Amazon Athena vs Azure AI Search comparison

Why PeerSpot?

Amazon Web Services (AWS) and Microsoft are both solutions in the Search as a Service category. Amazon Web Services (AWS) is ranked #5 with an average rating of 8.2, while Microsoft is ranked #4 with an average rating of 8.3. Amazon Web Services (AWS) holds a 5.4% mindshare in SS, compared to Microsoft’s 12.8% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 75% of Microsoft users who would recommend it.

Amazon Athena

Read 11 Amazon Athena reviews

1,399 Views
1,399 Comparison Views

90% willing to recommend

Azure AI Search

Read 12 Azure AI Search reviews

2,397 Views
2,397 Comparison Views

75% willing to recommend

Amazon Athena

Azure AI Search

Comparison Buyer's Guide

Download the report

Executive Summary

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

Buyer's Guide

Amazon Athena vs. Azure AI 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

Amazon Athena

Ranking in Search as a Service

5th

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Azure AI Search

Ranking in Search as a Service

4th

Average Rating

7.6

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 Amazon Athena is 5.4%, down from 5.9% compared to the previous year. The mindshare of Azure AI Search is 12.8%, up from 9.6% 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%
Amazon Athena	5.4%
Other	81.8%

Search as a Service

Featured Reviews

Yush Mittal

Senior Software Engineer at a tech services company with 10,001+ employees

Serverless analytics has reduced daily data query costs and supports accurate financial reporting

The best features Amazon Athena offers are its straightforward framework to perform ad hoc analysis on the data that we have. We use it primarily as our main query engine, so we are not using anything such as Snowflake or similar solutions. We essentially ingest the data into S3 buckets, and then we use Amazon Athena for querying the data. It is our main query platform on AWS. All of our queries, for transformations, are Athena-based queries that are orchestrated via Step Functions. We only pay for the amount of data that we scan. Since we operate on a relatively lower amount of data on a daily basis, we incur a very low cost on our querying. The pay-per-query pricing of Amazon Athena impacts my daily operations significantly. For other query engines, we pay for the query execution time, but in Amazon Athena, you only pay for the amount of data that you have scanned. Our queries primarily filter out the data. Since we operate on a daily basis, we are only concerned with today's data. When querying the data, we automatically put the filter to have the data in today's timestamp only. This way, we incur very low costs compared to other query engines. Our query scans are about 10 to 20 MBs, and despite performing 100 to 150 queries per day, this keeps our costs very manageable. Amazon Athena has positively impacted my organization by providing a completely serverless infrastructure. We have Step Functions, Lambda, and S3 buckets where we store our data. Our main infrastructure is serverless. We wanted a query engine that is less demanding in terms of setup efforts and cost-efficient, so we decided to go with Amazon Athena. It fulfills all our use cases, plus the ACID compliance that it brings, because we use Iceberg on top of Amazon Athena. This ensures our queries and data are consistent, durable, and that the queries are isolated in terms of execution. Athena's ACID compliance, especially with Iceberg, impacts our data consistency and reliability. We apply Iceberg with Parquet, which helps us compress the data to a very good volume. With the applied compression algorithms, we preserve our data consistency during parallel transformations. ACID compliance helps us achieve this, ensuring we do not compromise on our data and that our GDPR for the data is preserved.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"Amazon Athena has positively impacted my organization by providing a completely serverless infrastructure."

"It's easy to set up the product."

"Amazon Athena's ability to query structured and unstructured data has been beneficial."

"Amazon Athena works for scalability; I query data using tagged data that uses user usage of applications that contain very big data, millions and billions of lines, and it works very well."

"Athena has a really good UI and is very compatible with on-prem products."

"After implementing Amazon Athena in our project, we have observed significant savings in cost structure and effort, and the end user is very happy and is conducting analytical work using Amazon Athena."

"The best feature of Amazon Athena is that we can use Glue to build the schema from the data and then we can query the data directly on S3."

"The solution is very easy to use and integrations are very smooth."

More Amazon Athena pros

"It provides good access capabilities to various platforms."

"Azure Search provides plenty of benefits for business teams and sales teams, as it's a CLM system that helps you find everything related to specific customers and deals."

"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."

"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 amount of flexibility and agility is really assuring."

"The customer engagement was good."

"The solution's initial setup is straightforward."

"Usually, that search functionality used to take around 10 secs to search data, and that time has been reduced to a few milliseconds now."

More Azure AI Search pros

Cons

"In terms of its integration capabilities, I would say it's not straightforward. It works, but it's a little bit tricky."

"I think it would be better if the product were more mature. It's still a young product compared to Power BI or Qlik. I find that development is a bit difficult, but it might be because I'm used to other tools. The dashboarding capabilities could be better. The reporting and statement generation could be better. I couldn't technically initiate picture-perfect reporting, for example, to send out statements every month for banking customers."

"While implementing Amazon Athena, I observed an issue where, even after writing an exclusion pattern on the AWS Glue side, whenever the end user queries, it skips the exclusion pattern, overriding it and directly fetching data from the S3 bucket."

"Amazon Athena can be improved, especially when working with S3 tables, which is a caveat for us."

"Transaction support is one of the biggest missing features."

"You have to build out the metadata yourself because of the nature of the cloud."

"If you compare it with Palantir, if you have some data and you want to quickly have a look at it, then that feature is not available in Amazon Cloud."

"One improvement I can suggest is that Athena needs to work better with third-parties. For example, the process of querying a Microsoft SQL warehouse could be improved."

More Amazon Athena cons

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

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

"The after-hour services are slow."

"The solution's stability could be better."

"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."

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

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

More Azure AI Search cons

Pricing and Cost Advice

"It doesn't cost much if you are already part of the AWS ecosystem."

"Athena is very inexpensive for being a cloud tool."

"I am happy with what they are charging and how they charge it, especially because they charge you per query, and not per series."

"The solution operates on a serverless model so you only pay for data that you consume."

"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."

"The solution is affordable."

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

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

"The cost is comparable."

See which vendors are best for you

Use our free recommendation engine to learn which Search as a Service solutions are best for your needs.

See recommendations

920,929 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

13%

Outsourcing Company

10%

Comms Service Provider

Financial Services Firm

11%

Computer Software Company

10%

Outsourcing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

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

Questions from the Community

What is your experience regarding pricing and costs for Amazon Athena?

My experience with Amazon Athena regarding pricing, setup costs, and licensing is quite clear after more than three years of usage. I am part of my client's FinOps cloud team, assisting in reducing...

See all answers

What needs improvement with Amazon Athena?

While implementing Amazon Athena, I observed an issue where, even after writing an exclusion pattern on the AWS Glue side, whenever the end user queries, it skips the exclusion pattern, overriding ...

See all answers

What is your primary use case for Amazon Athena?

My main use case for Amazon Athena is serving a healthcare organization in the US that needed an interactive SQL editor to write complex queries and retrieve details instead of checking manually in...

See all answers

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

Comparisons

Amazon OpenSearch Service vs Amazon Athena

Compared 32% of the time

Amazon Kendra vs Amazon Athena

Compared 14% of the time

Elastic Search vs Amazon Athena

Compared 13% of the time

Solr vs Amazon Athena

Compared 10% of the time

Amazon AWS CloudSearch vs Amazon Athena

Compared 8% of the time

More Amazon Athena Competitors

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

Solr vs Azure AI Search

Compared 9% of the time

Xapien vs Azure AI Search

Compared 3% of the time

More Azure AI Search Competitors

Product Reports

Buyer's Guide

Amazon Athena

November 2026

Download Amazon Athena product report

Buyer's Guide

Azure AI Search

November 2026

Download Azure AI Search product report

Overview

Amazon Athena is a serverless, interactive query service for analyzing data in Amazon S3 using SQL. It efficiently supports data lake architectures and offers features for diverse data formats without needing extensive infrastructure. Athena's integration with AWS Glue enhances schema management.

Amazon Athena leverages a serverless architecture to provide scalable, cost-effective query capabilities for large datasets stored in Amazon S3. With native support for Parquet and Avro, it efficiently manages both structured and unstructured data. Its federated query functionality allows access to varied data sources, while database partitioning optimizes performance and cost. Integration with AWS Glue simplifies schema building and streamlines data querying, although it faces challenges with ease of use, transaction support, and third-party integrations. Performance optimization is needed for complex queries and handling large datasets, while API capabilities and scheduling features could be improved. Users benefit from cost-saving efficiencies in data processing and the ability to extract quick insights through SQL queries, fostering more agile data-driven decisions.

What are the most important features of Amazon Athena?

Database Partitioning: Enhances query performance and reduces data retrieval costs.
Federated Queries: Accesses data across multiple sources seamlessly.
Supports Diverse Formats: Handles data in Parquet, Avro, and more.
Serverless Architecture: Offers automatic scaling for fluctuating workloads.
Glue Integration: Simplifies schema management and data organization.
Columnar Storage Optimization: Speeds up query processing and lowers costs.
Data Quality Identification: Detects inconsistencies and errors proactively.

What benefits and ROI should be expected from Amazon Athena?

Scalability: Automatically adjusts to processing demands, eliminating the need for infrastructure management.
Cost Efficiency: Only pay for queries run, making it budget-friendly for varying workloads.
Quick Insights: Facilitates swift SQL-based analysis, improving decision-making timelines.
Comprehensive Data Handling: Manages both structured and unstructured data seamlessly.
Enhanced Query Performance: Utilizes partitioning and columnar storage for optimized speed and cost.

In sectors such as finance, retail, and technology, Amazon Athena is utilized for data lake management where voluminous structured and unstructured data exists. Businesses create dashboards, automate workflows, and execute ad-hoc analyses efficiently. Its integration with Lake Formation and Glue supports complex industry-specific data tasks, ensuring streamlined data operations.

Amazon Web Services (AWS)

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

Sample Customers

bp, Cerner, Expedia, Finra, HESS, intuit, Kellog's, Philips, TIME, workday

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

Buyer's Guide

Amazon Athena vs. Azure AI Search

September 2026

Free Report: Amazon Athena vs. Azure AI Search

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

DOWNLOAD NOW

920,929 professionals have used our research since 2012.

See our Amazon Athena vs. Azure AI Search report.

See our list of best Search as a Service vendors.

We monitor all Search as a Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.