Datadog Reviews

The most valuable features of Datadog are the flexibility and additional features when compared to other solutions, such as AppDynamics and Dynatrace. Some of the features include AI and ML capabilities and cloud and analysis monitoring 

Datadog could improve the flexibility with AI and ML concepts. This will allow customers to be more leveraged towards publishing.

I have been using Datadog for approximately one year.

Datadog is stable. We did not have a single outage.

I have found Datadog to be scalable.

We have approximately 2,000 users using the solution in my organization.

The support from Datadog is excellent.

I have previously used AppDynamics and Dynatrace. 

Datadog's initial setup is easy because they have helped us come up with the easiest way of instrumenting any of the features which need to be deployed.  We worked on it with their engineers and we were able to happily do it. We have done approximately 60 application monitoring through Datadog since our deployment.

We have a very tiny team of four members that do the maintenance of Datadog.

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

Datadog is far better than any other monitoring tool in introducing any of the new capabilities because they think before Amazon AWS and Microsoft Azure before they introduce the concepts. Datadog is a good tool to have for monitoring your own infrastructure.

I rate Datadog a ten out of ten. 

My customers were using Datadog for monitoring purposes. They were using it only because the solution is running on AWS and it's a microservices-based solution. They were using an application called Dynatrace for their log.

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.

Most of the monitoring tools nowadays are have or are going to have embedded artificial intelligence and machine learning to make monitoring and logging more proactive and intelligent. Datadog has incorporated some artificial intelligence.

The solution does not require a lot of maintenance.

The solution had all the features we were looking for and we were able to create a central dashboard as per our requirements.

All solutions have some area to improve, and in Datadog they can improve their overall technology moving forward.

I have been using Datadog for approximately four months.

Datadog is a stable solution.

Datadog is a highly scalable solution because it is a SaaS solution. Having this solution be a SaaS is one of its most appealing attributes. When the vendor is going to manage data scaling and everything for you, you are only going to use the solution as per your requirements. Autoscaling is a great feature that they have.

The support from Datadog is exellent. If you're stuck on something or you are facing any issue, support from the vendor itself is available. You will receive a response instantly from the vendor on anything related to the requirement,  issues, or feature you are looking for. The responses have always been in a timely manner.

I rate the technical support from Datadog a five out of five.

I have used other similar solutions to Datadog and when I do a comparison between the other tools Datadog is on top, it is great.

Since Datadog is a SaaS solution we had not deployed the Datadog on-premise or in any Cloud. We were using the SaaS solution from the vendor itself. From the provisioning perspective or from the monitoring and dashboard perspective, we were using Terraform to create the typical monitoring as code. Everything was basically automated, we were not doing anything manually.

If someone wants to set up Datadog on-premise or in any of the Cloud machines, they have to consider a lot of things from the auto-scaling perspective.

My recommendation is Datadog is very good. Your team can mainly focus on the development rather than the solution itself. The vendor is going to take care of auto-scaling and maintenance and everything for you.

I rate Datadog a nine out of ten.

We're using the product for logging and monitoring of various services in production environments. 

It excels at providing real-time observability across a wide range of metrics, logs, and traces, making it ideal for DevOps teams and enterprises managing complex environments. 

The platform integrates seamlessly with our cloud services, but browser side logging is a little lagging. 

Dashboards are very useful for quick insights, but can be time consuming to create, and the learning curve is steep. Documentation is vast, but not as detailed as I'd like.

The solution has made logging and tracing a lot easier, and the RUM sessions are something we did not have previously. Datadog’s real-time alerting and anomaly detection help reduce downtime by allowing us to identify and address performance issues quickly. 

The platform’s intelligent alert system minimises noise, ensuring your team focuses on critical incidents. This results in faster Mean Time to Resolution (MTTR), improving service availability. 

It consolidates monitoring for infrastructure, applications, logs, and security into a single platform. This enables us to view and analyse data across the entire stack in one place, reducing the time spent jumping between tools.

Real user monitoring has made triaging any possible bugs our users might face a lot easier. RUM tracks actual user interactions, including page load times, clicks, and navigation flows. This gives our organization a clear picture of how our users are experiencing your application in real-world conditions, including slow-loading pages, errors, and other performance issues that affect user satisfaction. We can then easily prioritize these, and make sure we offer our users the best possible experience.

I'm not sure if this is on Datadog, however, Vercel integration is very limited. 

They need to offer better/more customization on what logs we get and making tracing possible on Edge runtime logs is a real requirement. It is extremely difficult, if not completely impossible, to get working traces and logs displayed in Datadog with our stack of Vercel, NexJs, and Datadog. This is a very common stack in front end development and the difficulty of implementing it is unacceptable. Please do something about it soon. Front end logs matter.

I've used the solution for a little over a year.

Our company has a microservice architecture, with different teams in charge of different services. Also, it is a start, which means that we have to build fast and move very fast as well. So before we were properly using DD, we often had issues of things breaking, but without much information on where in our system the breaking happened. This was quite a big-time sync as teams were unfamiliar with other teams' codes, so they needed the help of other teams to debug. This slowed our building down a lot. So implementing dd traces fixed this

DataDog has many features, but the most valuable have become our primary uses.

Also, thanks to frequent concurrent deployments, the DataDog alerts monitors allow us quickly detect issues if anything occurs.

The monitors can be improved. The chart in the monitors only goes back a couple of hours, clunky. Also, it can provide more info, like traces within the monitors. We have many alerts connected to different notification systems, such as Slack and Opsgenie. 

When the on-caller receives notifications fired by the alerts, we are taken to the monitors. Yet often, we have to open up many different tabs to see logs, traces and info that is not accessible on the monitors. I think it would make all of the on callers' lives easier if the monitor had more data

We've used the solution for three years.

We primarily use the solution for logging and APM, and for real user metrics.

The solution has helped out organization gain improved visibility.

The most useful aspects of the solution include RUM, session replay, and APM.

The product needs a better Datadog agent installation.

I've used the solution for one year.

We previously used App Dynamics.

Before choosing Datadog, we looked at Splunk.

The product is primarily used for the DevOps team. 

It has helped us build pipelines for ops review and other functions.

Datadog is providing efficiency in the products we develop for the wireless device engineering department. We had to provide more developer integration tools and also needed to help in creating easy queries that would help in creating efficient toolsets for management to make decisions based on these metrics.

We need more integration functionality, including certain metrics integration. We should be able to monitor devs and need it to build more monitoring tools and offer leadership metrics.

I've used the solution for almost six months.

We use it to monitor and alert our ECS instances as well as other AWS services, including DynamoDB, API Gateway, etc. 

We have it connected to Pagerduty for alerting all our cloud applications. 

We also use custom RUM monitoring and synthetic tests for both our internal and public-facing websites. 

For our cloud applications, we can use Datadog to define our SLOs, and SLIs and generate dashboards that are used to monitor SLOs and report them to our senior leadership.

Datadog has been able to improve our cloud-native monitoring significantly, as CloudWatch doesn't have enough features to create robust, sustainable dashboards that are easily able to present all the information in an aggregated manner in one place for a combination of applications, databases, and other services including our UI applications. 

RUM monitoring is also something we didn't have before Datadog. We had Splunk, which was a lot harder to set up than Datadog's custom RUM metrics and its dashboards.

I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before. 

It's useful to be able to obfuscate sensitive information by setting up custom RUM actions and blocking the default ones with too much data. 

I also like being able to generate custom metrics and monitors by adding facets to existing logging. Datadog can parse logs well for that purpose. The primary method of error detection for our external website is synthetic tests. This is extremely valuable for us as we have a large user base.

At times, it can be hard to generate metrics out of logs. I've seen some of those break over time and have flakey data available. 

Creating a monitor out of the metric and using it in a dashboard to generate our SLIs and SLOs has been hard, especially in cases where the data comes from nested logging facets.

I've used the solution for two years.

The stability is pretty good.

The solution is pretty scalable! It's hard to set up all the infra (terraform code) required to link private links in Datadog to all of our different AWS accounts.

They offer good support. Solutions are provided by the team when needed. For example, we had to delete all our RUM metrics when we accidentally logged sensitive data and the CTO of Datadog stepped in to help out and prioritize it at the time.

Positive

We previously used Splunk and some internal tools. We switched due to the fact that some cloud applications don't integrate well with pre-existing solutions.

The initial setup for connecting our different AWS accounts via Datadog private link wasn't great. There was a lot of duplicate terraform that had to be written. The dashboard setup is way easier.

We installed it with the help of a vendor team.

Our return on investment is great and is so much better than CloudWatch. We can easily integrate with Pagerduty for alerting.

Our company set up the product for us, so the engineers didn't need to be involved with pricing. 

The pricing structure isn't very clear to engineers.

We looked into Splunk and some internal tools.

We use the product for recording loggers on our various services across different teams. For example, we use logs to keep track of info logs for events and error logs to catch exceptions. 

When users ask us to investigate a situation, we use logs to keep track of events and where the user's code traveled to. We also use synthetic testing and monitoring features to keep track of our many alerts in the production and QA environments.

We use Datadog mainly for debugging purposes. For example, we use it to navigate where the code trace is when an issue arises due to its ability to search through the logs. 

We also use it to address user queries. Sometimes users would ask us a certain question concerning our codebase, we use Datadog to track the code stack and also use time monitoring to get an idea of the time frame around when the use case happened.

The feature I have found to be the most valuable is the filtering feature in logs. It is really easy to type plus and minus to filter out different logs. I use it to navigate the noise. 

I use synthetic tests as well. It is easy to navigate the menu and create tests. 

Much of the UI is very straightforward, and I do appreciate the ability to search for any documentation on the various features when I need to as well. The DASH monitoring boards are nice to give an overview of various performances and allow us to track use cases.

This service could be less costly. Right now, we only keep 15 days worth of logs since we want to be more economical in terms of cost. It would be nice if I had the option to monitor logs beyond 15 days. For APM traces, we only keep a year worth of traces. The UI can be a little more straightforward as well. I found it to have too many options.

I've used the solution for three years.

The stability is good.

The scalability is good.