Amazon OpenSearch Service vs Datadog comparison

Amazon OpenSearch Service

Read 208 Datadog reviews

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14,735 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: April 2026).

Amazon OpenSearch Service vs. Datadog

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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

25th

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

4th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

208

Ranking in other categories

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

Mindshare comparison

As of April 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Amazon OpenSearch Service is 1.2%, down from 1.7% compared to the previous year. The mindshare of Datadog is 4.9%, down from 9.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Application Performance Monitoring (APM) and Observability Mindshare Distribution
Product	Mindshare (%)
Datadog	4.9%
Amazon OpenSearch Service	1.2%
Other	93.9%

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

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

"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."

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

"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."

"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"

"The stability of the product is good."

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

More Amazon OpenSearch Service pros

"Datadog has a very good visualization for my complete infrastructure and network traffic, which enabled me to create a capacity plan."

"I like that you can build out a dashboard pretty quickly. There are some things that come out of the box that you don't really need to do, which is great because they're default settings."

"The visibility that it provides is valuable, helping us be proactive around incident management and get more insight into our customers' applications so that we can assist them at the application layer and even call customers before they know about an issue."

"If you are monitoring the metrics and insights in your application, and need help monitoring, then this is a great application to look into."

"Our return on investment is great and is so much better than CloudWatch."

"Cost and performance optimization were the major enhancements for our organization."

"We find they have a very helpful alert system."

"We have been impressed with the uptime and clean and light resource usage of the agents."

More Datadog pros

Cons

"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."

"One improvement I would like to see is support for auto-scaling."

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

"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."

"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."

"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."

"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."

"They can enhance data visualization."

More Amazon OpenSearch Service cons

"The logging could be improved in the future."

"We need more visibility into the error tracking dashboard."

"In terms of UI, everything is very small, which makes it quite difficult to navigate at times."

"I want to applaud the efforts in making the UI extremely usable and approachable. My suggestion would be to take another look at how the menu structure is put together, however. Even after using the platform mostly every day for months, I still find myself trying to find a service or feature in the menus."

"Sometimes, it takes a long time to load the dashboard if we have many charts."

"Datadog lacks a deeper application-level insight. Their competitors had eclipsed them in offering ET functionality that was important to us."

"As Datadog is a bit on the expensive side, I would recommend it for simple, uncomplicated, solutions."

"Particularly as Datadog starts offering more platform capabilities like APM, Watchdog, shift left initiatives like instrumentation, continuous testing, intelligent test runner, and Synthetic and real user monitoring, the UI can become more and more clunky, giving users a very frustrating experience."

More Datadog cons

Pricing and Cost Advice

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

"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."

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

"It has a module-based pricing model."

"The solution is fairly priced but history and log storage can get costly depending on your needs."

"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 is easy to run up a large bill, so become familiar with the cost of each piece of your bill and use the metrics they supply to estimate and monitor your bill."

"The price of Datadog is reasonable. Other solutions are more expensive, such as AppDynamics."

"Licensing is based on the retention period of logs and metrics."

"Pricing is somewhat affordable compared to other solutions but in order to really lower the costs of other products you need to plan very carefully your resources usage, otherwise, it can get expensive real quick."

More Datadog pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

10%

Computer Software Company

10%

Government

Financial Services Firm

15%

Computer Software Company

10%

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	81
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

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Compared 5% of the time

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Compared 3% of the time

Splunk AppDynamics vs Datadog

Compared 3% of the time

Azure Monitor vs Datadog

Compared 2% of the time

Splunk Enterprise Security vs Datadog

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Product Reports

Amazon OpenSearch Service

Download Amazon OpenSearch Service product report

Download Datadog product report

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

Amazon OpenSearch Service vs. Datadog