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 #6 with an average rating of 8.4, while Microsoft is ranked #5 with an average rating of 8.0. Amazon Web Services (AWS) holds a 5.3% mindshare in SS, compared to Microsoft’s 11.7% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 72% of Microsoft users who would recommend it.

Amazon Athena

Read 11 Amazon Athena reviews

1,189 Views
1,189 Comparison Views

90% willing to recommend

Azure AI Search

Read 11 Azure AI Search reviews

2,126 Views
2,126 Comparison Views

72% willing to recommend

Amazon Athena

Azure AI Search

Comparison Buyer's Guide

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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 913,924 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

6th

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

5th

Average Rating

7.6

Number of Reviews

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 Athena is 5.3%, down from 5.9% 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
Product	Mindshare (%)
Azure AI Search	11.7%
Amazon Athena	5.3%
Other	83.0%

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

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.

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 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."

"Amazon Athena is very stable. I never had any issues with it. The dashboarding tool is okay."

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

"One of the most valuable features is the ability to partition your databases. I also like the federal query functionality, for cases when you have to query outside your S3 storage, or even completely outside of the AWS platform."

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

"Athena is serverless, so we don’t have to provision or manage compute clusters, and we can simply point Athena at our data in S3 and run SQL queries immediately."

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

"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."

More Amazon Athena pros

"The customer engagement was good."

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

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

"The solution's initial setup is straightforward."

"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."

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

"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."

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

More Azure AI Search pros

Cons

"I use Python to query in Amazon Athena, and it's very complex and difficult just to save Amazon Athena results as an Excel file."

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

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

"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."

"The solution should include a better API for query services."

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

"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."

"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."

More Amazon Athena 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 solution's stability could be better."

"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."

"The after-hour services are slow."

"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 pricing is room for improvement."

"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."

More Azure AI Search cons

Pricing and Cost Advice

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

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

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

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

"The cost is comparable."

"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."

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

"The solution is affordable."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

12%

Outsourcing Company

10%

Healthcare Company

Computer Software Company

16%

Financial Services Firm

11%

Manufacturing Company

Outsourcing Company

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	4

Questions from the Community

What needs improvement with Amazon Athena?

Amazon Athena can be improved, especially when working with S3 tables, which is a caveat for us. It does not work very well with Amazon Athena, as we have to do multiple settings in terms of provid...

See all answers

What is your primary use case for Amazon Athena?

My main use case for Amazon Athena is querying data that sits in S3 buckets. Currently, I am working for an aviation client for which we receive data on a daily basis. We take this data from a sour...

See all answers

What advice do you have for others considering Amazon Athena?

Regarding Amazon Athena's AI capabilities, we do not utilize them. However, data governance is ensured, and security is adequate since we operate within the VPC. ACID compliance helps preserve GDPR...

See all answers

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...

See all answers

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,...

See all answers

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...

See all answers

Comparisons

Amazon OpenSearch Service vs Amazon Athena

Compared 33% of the time

Elastic Search vs Amazon Athena

Compared 15% of the time

Amazon Kendra vs Amazon Athena

Compared 15% of the time

Solr vs Amazon Athena

Compared 9% of the time

Glean Platform vs Amazon Athena

Compared 7% of the time

More Amazon Athena Competitors

Elastic Search vs Azure AI Search

Compared 26% of the time

Amazon OpenSearch Service vs Azure AI Search

Compared 22% of the time

Amazon Kendra vs Azure AI Search

Compared 10% of the time

Algolia vs Azure AI Search

Compared 10% of the time

Xapien vs Azure AI Search

Compared 4% of the time

More Azure AI Search Competitors

Product Reports

Buyer's Guide

Amazon Athena

September 2026

Download Amazon Athena product report

Buyer's Guide

Azure AI Search

September 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

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