Sold by: Datadog
Deployed on AWS
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Datadog is a SaaS-based unified observability and security platform providing full visibility into the health and performance of each layer of your environment at a glance.
4.4
Overview
Free trial: Click "Continue to Subscribe" and create a new Datadog account to receive a 14-day free trial of all Datadog features. At the end of your free trial, your account will automatically convert to a paid Pay-As-You-Go plan detailed in this listing.
Datadog is a SaaS-based unified observability and security platform providing full visibility into the health and performance of each layer of your environment at a glance. Datadog allows you to customize this insight to your stack by collecting and correlating data from more than 600 vendor-backed technologies and APM libraries, all in a single pane of glass. Monitor your underlying infrastructure, supporting services, applications alongside security data in a single observability platform.
Prices are based on committed use per month over total term of the agreement (the Total Expected Use).
Highlights
- Get started in minutes from AWS Marketplace with our enhanced integration for account creation and setup. Turn-key integrations and easy-to-install agent to start monitoring all of your servers and resources in minutes.
- Quickly deploy modern monitoring and security in one powerful observability platform.
- Create actionable context to speed up, reduce costs, mitigate security threats and avoid downtime at any scale.
Details
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Quick Launch
Leverage AWS CloudFormation templates to reduce the time and resources required to configure, deploy, and launch your software.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Infra Pro Hosts per hour | Infra Pro Hosts per hour | $0.03 |
Additional Containers per hour | Additional Containers per hour | $0.002 |
Additional Custom Metrics per hour (per 100 Metrics) | Additional Custom Metrics per hour (per 100 Metrics) | $0.008 |
APM Hosts per hour | APM Hosts per hour | $0.06 |
APM Analyzed Spans per hour - 15 Day Retention (Per 1 Million) | APM Analyzed Spans per hour - 15 Day Retention (Per 1 Million) | $2.55 |
Indexed Log Events per hour - 15 Day Retention (Per 1 Million) | Indexed Log Events per hour - 15 Day Retention (Per 1 Million) | $2.55 |
Ingested Logs per hour (Per 1 GB) | Ingested Logs per hour (Per 1 GB) | $0.10 |
Synthetics API Tests per hour (Per 10K test runs) | Synthetics API Tests per hour (Per 10K test runs) | $7.20 |
Synthetics Browser Checks per hour (Per 1K test runs) | Synthetics Browser Checks per hour (Per 1K test runs) | $18.00 |
Serverless Functions per hour (no longer offered) | Serverless Functions per hour (no longer offered) | $0.012 |
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Support
Vendor support
Contact our knowledgable Support Engineers via email, live chat, or in-app messages
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Updated weekly
By LogicMonitor
By Observe, Inc.
Top
25
In Log Analysis
Top
10
In Monitoring and Observability, Migration
Top
10
In Application Performance and UX Monitoring
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Multi-Source Data Integration
Collects and correlates data from more than 600 vendor-backed technologies and APM libraries in a single unified interface
Infrastructure and Application Monitoring
Monitors underlying infrastructure, supporting services, applications, and security data simultaneously within a single observability platform
Unified Observability Dashboard
Provides full visibility into the health and performance of each layer of the environment through a single pane of glass
Rapid Deployment and Setup
Enables quick deployment through turn-key integrations and easy-to-install agent for monitoring servers and resources
Security and Threat Mitigation
Integrates security data and threat detection capabilities to identify and mitigate security threats across the monitored environment
Automated Device Discovery and Configuration
Automatic recognition and configuration of 2,000+ technologies with preconfigured alert thresholds and best practices-based setup without manual intervention
Agentless Monitoring Architecture
Agentless Collector deployment enabling hybrid and multi-cloud visibility with reduced operational overhead and no requirement for continuous agent upgrades
Unified Multi-Environment Visibility
Single-pane-of-glass monitoring across on-premises, hybrid, and multi-cloud infrastructures including AWS services with panoramic performance visibility
Flexible Data Collection Mechanism
Capability to pull metrics from virtually any device or API with support for custom graphs, dashboards, and alerts for application status analysis and trend identification
Performance Forecasting and Customizable Dashboards
Built-in performance forecasting capabilities combined with rich, customizable dashboards and full reporting functionality for actionable infrastructure insights
Data Ingestion and Query Performance
Ingests petabytes of telemetry per day with capability to process hundreds of terabytes and execute tens of millions of queries daily without performance degradation
Knowledge Graph Architecture
Utilizes O11y Knowledge Graph to structure and correlate data across logs, metrics, and traces for fast search and correlation capabilities
Natural Language Processing for Incident Analysis
Enables troubleshooting of complex incidents using natural language queries through O11y AI for accelerated root cause analysis
Open Data Lake Foundation
Built on Snowflake data lake architecture providing open data storage without vendor lock-in
Multi-Signal Correlation
Correlates and correlates telemetry signals across logs, metrics, and traces with context-aware analysis for incident resolution
Standard contract
No
Customer reviews
Yash Dhawan
Log monitoring has reduced costs and enables faster troubleshooting for cloud applications
Reviewed on Mar 18, 2026
Review from a verified AWS customer
What is our primary use case?
The main use case for Datadog is to troubleshoot using application logs, and that is a great use case. Whenever we have integrated Datadog with all the application logs, we receive all the application logs there. Because CloudWatch logs are so expensive to use, we have integrated with Datadog, and it is very cost-friendly. The best use case is whenever we receive an error or suppose we have an issue going on, we can check it using Datadog logs and we can resolve it based on the logs.
What is most valuable?
The integration part is very smooth in terms of Datadog. The best features Datadog offers are first integration, second it is reliable, we can rely on it twenty-four seven, and third it is cost-effective.
Datadog integrates with any of the tools, and for now, we are working on the AWS cloud, so it is very easy to integrate with any of AWS services. We can push any of the logs for any of the applications. Datadog seems to be very reliable and stable, so we can check the logs and check anything there.
In terms of positivity, Datadog is cost-effective. We have used CloudWatch as well sometimes, but CloudWatch is expensive to use. If we want to search for any of the log streams, it is quite expensive. The cost is so much higher for that particular tool. But in terms of Datadog, it is very much cheaper than CloudWatch logs. It is similar to CloudWatch, but it is more cost-effective.
Datadog has helped our team save money, time, and resources, and I am certain that it has saved our time and our money.
What needs improvement?
Datadog can be improved if there is an AI functionality enabled. Let's suppose we are receiving a number of errors; an AI-integrated feature can happen there and it just gives us a root cause analysis based on the report, based on the error logs, and which service and what error codes we have received. That is how we can improve it.
I believe that is something which every organization wants, and I guess that is something really important because everyone wants the root cause after an incident has occurred.
For how long have I used the solution?
I have been using Datadog for the last five years.
What do I think about the stability of the solution?
I have never seen downtime with Datadog. It is pretty reliable.
What do I think about the scalability of the solution?
We can scale N number of things in Datadog. It is pretty scalable with no issues in the scaling part.
How are customer service and support?
I never reached out to customer support because I did not have to. Because we had no downtime for Datadog, we are good.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I have used Kibana before Datadog, but the problem with Kibana was there was so much downtime in Kibana. We used to have so many issues in Kibana and the troubleshooting was impacted due to that. So we switched to Datadog.
How was the initial setup?
I purchased Datadog through the AWS Marketplace .
What about the implementation team?
The implementation was amazing. The pricing is amazing and everything was so smooth with Datadog, so no issues at all.
What was our ROI?
I would say 100% return on investment. Let's suppose if I have to give an example of CloudWatch, it costs so much. If I have to search for two days of logs, it would cost me around three times more than what I search for from Datadog. Datadog is something which I can 100% rely on, and it is very cost-effective and totally worth it.
Which other solutions did I evaluate?
I have evaluated CloudWatch and Kibana, and hence I chose Datadog.
What other advice do I have?
I would recommend creating a data dashboard instead of searching for the logs. That feature is quite useful, so you can use the dashboards and you can monitor it from one place and also you can troubleshoot it from there as well using the logs. That is a very useful thing in Datadog. I give this review an overall rating of nine out of ten.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Jesse S.
Insightful Monitoring, Quick Integration
Reviewed on Mar 11, 2026
Review provided by G2
What do you like best about the product?
I really like how quickly data shows up in Datadog. It's really quick and easy to integrate webhooks with it, and we can search through the results quickly and easily to find examples of integrations working or not working. Being able to dig into API payloads and understand what's causing issues by looking at API responses in Datadog makes troubleshooting a lot easier for me. The ability to build dashboards and metrics to gain insights on our integrations also stands out.
What do you dislike about the product?
Sometimes, once you have searched for something and it has filtered down to a specific context, it can be difficult to know how to expand the context to include other sources.
What problems is the product solving and how is that benefiting you?
I use Datadog to track errors in integrations, troubleshoot export issues, and check API error rates. It provides insights into system performance and helps me build dashboards to monitor API response times and integration status.
Program Development
Very Easy to Use and Incredibly Useful for My Job
Reviewed on Mar 10, 2026
Review provided by G2
What do you like best about the product?
It’s very easy to use and has been really useful for my job.
What do you dislike about the product?
Honestly, there’s nothing I really dislike about it. It’s a very good product overall.
What problems is the product solving and how is that benefiting you?
It’s useful for tracking logs and monitoring custom metrics, and it makes it easier to follow what’s happening over time.
Emilio G.
All-in-One Observability That Speeds Up Root Cause Analysis
Reviewed on Mar 05, 2026
Review provided by G2
What do you like best about the product?
I like the concept overall, the system that tracks every data point your applications provide and you can collect and analyse it in a single space.
It basically allows to find the root cause of issues much faster as you are able to correlate data from different sources (server load, logs, network performance etc.)
And because of all those data agregated in one place you can setup notifications based on multiple metrics together, not just one. Or even do something with webhook.
It basically allows to find the root cause of issues much faster as you are able to correlate data from different sources (server load, logs, network performance etc.)
And because of all those data agregated in one place you can setup notifications based on multiple metrics together, not just one. Or even do something with webhook.
What do you dislike about the product?
I personally don't really enjoy Datadog's interface, it does look modern and UI elements are small, but I don't have any other complaints so far.
What problems is the product solving and how is that benefiting you?
Datadog allows to identify what caused some problem fast, closely monitor systems with useful notifications that based on multiple metrics, and improve and analyse performace of applications I manage.
Abednego Petrus
Unified monitoring has improved incident detection and reduced resolution time across our stack
Reviewed on Jan 22, 2026
Review from a verified AWS customer
What is our primary use case?
Datadog 's main use case is end-to-end monitoring that helps check problems across infrastructure, application, database, security, and logs.
For example, when checking a problem with a mobile application such as an error from a user hitting a transaction, we check from the client-side mobile device and also from the back end for the API to see if there is latency or an error that triggers the problem. There may be an issue on the database, such as a locking query or high latency on query performance. For infrastructure, if the application is slow, it may be impacted on infrastructure monitoring by CPU and memory consumption.
Datadog is a powerful observability tool that allows us to correlate and see problems on the infrastructure or application side. In an incident war room, we can see the correlation and the detailed root cause of the problem across real user monitoring, application, database, and infrastructure.
How has it helped my organization?
Datadog has positively impacted our organization because our customers are very happy using it. With silo monitoring, where infrastructure has separate monitoring, application has another, and cloud has another, it becomes tricky and complex. We cannot correlate the silo monitoring, and pricing is complicated. With Datadog, we can centralize and use one observability tool for monitoring all components across all features or modules, unifying the monitoring process.
Regarding specific outcomes, I observe that tools with Datadog's capabilities enable us to quickly achieve mean time to detect problems. We can specifically check the root cause analysis of issues from the infrastructure, application, database, or security sides. Mean time to resolve is improved with Datadog since it provides many suggestions and actions to resolve problems, which heavily impacts the business for our application customers when issues arise.
What is most valuable?
Datadog's best feature is real user monitoring.
I prefer Datadog's real user monitoring most because of its analytics capabilities. First, Datadog is recognized in the Gartner Digital Experience for real user monitoring. Second, the analytics capability is very powerful, enabling us to check the experience of customers first. We can also correlate with the back-end side of the performance for real user monitoring and application monitoring. Finally, the capability of metrics within real user monitoring provides many helpful insights for mobile developers to improve their mobile application performance.
What needs improvement?
Datadog could improve its pricing because it is very tricky, and most of our customers notice many hidden costs. Additionally, if possible, Datadog should offer deployment options not only for SaaS but also for on-premises solutions, which would benefit banking transactions.
Regarding pricing, it remains tricky with many hidden costs. For technological enhancement, there could be an on-premises option alongside the SaaS version. I also find setting up and configuring Datadog to be very complex.
For how long have I used the solution?
I have been using Datadog for two years.
What do I think about the stability of the solution?
Datadog is very stable, and the features are quickly updated because the research and development process moves swiftly, making it reliable for fixes and updates.
What do I think about the scalability of the solution?
Datadog's scalability is very strong due to its cloud-native distributed architecture, massive data capability, extensive integration ecosystem, seamless expansion, and real-world scalability evidence.
How are customer service and support?
Customer support is very good because there is extensive support from Datadog, including live chat, ticketing, and a very high SLA of 99.98%.
Which solution did I use previously and why did I switch?
I was using Instana and Dynatrace as different solutions before Datadog.
What was our ROI?
I have seen a return on investment because Datadog helps save money and reduces the need for fewer employees while also saving time, which is very beneficial.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup costs, and licensing is that it is very tricky due to many hidden costs, so we need to check repeatedly for allotments and commitments regarding what we receive from the license.
Which other solutions did I evaluate?
I evaluated other options before choosing Datadog, specifically Dynatrace .
What other advice do I have?
My advice for others looking into using Datadog is to initially simplify the technical setup and configuration. Secondly, regarding pricing mechanisms, it would be wise to commit to clear allotments to avoid hidden costs for customers, as it significantly impacts pricing.
I believe Datadog is the largest single observability platform, with correlation as a differentiation factor, enterprise readiness as a measure, and cost management now being a key topic with a very clear roadmap and direction. I would rate this product nine out of ten.