Azure AI Search vs Solr comparison

Why PeerSpot?

Microsoft and Apache are both solutions in the Search as a Service category. Microsoft is ranked #4 with an average rating of 8.3, while Apache is ranked #9. Microsoft holds a 12.8% mindshare in SS, compared to Apache’s 6.5% mindshare. Additionally, 75% of Microsoft users are willing to recommend the solution, compared to 100% of Apache 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

Solr

Read 4 Solr reviews

1,342 Views
1,342 Comparison Views

100% willing to recommend

Azure AI Search

Solr

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure AI Search and Solr 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. Solr Report (Updated: September 2026).

Buyer's Guide

Azure AI Search vs. Solr

September 2026

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Helped 920,769 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

Solr

Ranking in Search as a Service

9th

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

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 Solr is 6.5%, up from 5.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Search as a Service Mindshare Distribution
Product	Mindshare (%)
Azure AI Search	12.8%
Solr	6.5%
Other	80.7%

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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it_user823641

Senior Search Engineer at a financial services firm with 51-200 employees

The Natural Language Search capability is helpful and intuitive for our users

The initial setup is complex because this is a distributed system, and you have to make sure that every individual node is aware of every other node in existence. This search engine has a large capacity, so you need to make sure that there is enough buffer space. We took one month to deploy and perform a fresh setup. Our strategy was to start with a local data center, before venturing into cross data center replicas. A staff size of two to four people is suitable for deploying and maintaining the solution, depending upon the scale. They would set up the solution and put monitoring in place for the indexing jobs, as well as design the schema so that the data can feed well.

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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

"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 solution's initial setup is straightforward."

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

"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."

"The broad access capability is probably the most valuable feature, as it provides access with hardly any physical infrastructure."

"The product is pretty resilient."

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

"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."

More Azure AI Search pros

"Sharding data, Faceting, Hit Highlighting, parent-child Block Join and Grouping, and multi-mode platform are all valuable features."

"The most valuable feature is the ability to perform a natural language search."

"One of the best aspects of the solution is the indexing; it's already indexed to all the fields in the category, so we don't need to spend so much extra effort to do the indexing, which is great."

"It has improved our search ranking, relevancy, search performance, and user retention."

"This is an infinitely scalable product with state-of-the-art technology, and the value of Natural Language Search is tremendous."

"We use Solr to index over 600k documents; it's very fast, flexible to use, and the speed of indexing individual documents has been great."

Cons

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

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

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

"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."

"On a scale from one to ten where one is the worst and ten is the best, I would rate Azure Search as probably a six-out-of-ten."

"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."

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

More Azure AI Search cons

"The solution's grammar and syntax should be easier."

"SolrCloud stability, indexing and commit speed, and real-time Indexing need improvement."

"It does take a little bit of effort to use and understand the solution. It would help us a lot if the solution offered up more documentation or tutorials to help with training or troubleshooting."

"Memory utilization could be better but it is an industrial strength tool so some overhead is to be expected."

"The performance for this solution, in terms of queries, could be improved."

"Encountered issues with both master-slave and SolrCloud. Indexing and serving traffic from same collection has very poor performance. Some components are slow for searching."

"With increased sharding, performance degrades. Merger, when present, is a bottle-neck. Peer-to-peer sync has issues in SolrCloud when index is incrementally updated."

Pricing and Cost Advice

"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."

"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."

"The only costs in addition to the standard licensing fees are related to the hardware, depending on whether it is cloud-based, or on-premise."

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Computer Software Company

11%

Outsourcing Company

Comms Service Provider

16%

Financial Services Firm

14%

Outsourcing Company

14%

Construction Company

12%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

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

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Comparisons

Elastic Search vs Azure AI Search

Compared 25% of the time

Amazon OpenSearch Service vs Azure AI Search

Compared 22% of the time

Algolia vs Azure AI Search

Compared 10% 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

Elastic Search vs Solr

Compared 19% of the time

Amazon OpenSearch Service vs Solr

Compared 13% of the time

Algolia vs Solr

Compared 13% of the time

Amazon AWS CloudSearch vs Solr

Compared 11% of the time

Amazon Athena vs Solr

Compared 8% of the time

More Solr Competitors

Product Reports

Buyer's Guide

Azure AI Search

October 2026

Download Azure AI Search product report

Buyer's Guide

Search as a Service

October 2026

Download Solr product report

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

Solr is an open-source search platform from the Apache Lucene project designed for scalability and providing high-performance search capabilities. It is widely used for its flexibility and scalability, making it ideal for various search-driven applications.

Solr is recognized for its capacity to handle large amounts of data and complex queries, offering a distributed approach to search. It enables users to perform full-text search, hit highlighting, faceted search, real-time indexing, and dynamic clustering. Solr's robust administration interfaces and extensive plugin architecture allow tailored configurations suited to specific applications. Its compatibility with Hadoop environments enhances big data handling, facilitating effective, high-volume search processing.

What are the most important features of Solr?

Full-text Search: Provides advanced search capabilities with support for multiple data types.
Faceting: Offers dynamic faceting that simplifies data classification.
Scalability: Allows reliable distributed search indexes that can scale horizontally.
Real-time Indexing: Supports quick updates and indexing of data for rapid search availability.
Rich Document Handling: Can manage a wide range of document formats, including PDFs and Word files.

What benefits or ROI should be considered?

Cost Efficiency: As an open-source platform, it reduces licensing costs.
Flexibility: Supports custom implementations and applications across different industries.
Performance: Offers high-speed searches, improving data retrieval times.
Scalability: Adapts to growing data needs while maintaining performance levels.

Solr is prominently used in e-commerce, where rapid and precise search capabilities are critical for customer satisfaction. In publishing, it powers content discovery and enhances user engagement through personalized recommendations. Finance sectors utilize Solr for analyzing large datasets, optimizing search for records and transactions. Its versatility and ability to integrate seamlessly into existing IT infrastructures make it a preferred choice across these markets.

Apache

Sample Customers

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

eHarmony, Sears, StubHub, Best Buy, Instagram, Netflix, Disney, AT&T, eBay, AOL, Bloomberg, Comcast, Ticketmaster, Travelocity, MTV Networks

Buyer's Guide

Azure AI Search vs. Solr

September 2026

Free Report: Azure AI Search vs. Solr

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

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