Amazon OpenSearch Service vs Datadog comparison

Amazon OpenSearch Service

Read 210 Datadog reviews

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Executive SummaryUpdated on Nov 16, 2025

Datadog and Amazon OpenSearch Service compete in the observability and analytics category, with Datadog having an upper hand due to its extensive integration capabilities and comprehensive observability features.

Features: Datadog is known for its hosted infrastructure, allowing easy scalability without internal management, integrating seamlessly with platforms like Amazon ECS, RDS, Docker, and Splunk. It excels in monitoring with features like sharable dashboards, alerts, and anomaly detection. Amazon OpenSearch Service, on the other hand, offers powerful search results and analytics features, notable for its customizable dashboards and flexibility in integrating multiple tools.

Room for Improvement: Datadog's challenges include the complexity of data querying and customization of alerts, along with pricing concerns and a steep learning curve for new users. Amazon OpenSearch Service could improve its data visualization and real-time analytics. Its managed nature limits custom configurations, and it would benefit from enhanced support for scaling and better documentation.

Ease of Deployment and Customer Service: Datadog supports a wide range of environments, including private and public clouds, hybrid setups, and on-premises deployments. It receives mixed reviews on customer service, often considered responsive but sometimes inconsistent. Amazon OpenSearch Service supports public cloud environments with solid customer service, though feedback variability exists.

Pricing and ROI: Datadog is perceived as expensive but offers a flexible, scalable pricing model. Despite the cost, users report significant ROI via reduced downtime and improved efficiency. Amazon OpenSearch Service operates on a pay-as-you-go model, noted for cost-effectiveness compared to other solutions. However, pricing remains a concern for larger data requirements. Both platforms provide substantial ROI, with Datadog offering more comprehensive feature benefits.

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

26th

Ranking in Log Management

22nd

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

7.0

Number of Reviews

210

Ranking in other categories

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

Mindshare comparison

As of January 2026, in the Application Performance Monitoring (APM) and Observability category, the mindshare of Amazon OpenSearch Service is 1.6%, up from 1.2% compared to the previous year. The mindshare of Datadog is 5.5%, down from 9.7% 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	5.5%
Amazon OpenSearch Service	1.6%
Other	92.9%

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

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

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

"It's actually easier to collaborate since it is already deployed in the AWS cloud itself."

"It enables us to efficiently search and retrieve our event data, offering us a versatile approach to locate specific information within these logs."

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

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

More Amazon OpenSearch Service pros

"The two most valuable aspects are the Terraform provider for Datadog and the K8s Orchestrator."

"Having a wealth of information has helped us investigate outages, and having historical data helps us tune our system."

"Datadog agents act as an integration to different services, providing easy access and management."

"The infrastructure monitoring capabilities, especially for our Kubernetes clusters, have helped us optimize resource allocation and reduce costs."

"We enjoy the multistep API tests."

"Most of the features in the way Datadog does monitoring are commendable and that is the reason we choose it. We did some comparisons before picking Datadog. Datadog was recommended based on the features provided."

"The web app has a real-time support chat window in which a support engineer is chatting with you within a minute."

"It has provided visibility with ease of implementation and allowed multiple teams to quickly onboard it."

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."

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

"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."

"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."

"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."

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

"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

"The setup was a bit complex."

"The pricing model seems to escalate quickly with increasing metrics ingestion and monitoring across clouds."

"Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time."

"I spent longer than I should have figuring out how to correlate logs to traces, mostly related to environmental variables."

"The solution needs to integrate AI tools."

"We want to reduce having to go to different screens to obtain all the information."

"The sheer amount of products that are included can be overwhelming."

"The FinOps needs improvement."

More Datadog cons

Pricing and Cost Advice

"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."

"You only pay for what you use."

"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."

"It has a module-based pricing model."

"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."

"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 costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost."

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

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

More Datadog pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

11%

Manufacturing Company

10%

Government

Financial Services Firm

14%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	2
Large Enterprise	2

By reviewers
Company Size	Count
Small Business	80
Midsize Enterprise	46
Large Enterprise	98

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?

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 t...

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 20% of the time

Amazon CloudWatch vs Amazon OpenSearch Service

Compared 6% of the time

Xapien vs Amazon OpenSearch Service

Compared 5% of the time

Amazon Athena vs Amazon OpenSearch Service

Compared 4% of the time

Dynatrace vs Amazon OpenSearch Service

Compared 4% of the time

More Amazon OpenSearch Service Competitors

Dynatrace vs Datadog

Compared 9% of the time

Azure Monitor vs Datadog

Compared 5% of the time

New Relic vs Datadog

Compared 4% of the time

Splunk Enterprise Security vs Datadog

Compared 3% of the time

Coralogix vs Datadog

Compared 2% of the time

More Datadog Competitors

Product Reports

Amazon OpenSearch Service

Download Amazon OpenSearch Service product report

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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