Amazon Athena vs Amazon OpenSearch Service comparison

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 8.5, while Amazon OpenSearch Service is ranked #3 with an average rating of 7.3. Amazon Athena holds a 4.8% mindshare in SS, compared to Amazon OpenSearch Service’s 11.3% mindshare. Additionally, 88% 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 9 Amazon Athena reviews

923 Views
923 Comparison Views

88% willing to recommend

Amazon OpenSearch Service

Read 13 Amazon OpenSearch Service reviews

7,043 Views
1,127 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: April 2026).

Buyer's Guide

Amazon Athena vs. Amazon OpenSearch Service

April 2026

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Helped 893,221 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

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 (22nd), Log Management (18th)

Mindshare comparison

As of May 2026, in the Search as a Service category, the mindshare of Amazon Athena is 4.8%, down from 10.1% compared to the previous year. The mindshare of Amazon OpenSearch Service is 11.3%, up from 7.9% 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	11.3%
Amazon Athena	4.8%
Other	83.9%

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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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

"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."

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

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

"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."

"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."

"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."

More Amazon Athena pros

"Amazon OpenSearch Service provides a managed database solution, so we don't need to manage everything ourselves."

"It's a good log management platform. In terms of infrastructure management, it's good."

"The stability of the product is good."

"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."

"Our customers have seen tangible benefits from Amazon OpenSearch Service, especially in terms of their applications running smoothly, so they do get a return on investment."

"AWS has now made our life easy."

"This service already sorts data like vectors. They have classified the storage pre-defined."

"The customer service is excellent, rated nine out of ten."

More Amazon OpenSearch Service pros

Cons

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

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

"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."

"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."

"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."

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

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

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

"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 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."

"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."

"There is a problem with the database. Amazon only provides the hosting to run our applications bias, but there is no option to manage the database within the Elasticsearch product."

"They can enhance data visualization."

"There is the problem with the database. Amazon only provides the host to run to our applications bias, but there is no option to manage the database within the Elasticsearch product."

"Amazon Elasticsearch can improve the bullion in the near search and the ease of integration with Kibana. Additionally, there could be more flexibility in the configuration and documentation."

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."

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

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

"You only pay for what you use."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Manufacturing Company

11%

Computer Software Company

10%

Outsourcing Company

Financial Services Firm

16%

Manufacturing Company

10%

Computer Software Company

10%

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	3
Large Enterprise	2

By reviewers
Company Size	Count
Small Business	7
Midsize Enterprise	2
Large Enterprise	3

Questions from the Community

What needs improvement with Amazon Athena?

I don't have any specific answer on how Amazon Athena can be improved. This integration is more on the Glue side rather than on Amazon Athena, I would guess. Nothing comes to my mind here. In terms...

See all answers

What is your primary use case for Amazon Athena?

The typical use case for Amazon Athena is that we have data in a data lake, and if we need to query the data from the data lake, we use Amazon Athena before it gets to the data warehouse where we w...

See all answers

What advice do you have for others considering Amazon Athena?

I have experience of integration of Amazon Athena with AWS Glue. I think the pricing of Amazon Athena is quite reasonable as we use it in pay-as-you-go mode. On a scale from one to ten, I rate Amaz...

See all answers

What do you like most about Amazon OpenSearch Service?

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

See all answers

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

Comparisons

Amazon Kendra vs Amazon Athena

Compared 22% of the time

Elastic Search vs Amazon Athena

Compared 16% of the time

Azure AI Search vs Amazon Athena

Compared 8% of the time

Solr vs Amazon Athena

Compared 8% of the time

Glean Platform vs Amazon Athena

Compared 6% of the time

More Amazon Athena Competitors

Grafana Loki vs Amazon OpenSearch Service

Compared 9% of the time

Xapien vs Amazon OpenSearch Service

Compared 6% of the time

Datadog vs Amazon OpenSearch Service

Compared 5% of the time

Amazon CloudWatch vs Amazon OpenSearch Service

Compared 4% of the time

Dynatrace vs Amazon OpenSearch Service

Compared 2% of the time

More Amazon OpenSearch Service Competitors

Product Reports

Buyer's Guide

Amazon Athena

May 2026

Download Amazon Athena product report

Buyer's Guide

Amazon OpenSearch Service

May 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

April 2026

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