Amazon OpenSearch Service vs Datadog comparison

Amazon OpenSearch Service

Read 211 Datadog reviews

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19,902 Comparison Views

97% willing to recommend

Comparison Buyer's Guide

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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: September 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

21st

Ranking in Log Management

15th

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

211

Ranking in other categories

Network Monitoring Software (3rd), IT Infrastructure Monitoring (1st), Container Monitoring (3rd), Cloud Monitoring Software (1st), AIOps (1st), Cloud Security Posture Management (CSPM) (6th), AI Observability (1st)

Mindshare comparison

As of September 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Amazon OpenSearch Service is 0.9%, down from 2.0% compared to the previous year. The mindshare of Datadog is 4.1%, down from 7.6% 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	4.1%
Amazon OpenSearch Service	0.9%
Other	95.0%

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

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

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

"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."

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

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

"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."

More Amazon OpenSearch Service pros

"It lets us react more quickly to things going wrong. Whereas before, it might have been 30 minutes to an hour before we noticed something going on, we will know within a minute or two if something is off, which will let us essentially get something back up and running faster for our customers, which is revenue."

"The best way it has helped us is by consolidating all our logs into a single place and making it easier to find errors."

"Enabling customers to manage their content in a fast, reliable, and highly user-friendly setup has been critical to our success."

"The feature I have found to be the most valuable is the filtering feature in logs."

"The dashboards provide a comprehensive and visually intuitive way to monitor all our key data points in real-time, making it easier to spot trends and potential issues."

"It really provides a lot of visibility in terms of how our software is working, and if there are any problems, it surfaces them right away and we get alerts in Slack, making it an essential tool for a company that provides software as a service."

"Customer Service: Never seen better."

"I find the greatest feature is being able to search across logs from various microservices."

More Datadog pros

Cons

"They can enhance data visualization."

"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."

"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."

"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."

"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."

"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."

"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."

More Amazon OpenSearch Service cons

"When I started using it years ago, it had stability problems. I remember, specifically, we ran everything in Docker containers. There were some problems getting it into a Docker container with very specific memory limits."

"One key improvement we would like to see in a future Datadog release is the inclusion of certain metrics that are currently unavailable. Specifically, the ability to monitor CPU and memory utilization of AWS-managed Airflow workers, schedulers, and web servers would be highly beneficial for our organization."

"I'd like to see more flexibility in the customization and they have a few settings which need to be changed but we are unable to make those changes as users or as the administrator."

"Ingesting data from various sources to monitor the log metrics of the system can always improve so that, if something goes wrong, the right teams are alerted."

"For specialized support, it feels like you're under-staffed, having to wait days/weeks for a solution is a big NO-NO."

"Some of the interface is still confusing to use."

"We primarily use the log management functionality, and the only feedback I have there is better fuzzy text searching in logs (the kind that Kibana has)."

"The installation is easy for me. However, if you are new to this solution it might not be so easy."

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."

"It has always scaled for us. Cost scales up too, but that is not necessarily a bad thing. It's reasonable for what they're providing."

"Sometimes it's very hard to project how much it will cost for the monthly subscription for the next month when you add certain features. Having better visibility of the cost would give a better experience."

"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."

"The pricing came up a bit compared to their competitors. It is not that the price has risen, but that the competitors have gone down. They keep adding more features that I would have expected to be baked in at a more nominal price. I have been increasingly dissatisfied with the pricing, but not enough to jump ship."

"This solution is budget friendly."

"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."

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

"The cost is high and this can be justified if the scale of the environment is big."

More Datadog pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Manufacturing Company

10%

Computer Software Company

Outsourcing Company

Financial Services Firm

14%

Manufacturing Company

Outsourcing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	7
Midsize Enterprise	2
Large Enterprise	4

By reviewers
Company Size	Count
Small Business	81
Midsize Enterprise	50
Large Enterprise	100

Questions from the Community

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?

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 ...

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 ...

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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