Amazon OpenSearch Service vs Datadog comparison

Amazon OpenSearch Service

Read 211 Datadog reviews

38,856 Views
18,262 Comparison Views

97% willing to recommend

Comparison Buyer's Guide

Download the report

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: June 2026).

Amazon OpenSearch Service vs. Datadog

June 2026

Download the complete report

Helped 906,852 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 OpenSearch Service

Ranking in Application Performance Monitoring (APM) and Observability

24th

Ranking in Log Management

19th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Search as a Service (4th)

Datadog

Ranking in Application Performance Monitoring (APM) and Observability

1st

Ranking in Log Management

4th

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) (5th), AI Observability (1st)

Mindshare comparison

As of July 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Amazon OpenSearch Service is 1.0%, down from 2.0% compared to the previous year. The mindshare of Datadog is 4.6%, down from 9.1% 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	4.6%
Amazon OpenSearch Service	1.0%
Other	94.4%

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.

Read full review

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

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

"I would definitely recommend Amazon OpenSearch Service to other professionals due to its fast and reliable search capabilities."

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

"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."

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

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

"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

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

"It has empowered all our platform engineers with a very powerful and easy to use monitoring system."

"Synthetic testing is by far the most valuable feature in our organization."

"With Datadog I can look at the health of the technology stack and services."

"The most valuable features have been: Sharable dashboards, TimeBoards, dogstatsd API, Slack Integration, Event logging API. CloudTrail Events, Tags, alerts, and anomaly detection. EBS Volume Snapshot Age, which they added upon request."

"Dashboards are helpful for reviewing occasionally to get a higher-level overview of what's happening."

"The technology itself is generally very useful and the interface is great."

"We integrate our application logs. It is great to be able to tie our metrics and our traces together."

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."

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

"As a user, lower prices or reasonable pricing is always better."

"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."

"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."

"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."

"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."

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

More Amazon OpenSearch Service cons

"One thing that I haven't been able to figure out is how to retrieve a report."

"The correlation between the logs and the metrics needs improvement as most cases, we might use another logging tool (that is cheaper in cost) which we then have to link together."

"Datadog can be improved by addressing billing and spend calculation methods, as it would be better if these were more straightforward."

"I'm not sure if Datadog can monitor K8s deployments in real-time. For instance, being able to see a deployment step by step visually. This would be helpful if there were any incidents during the deployment."

"They should continue expanding and integrating with more third-party apps."

"One area where I was really looking for improvement was the CSPM product line. I had really wanted to have team-level visibility for findings, since the team managing the resources has much more context and ability to resolve the issue, as the service owner. However, this has been added to the announcement in a recent keynote."

"Datadog does not have the feature where you can monitor external websites or check the SSL secure for websites."

"The hardest thing we experience is just training people on what to search for when identifying a problem in Datadog, and having some additional training that might be easily accessible would probably be a benefit."

More Datadog cons

Pricing and Cost Advice

"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."

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

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

"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."

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

"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."

"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."

"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."

"The pricing and licensing through AWS Marketplace has been good. It would be nice if it was cheaper, but their pricing is reasonable for what it is. Sometimes, for their newer features, they charge as if it's fully fleshed out, even though it is a newer feature and it may have less stuff than their other items."

"It has a module-based pricing model."

More Datadog pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Application Performance Monitoring (APM) and Observability solutions are best for your needs.

See recommendations

906,852 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

10%

Computer Software Company

Government

Financial Services Firm

15%

Manufacturing Company

Computer Software Company

Outsourcing 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	82
Midsize Enterprise	49
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 ...

Grafana Loki vs Amazon OpenSearch Service

Comparisons

Compared 7% 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 5% of the time

Amazon CloudWatch vs Amazon OpenSearch Service

Compared 4% of the time

More Amazon OpenSearch Service Competitors

Dynatrace vs Datadog

Compared 4% of the time

New Relic vs Datadog

Compared 3% of the time

Splunk AppDynamics vs Datadog

Compared 2% of the time

Azure Monitor vs Datadog

Compared 2% of the time

Splunk Enterprise Security vs Datadog

Compared 2% of the time

More Datadog Competitors

Product Reports

Amazon OpenSearch Service

July 2026

Download Amazon OpenSearch Service product report

Download Datadog product report

July 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