Amazon OpenSearch Service vs Datadog comparison

Amazon OpenSearch Service

Read 208 Datadog reviews

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13,406 Comparison Views

97% willing to recommend

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Executive SummaryUpdated on Feb 22, 2026

Datadog and Amazon OpenSearch Service compete in the monitoring and analytics category. Datadog seems to have the upper hand due to its integration capabilities, ease of use, and comprehensive monitoring solutions.

Features: Datadog offers extensive monitoring with strong integration, including sharable dashboards, timeboards, and various APIs. Slack integration and ease of use are significant advantages. Amazon OpenSearch Service excels in search and analytics features, providing robust log analysis and data management suitable for handling large data volumes.

Room for Improvement: Datadog users desire better performance with older data, more detailed metrics, and flexible dashboard sharing. Amazon OpenSearch Service could benefit from improved auto-scaling support, configuration flexibility, and more cost-effective solutions for large datasets, along with clearer documentation for easier use.

Ease of Deployment and Customer Service: Datadog is praised for cloud deployment flexibility across various configurations, with good customer support but occasional feedback on response speed. Amazon OpenSearch primarily serves cloud environments, limiting flexibility for on-premises setups, yet offers quick resolutions and user satisfaction through proactive customer service.

Pricing and ROI: Datadog uses a module-based pricing model that offers flexibility but can become expensive due to extensive features, providing good ROI through efficiency improvements. Amazon OpenSearch Service has a pay-as-you-go pricing model, appealing yet potentially costly for large-scale data, offering savings on infrastructure management despite higher initial costs.

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

Amazon OpenSearch Service vs. Datadog

January 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 OpenSearch Service

Ranking in Application Performance Monitoring (APM) and Observability

23rd

Ranking in Log Management

19th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Search as a Service (3rd)

Datadog

Ranking in Application Performance Monitoring (APM) and Observability

1st

Ranking in Log Management

3rd

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

208

Ranking in other categories

Network Monitoring Software (3rd), IT Infrastructure Monitoring (2nd), Container Monitoring (1st), Cloud Monitoring Software (2nd), AIOps (1st), Cloud Security Posture Management (CSPM) (5th), AI Observability (1st)

Mindshare comparison

As of February 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Amazon OpenSearch Service is 1.5%, up from 1.3% compared to the previous year. The mindshare of Datadog is 5.3%, down from 9.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Application Performance Monitoring (APM) and Observability Market Share Distribution
Product	Market Share (%)
Datadog	5.3%
Amazon OpenSearch Service	1.5%
Other	93.2%

Application Performance Monitoring (APM) and Observability

Featured Reviews

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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Dhroov Patel

Site Reliability Engineer at Grainger

Has improved incident response with better root cause visibility and supports flexible on-call scheduling

Datadog needs to introduce more hard limits to cost. If we see a huge log spike, administrators should have more control over what happens to save costs. If a service starts logging extensively, I want the ability to automatically direct that log into the cheapest log bucket. This should be the case with many offerings. If we're seeing too much APM, we need to be aware of it and able to stop it rather than having administrators reach out to specific teams. Datadog has become significantly slower over the last year. They could improve performance at the risk of slowing down feature work. More resources need to go into Fleet Automation because we face many problems with things such as the Ansible role to install Datadog in non-containerized hosts. We mainly want to see performance improvements, less time spent looking at costs, the ability to trust that costs will stay reasonable, and an easier way to manage our agents. It is such a powerful tool with much potential on the horizon, but cost control, performance, and agent management need improvement. The main issues are with the administrative side rather than the actual application.

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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

"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."

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

"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!"

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

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

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

"In case there is a failure, Elastic manages everything well, and there no major downtime."

More Amazon OpenSearch Service pros

"Integrating Datadog with other platforms has made our monitoring processes a bit easier. It's not super simple, but it's manageable."

"Datadog has significantly improved our organization’s visibility into system performance and application health, and the real-time dashboards and alerting capabilities have helped our teams detect issues faster, reduce downtime, and improve response times."

"The ease of use allowed me to get up to speed with log management since it's my first time using Datadog."

"We have been able to set very specific CPU and memory alerts, at the very base level, then we started to pull real business value, like 99th percentile response rates for our API calls."

"The fact that everything is under a single pane of glass is really valuable, as developers don't have to spend their time copying correlation IDs across tools to find what they need."

"Having a clear view, not only of our infrastructure but our apps and services as well, has brought a great added value to our customers."

"The integration into AWS is key as well as our software is currently bound to AWS."

"The service catalog helped improve our organization by giving a good view of the flow for our microservices applications."

More Datadog pros

Cons

"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."

"One glaring issue was with our mapping configuration as the system accepted the data we posted, but after a few months, when we attempted complex queries, we realized the date formatting had become problematic."

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

"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."

"They can enhance data visualization."

"We faced documentation challenges during integration after migrating from Elasticsearch to Amazon OpenSearch Service. Better documentation on integration, query handling, and a more user-friendly UI could enhance the product."

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

"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."

More Amazon OpenSearch Service cons

"The UI has a lot going on. It should be simpler and have a better way to onboard someone new to using Datadog."

"Datadog can be improved because sometimes it seems it has not been developed for enterprises."

"I often have issues with the UI in my browser."

"I'm not sure what kind of features are in the roadmap right now, but I encourage the development of features for defining your organization, and allowing the visibility of what kind of metrics you can get. Those features would be really useful for us."

"The pricing nowadays is quite complex."

"The query performance could be improved, particularly when handling large datasets, as slower response times can hinder efficiency."

"My experience with pricing, setup cost, and licensing is that it is really expensive."

"There is always room for improvement when dealing with cloud-based technologies. Mainly, I would say, it's just increasing our offerings to attract various other types of industries and businesses across more fields."

More Datadog cons

Pricing and Cost Advice

"You only pay for what you use."

"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."

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

"This solution is budget friendly."

"The tool is open-source."

"While it is an expensive product, I would rate the pricing level at four out of five."

"Pricing seems reasonable. It depends on the size of your organization, the size of your infrastructure, and what portion of your overall business costs go toward infrastructure."

"If you do your homework, you'll find that if you're really concerned with cost, it's good."

"I am not satisfied with its licensing. Its payment is based on the exported data, and there was an explosion of the data for three or four weeks. My customer was not alerted, and there was no way for them to see that there has been an explosion of data. They got a big invoice for one or two months. The pricing model of Datadog is based on the data. The customer was quite surprised about not being alerted about this explosion of data. They should provide some kind of alert when there is an increase in usage."

"It didn't scale well from the cost perspective. We had a custom package deal."

"Datadog does not provide any free plans to use the solution. When I start with a proof of concept it would be sensible to have a free plan to test the tool and check whether it fits the requirements of the project. Before the production stage, it is always good to have a free plan with some limited features, number of requests, or logs."

More Datadog pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

10%

Manufacturing Company

10%

Government

Financial Services Firm

14%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	80
Midsize Enterprise	46
Large Enterprise	99

Questions from the Community

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

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...

What needs improvement with Amazon OpenSearch Service?

Any advice about APM solutions?

There are many factors and we know little about your requirements (size of org, technology stack, management systems, the scope of implementation). Our goal was to consolidate APM and infra monitor...

Datadog vs ELK: which one is good in terms of performance, cost and efficiency?

With Datadog, we have near-live visibility across our entire platform. We have seen APM metrics impacted several times lately using the dashboards we have created with Datadog; they are very good c...

Which would you choose - Datadog or Dynatrace?

Our organization ran comparison tests to determine whether the Datadog or Dynatrace network monitoring software was the better fit for us. We decided to go with Dynatrace. Dynatrace offers network ...

Grafana Loki vs Amazon OpenSearch Service

Comparisons

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Compared 6% of the time

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Compared 5% of the time

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Compared 4% of the time

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Compared 4% of the time

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Compared 8% of the time

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Compared 4% of the time

Azure Monitor vs Datadog

Compared 4% of the time

Splunk AppDynamics vs Datadog

Compared 3% of the time

ScienceLogic vs Datadog

Compared 2% of the time

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Product Reports

Amazon OpenSearch Service

February 2026

Download Amazon OpenSearch Service product report

Download Datadog product report

February 2026

Also Known As

Amazon Elasticsearch Service

No data available

Overview

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)

Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.

Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.

What are Datadog's Key Features?

Sharable Dashboards: Facilitate collaboration with customizable, intuitive dashboards.
Extensive Integrations: Seamlessly connects with tools like Slack and AWS to support workflows.
APM and RUM: Provides deep insights into application performance and user interactions.
Real-Time Tools: Enables agile responses to system issues with real-time monitoring.
Unified Tagging and Visualization: Eases management of complex systems with simplified visualization.

What Benefits and ROI Should Users Look For?

Efficient Troubleshooting: Centralized logging speeds up the identification and resolution of issues.
Streamlined Workflows: Integrations with popular tools enhance operational efficiency.
Improved Observability: Comprehensive monitoring improves system reliability and performance.
Resource Optimization: Efficient microservices and container management leads to better cloud resource utilization.
Responsive Alerts: Customizable alerts ensure timely identification of critical issues.

In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and proactive issue resolution.

Sample Customers

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

Adobe, Samsung, facebook, HP Cloud Services, Electronic Arts, salesforce, Stanford University, CiTRIX, Chef, zendesk, Hearst Magazines, Spotify, mercardo libre, Slashdot, Ziff Davis, PBS, MLS, The Motley Fool, Politico, Barneby's