Amazon Athena vs Azure AI Search comparison

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 5.0% mindshare in SS, compared to Microsoft’s 11.3% 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 10 Amazon Athena reviews

1,092 Views
1,092 Comparison Views

90% willing to recommend

Azure AI Search

Read 11 Azure AI Search reviews

1,948 Views
1,948 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: August 2026).

Buyer's Guide

Amazon Athena vs. Azure AI Search

August 2026

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Helped 908,858 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 August 2026, in the Search as a Service category, the mindshare of Amazon Athena is 5.0%, down from 7.3% compared to the previous year. The mindshare of Azure AI Search is 11.3%, down from 11.5% 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	11.3%
Amazon Athena	5.0%
Other	83.7%

Search as a Service

Featured Reviews

Ciro Baldim Guerra

Sr Analytics Engineer at Itau Unibanco S.A.

Have struggled with exporting complex data and have disabled code suggestions due to inefficiency

I think there is room for improvement in Amazon Athena, and the first thing I will put is the data output. 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. The only option is copying the data, but sometimes if it exceeds 100 lines, if you copy and paste in Excel, it's very bad. You can't copy above 100 lines. The other option is downloading a CSV file, but the CSV file is not UTF-8 Unicode. Here in Brazil, we speak Portuguese, and there are a lot of special characters in the words and even names, and everything gets garbled when you put it in a CSV. You have to decode, encode, and there are a lot of problems. It could easily save as an Excel file since there are a lot of engines to help with it, so an XLSX file extension could be this way. Another point I would mention is the word completion. When I'm coding and making statements and queries, Amazon Athena tries to help me write the code, and that's very problematic. Sometimes I'm using some tables that I use every day, and Amazon Athena doesn't get the tables I'm using and suggests very improbable data. I have access to more than 30 databases and hundreds of tables. So, I turn it off, I disable the word completion because when I'm coding, the word completion makes the coding slower. It's very difficult, and every time I have to press escape to skip the completion. It's very ineffective, so I disable it because in other applications it functions very well, such as VS Code.

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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.

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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

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

"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."

"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."

"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."

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

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

More Amazon Athena pros

"The solution's initial setup is straightforward."

"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."

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

"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."

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

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

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

More Azure AI Search pros

Cons

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

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

"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."

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

"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."

"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."

"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."

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

More Amazon Athena cons

"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."

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

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

"The solution's stability could be better."

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

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

"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."

"The after-hour services are slow."

More Azure AI Search cons

Pricing and Cost Advice

"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."

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

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

"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."

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

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

12%

Outsourcing Company

Computer Software Company

16%

Financial Services Firm

11%

Manufacturing Company

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

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...

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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 31% of the time

Elastic Search vs Amazon Athena

Compared 17% of the time

Amazon Kendra vs Amazon Athena

Compared 16% 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 27% of the time

Amazon OpenSearch Service vs Azure AI Search

Compared 24% of the time

Amazon Kendra vs Azure AI Search

Compared 10% of the time

Algolia vs Azure AI Search

Compared 9% 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

August 2026

Download Amazon Athena product report

Buyer's Guide

Azure AI Search

August 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

August 2026

Free Report: Amazon Athena vs. Azure AI Search

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