
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.
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You can now purchase comprehensive solutions tailored to use cases and industries.
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Pricing
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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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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Customer reviews
Centralized monitoring has improved cloud observability and reduces manual debugging efforts
What is our primary use case?
My main use case for Datadog is to monitor the logs and capture metrics like CPU metrics, memory, and traces across different services in a cloud-based monitoring system where I initially worked, specifically to debug failing systems and systems which are slow, mainly for monitoring my servers in AWS .
What is most valuable?
The best features of Datadog for me are the user-friendly real-time dashboard and its ability to easily integrate with AWS , Azure , Kubernetes , Kafka, and provide a centralized log management system, which gives me excellent visibility into the microservice architecture.
Datadog has impacted my organization by providing a centralized monitoring system so that each person can trace what is happening in the VM servers, and it has given us a centralized dashboard view.
Since adopting Datadog, it has reduced the manual effort by around seven to eight hours per week, making the process completely automated.
Datadog has improved the collaboration across the teams and cross-functional teams, making it very fast and allowing us to easily track what is wrong.
What needs improvement?
If I could change one thing about Datadog, it would be the pricing, as it has extraordinary functionality, but the pricing is somewhat expensive, and as we increase the number of servers and monitoring services, the cost increases. A more predictable and flexible pricing structure would be beneficial, along with additional customization options and reporting features.
For how long have I used the solution?
I have been familiar with Datadog for more than two years.
What do I think about the stability of the solution?
I have not yet faced any frustration with Datadog.
Which solution did I use previously and why did I switch?
Before I landed on Datadog, I used to review the CloudWatch logs in AWS, and we initially had the tool Checkmk for monitoring.
How was the initial setup?
When I first implemented Datadog, it took me around thirty to forty minutes for the basic setup because we had a very large application to monitor metrics. After the configuration, the data actually appeared within three to four minutes.
What about the implementation team?
We did not have any formal training on Datadog. Instead, we referred to Google documentation regarding what Datadog is, how to set it up, and what the use cases are, and based on that, we initially set up Datadog.
Which other solutions did I evaluate?
When evaluating options before choosing Datadog, I compared it with tools such as New Relic and Grafana Labs with Prometheus. The main reason I chose Datadog is that it is a single platform where I can see metrics, logs, traces, and alerts, and it easily integrates with Kubernetes and other services such as Kafka.
What other advice do I have?
Our workflow is both team-wide and individual, as we check the end-to-end observability and the monitoring of our end-to-end application, infrastructure, and cloud services individually as well as in a team.
When I open Datadog, the first thing I do is see the home dashboard, which will have the active alerts and the system health status, as well as listing out all the monitored resources, including the servers, virtual machines, Kubernetes pods, and nodes. I will also see the CPU usage and memory usage, including the disk utilization.
Datadog is used by the cloud infrastructure monitoring team and the application team within the company, and everyone uses it on the same level as I do.
I have not experienced any features during implementation of Datadog that I am not really using in practice.
As of now, for my use case, I am satisfied with what Datadog offers, and I do not wish for any specific features that it currently lacks.
My advice to someone considering Datadog who has a similar workflow to mine is to read the entire documentation and work on it. I would rate my overall experience with Datadog as an eight out of ten.