Amazon Athena vs Amazon OpenSearch Service comparison

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Amazon Athena and Amazon OpenSearch Service are both solutions in the Search as a Service category. Amazon Athena is ranked #6 with an average rating of 8.4, while Amazon OpenSearch Service is ranked #3 with an average rating of 7.8. Amazon Athena holds a 5.3% mindshare in SS, compared to Amazon OpenSearch Service’s 13.0% mindshare. Additionally, 90% of Amazon Athena users are willing to recommend the solution, compared to 92% of Amazon OpenSearch Service users who would recommend it.

Amazon Athena

Read 11 Amazon Athena reviews

1,189 Views
1,189 Comparison Views

90% willing to recommend

Amazon OpenSearch Service

Read 13 Amazon OpenSearch Service reviews

8,392 Views
1,427 Comparison Views

92% willing to recommend

Amazon Athena

Amazon OpenSearch Service

Comparison Buyer's Guide

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Executive SummaryUpdated on Feb 22, 2026

Amazon Athena and Amazon OpenSearch Service operate in the data management category. OpenSearch Service appears to have the advantage in providing sophisticated search and analytics, whereas Athena excels in simplicity and cost-effectiveness for accessing large datasets.

Features: Amazon Athena enables executing SQL queries on S3-stored datasets without infrastructure setup, compatible with diverse data formats. The federated query functionality and metastore support seamless querying across AWS accounts. Amazon OpenSearch Service includes extensive search capabilities, analytics, and integrations, facilitating fast searches of large data volumes. The OpenSearch dashboard supports tool integrations and visualizations.

Room for Improvement: Amazon Athena might enhance its performance optimization for complex queries and provide greater insights into data quality. Ease of federated query setup could be improved. Amazon OpenSearch Service could reduce the learning curve of its feature set and improve documentation clarity. Enhancing real-time analytics and reducing initial setup complexity could further streamline usage.

Ease of Deployment and Customer Service: Athena offers straightforward deployment with no infrastructure setup, while OpenSearch Service provides managed deployment options, including scaling and patching support. Both Athena and OpenSearch receive robust AWS customer support, though Athena's simplicity generally requires less frequent assistance.

Pricing and ROI: Amazon Athena employs a pay-per-query pricing model, offering cost efficiency and high ROI by reducing upfront costs. Amazon OpenSearch Service may incur higher initial expenses but delivers significant ROI for businesses requiring advanced analytics and search capabilities considering its feature-rich environment.

To learn more, read our detailed Amazon Athena vs. Amazon OpenSearch Service Report (Updated: September 2026).

Buyer's Guide

Amazon Athena vs. Amazon OpenSearch Service

September 2026

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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

Amazon OpenSearch Service

Ranking in Search as a Service

3rd

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Application Performance Monitoring (APM) and Observability (21st), Log Management (15th)

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 Amazon OpenSearch Service is 13.0%, up from 4.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Search as a Service Mindshare Distribution
Product	Mindshare (%)
Amazon OpenSearch Service	13.0%
Amazon Athena	5.3%
Other	81.7%

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.

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Md. Shahariar Hossen

Senior Software Engineer at Cefalo

Event tracking has become smoother and data analytics provide clear insights for user actions

Amazon OpenSearch Service is not providing the processing feature directly. From Amazon OpenSearch Service, we are actually maintaining the AWS SQS, the queue service, which is responsible for providing information about what data has to be modified. So using that SQS, we're actually providing it, but we're not directly using Amazon OpenSearch Service for keeping data to other data pipeline thing. So far we didn't use it for any machine learning purposes, but in future, we have plans to extend or implement this feature. Since AWS itself is secure and Amazon OpenSearch Service is a part of this entire ecosystem, it becomes much easier for security purposes. From the validation point of view, Amazon OpenSearch Service itself provides easy to communicate APIs and up-to-date documents, which is much beneficial. For example, if I'm missing anything, I can directly go and check the documentation. That is actually much easier. I would rate it as really good so far. It's much faster. For our local machine, we can also use a kind of replica of Amazon OpenSearch Service just for development purposes. That is another good feature. I would say for the encryption thing and also the user access control management, it's much faster. For some of these hashing algorithms, it also worked really well so far. To be honest, I didn't find any places where it can be improved. However, I think they could provide more abstraction. For example, still for searching, we have to write down the queries in a specific manner, such as for a specific JSON structure or in a specific way. Otherwise, they don't provide us the actual results. For at least this purpose, I think abstraction could be a bit easier or a bit improved. Other than that, right now there is the age of AI, so some kind of prompting could also work, but I'm not sure how it could be integrated. As a user, lower prices or reasonable pricing is always better. Those can be improved as well. However, it is good that most of the services including Amazon OpenSearch Service actually provide pay as you go pricing. So if there were a bit lower version or a bit less payment methodology, it might be much better.

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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 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."

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

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

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

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

"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."

More Amazon Athena pros

"It enables us to efficiently search and retrieve our event data, offering us a versatile approach to locate specific information within these logs."

"Regarding valuable features of the solution, we found with the process, which we have used in both cases where we used the solution that while you're seeing the streaming of data, you can analyze in the initial phase what sort of data you are streaming and whether it is valuable."

"They have the good documentation in the help text and that is the reason the Amazon Elasticsearch is the perfect solution for the current market."

"The most valuable features of Amazon Elasticsearch are ease of use, native JSON, and efficiency. Additionally, handles many use cases and search grammar was useful."

"The business analytics capabilities are the most important feature it provides."

"It's actually easier to collaborate since it is already deployed in the AWS cloud itself."

"The initial set up is very easy...We really appreciate Amazon!"

"We retrieve historical data with just a click of a button to move it from cold to hot or warm because it's already stored in the backend storage"

More Amazon OpenSearch Service pros

Cons

"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."

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

"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."

"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."

"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."

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

"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."

More Amazon Athena cons

"I would say that, basically, the configuration part is an area with a shortcoming...Some upgradation is required on the configuration side so that we can get to use it."

"The configuration should be more straightforward because we had to select a lot of things."

"The pricing aspect is a concern. The service is way too costly. For the past month, I used only 30 to 40 MB of data, and the cost was $500. AWS could improve pricing."

"It would be beneficial to have some level of customization available in the managed service, tailored to the specific use cases of the end users."

"In terms of data handling capabilities with Amazon OpenSearch Service, they can be complex and managing data in comparison to other SIM solutions is a major drawback, as it is very hard to handle the data."

"The price is fair yet leans towards the expensive side. I'd rate it five out of ten with respect to capabilities vs. cost."

"I want to see a new feature in Amazon Elasticsearch Service that allows users to create default filters for filtered levels."

"They can enhance data visualization."

More Amazon OpenSearch Service cons

Pricing and Cost Advice

"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."

"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."

"Compared to other cloud platforms, it is manageable and not very expensive."

"There is a community edition available and the price of the commercial offering is reasonable."

"You only pay for what you use."

"The solution is not expensive, but priced averagely, I will say."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

12%

Outsourcing Company

10%

Healthcare Company

Financial Services Firm

15%

Manufacturing Company

10%

Computer Software 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	7
Midsize Enterprise	2
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...

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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...

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What is your experience regarding pricing and costs for Amazon OpenSearch Service?

I would consider the pricing as a six based on how much data we are handling; if we handle minimal data, it's cheap, but for large data, it becomes costly. Our clients usually pay between $1,000 to...

See all answers

What needs improvement with Amazon OpenSearch Service?

See all answers

What is your primary use case for Amazon OpenSearch Service?

Amazon OpenSearch Service is a user-friendly version of Elasticsearch, as per my understanding. I have been using it for our volunteer management system where around 5,000 to 6,000 users are using ...

See all answers

Comparisons

Elastic Search vs Amazon Athena

Compared 15% of the time

Amazon Kendra vs Amazon Athena

Compared 15% of the time

Azure AI Search vs Amazon Athena

Compared 10% 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

Grafana Loki vs Amazon OpenSearch Service

Compared 6% of the time

Azure AI Search vs Amazon OpenSearch Service

Compared 6% of the time

Xapien vs Amazon OpenSearch Service

Compared 5% of the time

Grafana vs Amazon OpenSearch Service

Compared 4% of the time

Elastic Search vs Amazon OpenSearch Service

Compared 3% of the time

More Amazon OpenSearch Service Competitors

Product Reports

Buyer's Guide

Amazon Athena

September 2026

Download Amazon Athena product report

Buyer's Guide

Amazon OpenSearch Service

September 2026

Download Amazon OpenSearch Service product report

Also Known As

No data available

Amazon Elasticsearch Service

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)

Amazon OpenSearch Service provides scalable and reliable search capabilities with efficient data processing, supporting easy domain configuration and integration with numerous systems for enhanced performance.

Amazon OpenSearch Service offers advanced features for handling JSON, diverse search grammars, quick historical data retrieval, and ultra-warm storage. It also includes customizable dashboards and seamless tool integration for large enterprises. With its managed infrastructure, OpenSearch Service supports efficient system analysis and business analytics, improving overall performance and flexibility. Despite these features, areas like configuration complexity, lack of auto-scaling, and integration with Kibana require attention. Users seek enhanced documentation, better pricing options, and more flexible data handling. Desired improvements include default filters, mapping configuration, and alerting capabilities. Enhanced data visualization and Compute Optimizer Service integration are also recommended for future updates.

What features define Amazon OpenSearch Service?

Scalable Search: Offers reliable search capabilities across vast datasets.
JSON Handling: Supports efficient management of JSON data.
Ultra-warm Storage: Enables cost-effective historical data retrieval.
Dashboard Customization: Supports tool integration and tailored visuals.
System Upgrades: Allows for seamless infrastructure upgrades.

What benefits and ROI should users consider?

Performance Enhancement: Significant improvement in search and data retrieval.
Cost-Effectiveness: Provides a cloud-native solution optimized for AWS environments.
Comprehensive Analytics: Delivers detailed insights for business applications.

Amazon OpenSearch Service is utilized in various industries for log management, data storage, and search capabilities. It supports infrastructure and embedded management, analyzing logs from AWS Lambda, Kubernetes, and other services. Companies use it for application debugging, monitoring security and performance, and customer behavior analysis, integrating it with tools like DynamoDB and Snowflake for a cost-effective solution.

Amazon Web Services (AWS)

Sample Customers

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

VIDCOIN, Wyng, Yellow New Zealand, zipMoney, Cimri, Siemens, Unbabel

Buyer's Guide

Amazon Athena vs. Amazon OpenSearch Service

September 2026

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