.3, while Elastic is ranked #1 with an average rating of 8.4. Microsoft holds a 12.8% mindshare in SS, compared to Elastic’s 15.3% mindshare. Additionally, 75% of Microsoft users are willing to recommend the solution, compared to 98% of Elastic users who would recommend it.

Azure AI Search

Read 12 Azure AI Search reviews

2,397 Views
2,397 Comparison Views

75% willing to recommend

Elastic Search

Read 100 Elastic Search reviews

14,072 Views
3,940 Comparison Views

98% willing to recommend

Azure AI Search

Elastic Search

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure AI Search and Elastic Search based on real PeerSpot user reviews.

Find out in this report how the two Search as a Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Azure AI Search vs. Elastic Search Report (Updated: September 2026).

Buyer's Guide

Azure AI Search vs. Elastic Search

September 2026

Download the complete report

Helped 920,929 peers since 2012

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

Azure AI Search

Ranking in Search as a Service

4th

Average Rating

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Elastic Search

Ranking in Search as a Service

1st

Average Rating

8.2

Reviews Sentiment

6.5

Number of Reviews

100

Ranking in other categories

Indexing and Search (1st), Cloud Data Integration (8th), Vector Databases (6th)

Mindshare comparison

As of November 2026, in the Search as a Service category, the mindshare of Azure AI Search is 12.8%, up from 9.6% compared to the previous year. The mindshare of Elastic Search is 15.3%, down from 18.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Search as a Service Mindshare Distribution
Product	Mindshare (%)
Elastic Search	15.3%
Azure AI Search	12.8%
Other	71.9%

Search as a Service

Featured Reviews

Harshal Chandekar

AI Consultant at a tech vendor with 10,001+ employees

Rag workflows have improved document search and reduced development effort for client projects

In my experience, the best features Azure AI Search offers are that it is very easy to upload documents and they can obviously be very agile and we can query upon these documents very easily. When I say it is easy to upload and query documents, it makes the application more reliable in the case that if we have a certain study or certain use case wherein just that PDF or just those amount of information will be used and not the entire thing, so the user can just go quickly and search for that documentation. I think it is fully integrated with Azure and we use Azure primarily for our primary cloud platform, so that helps very much in integrating and in maintaining one service provider. Regarding Azure AI Search's AI capabilities, it provides good security in terms of reliability and scalability as well, so there are no concerns for it regarding those issues. Regarding Azure AI Search's AI capabilities, it is accurate and reliable 99% of the time.

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reviewer2817942

Senior Software Engineer at a consultancy with 11-50 employees

Logging and vector search have transformed observability and empowered reliable ai agents

Elastic Search is not specifically being used for certain purposes. I deploy Elastic Search database on the cloud and use cloud services so that nobody can attack. However, I do not use Elastic Search to resolve attack issues. The basic main purpose of Elastic Search, as of now, I feel it can do more in the AI area. Sometime I saw that when I am developing RAG and have to generate the embeddings, which I call metadata, sometimes it tries to fail. That durability or issue handling should be improved, but apart from that, I did not find anything as of now. As per my use case, whatever I am using seems pretty good. Apart from that, some definitely improvement will be there. One improvement is that it should be faster. Whenever I am searching any logs, it takes much time. For example, if I open my log in Notepad or a similar tool, I can search the text within a second. With Elastic Search, it takes a little bit of time, ten to fifteen seconds. That can be improved. Sometimes, engineers take time to assign when I create a ticket.

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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

"Azure AI Search has impacted my organization positively with overall time saving and low costs as the main outputs that we get after using it."

"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 product is pretty resilient."

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

"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."

"Azure AI Search has positively impacted my organization by reducing the turnaround time for developing applications and for setting up vector databases and then for consuming other RAG applications as well."

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

More Azure AI Search pros

"The solution is quite scalable and this is one of its advantages."

"The initial installation and setup were straightforward."

"The product offers a powerful, cost effective solution for proprietary log management and is easy to understand and start with."

"We have many advantages from the features of Elasticsearch, and we have enough possibilities and features with Elasticsearch for our business requirements."

"One thing I appreciate about Elastic Search is the ability to aggregate everything into one dashboard, so I can have monitoring, logs, and traces in one portal instead of having multiple different tools to do the same."

"I really like the visualization that you can do within it; that's really handy, and product-wise, it is a very good and stable product."

"All the quality features are there. There are about 60 to 70 reports available."

"We had many reasons to implement Elasticsearch for search term solutions. Elasticsearch products provide enterprise landscape support for different areas of the company."

More Elastic Search pros

Cons

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

"The pricing is room for improvement."

"We used the semantic search capabilities of Azure AI Search, but we haven't gotten good results in the semantic search."

"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 primarily because the UX and UI could be a little bit more intuitive for persons since the learning curve could be a little bit high."

"Adding items to Azure Search using its .NET APIs sometimes throws exceptions."

"The after-hour services are slow."

"I think Azure AI Search can be improved by implementing it with the agents as well, and I think that their agent integration is something they could work upon or they can improve it very easily or without managing anything, but apart from that, it is building basic functionalities of RAG, but if that would be expanded to agents as well, then that would be very useful."

More Azure AI Search cons

"I think the pricing of Elastic Search is really, really expensive."

"They could improve some of the platform's infrastructure management capabilities."

"Something that could be improved is better integrations with Cortex and QRadar, for example."

"Scalability of Elastic Search presents disadvantages, particularly when handling minimal or production-level data."

"The deployment was a struggle as I faced challenges with bash commands and understanding how to run things on my system."

"I think the biggest issue we had with Elastic Search was regarding integrations with our multi-factor authentication tool."

"From the UI point of view, we are using most probably Kibana, and I think they can do much better than that."

"Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one." In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release."

More Elastic Search cons

Pricing and Cost Advice

"The solution is affordable."

"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."

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

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

"The cost is comparable."

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

"The cost varies based on factors like usage volume, network load, data storage size, and service utilization. If your usage isn't too extensive, the cost will be lower."

"It can move from $10,000 US Dollars per year to any price based on how powerful you need the searches to be and the capacity in terms of storage and process."

"The pricing structure depends on the scalability steps."

"The price of Elastic Enterprise is very, very competitive."

"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive."

"The solution is affordable."

"ELK has been considered as an alternative to Splunk to reduce licensing costs."

"To access all the features available you require both the open source license and the production license."

More Elastic Search pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Computer Software Company

10%

Outsourcing Company

Comms Service Provider

Financial Services Firm

11%

Outsourcing Company

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	5
Large Enterprise	5

By reviewers
Company Size	Count
Small Business	40
Midsize Enterprise	12
Large Enterprise	50

Questions from the Community

What needs improvement with Azure Search?

I think Azure AI Search can be improved by implementing it with the agents as well, and I think that their agent integration is something they could work upon or they can improve it very easily or ...

See all answers

What is your primary use case for Azure Search?

My main use case for Azure AI Search is building RAG workflows which are useful for client work. I used Azure AI Search in finding documents about a user manual in which the problem statement was i...

See all answers

What advice do you have for others considering Azure Search?

On a scale of 1 to 10, I rate Azure AI Search a nine. I chose a nine for Azure AI Search primarily because of the agents and the use cases are very limited, I would say to RAG as of now. Azure AI S...

See all answers

What is your experience regarding pricing and costs for ELK Elasticsearch?

The pricing for Elastic Search is mainly budgeted according to the organization budget, so we take it as a yearly subscription, and that is acceptable since we do get a fair discount when we are ta...

See all answers

What needs improvement with ELK Elasticsearch?

When we get the logs, it is mostly about how we edit the configurations and how we make changes according to the requirements of our organization. In these cases, the logs sometimes can be a bit in...

See all answers

What is your primary use case for ELK Elasticsearch?

I am the Elastic Search admin for my organization, and we are using Elastic Search to handle the traffic to GCP. The monitoring of all the clusters and all the deployments are quite good, and compa...

See all answers

Comparisons

Amazon OpenSearch Service vs Azure AI Search

Compared 22% of the time

Algolia vs Azure AI Search

Compared 10% of the time

Solr vs Azure AI Search

Compared 9% of the time

Amazon Kendra vs Azure AI Search

Compared 9% of the time

Amazon AWS CloudSearch vs Azure AI Search

Compared 9% of the time

More Azure AI Search Competitors

Milvus vs Elastic Search

Compared 7% of the time

PostgreSQL vs Elastic Search

Compared 4% of the time

Palantir Foundry vs Elastic Search

Compared 3% of the time

Algolia vs Elastic Search

Compared 3% of the time

Glean Platform vs Elastic Search

Compared 3% of the time

More Elastic Search Competitors

Product Reports

Buyer's Guide

Azure AI Search

November 2026

Download Azure AI Search product report

Buyer's Guide

Elastic Search

November 2026

Download Elastic Search product report

Also Known As

No data available

Elastic Enterprise Search, Swiftype, Elastic Cloud

Overview

Azure AI Search is a cloud-based service offering flexible, user-friendly search with features like custom scoring, text analyzers, and seamless integration, simplifying data infrastructure.

Azure AI Search delivers configurable features such as custom scoring and synonym mapping, supporting broad access that simplifies infrastructure requirements. Users value its comprehensive documentation, stable search syntax, and resilience comparable to Elasticsearch. Automation through blob storage or SQL tables facilitates effortless full-text search and field-specific indexing, enhancing performance. While room for improvement exists, notably in expanding SDK support beyond .NET and Python and refining interface and documentation, Azure AI Search is preferred for applications in the tech space for its ease of setup, speed, and integration capabilities.

What are its key features?

Custom Scoring: Tailor ranking to business preferences.
Text Analyzers: Refine search input for precise results.
Synonym Mapping: Enhance relevancy with synonym support.
Automated Indexing: Streamline index creation with SQL or blob storage.
Seamless Integration: Easy integration with Azure portal and other services.

What benefits should users note in reviews?

Flexibility: Easily adaptable for different business needs.
Resilience: Reliable functionality with stable search syntax.
Effortless Setup: Quick implementation and efficient search capabilities.
Comprehensive Documentation: Detailed guides aiding smooth operation.
Performance: Consistency in high-speed search operations.

In the tech industry, Azure AI Search is implemented for managing accounts, integrating with applications, and addressing security issues, enhancing scalability with Active Directory syncing and Office 365 linking. Users leverage it for efficient log searching, VM identification, and handling vector search queries, appreciating its speed and integration support.

Microsoft

Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.

Elastic

Sample Customers

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

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.

Buyer's Guide

Azure AI Search vs. Elastic Search

September 2026

Free Report: Azure AI Search vs. Elastic Search

Find out what your peers are saying about Azure AI Search vs. Elastic Search and other solutions. Updated: September 2026.

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