Datadog Reviews

We use the solution for testing all of our application's endpoints. It is making sure that they work on a consistent basis.

We enjoy the multistep API tests.

They need to implement template variables into the message response body. They could be injected in the subsequent calls. However, they fail to be able to use those variables anywhere in the alert body message that is sent out.

We've been using the solution for a year.

I've never thought about stability and have no insights.

I find we are getting tossed from engineer to engineer. It is not fun when you have an ongoing problem. That is something from the customer resolution team that needs to be addressed.

We previously used Selenium. I was forced to use it. However, Datadog was a way simpler solution to setting up browser and API tests quickly.

The solution is very straightforward since it has a good GUI.

The initial setup was handled in-house.

It is not my job to track the ROI.

I have no details about the pricing. 

We did not evaluate other solutions. 

The solution is a SaaS.

We use this solution to monitor our Kubernetes clusters, nodes, deployments, daemon sets, replica sets, and pods.

I've been using this solution for a few months at my current company as a member of the Kubernetes team we use Datadog to provide monitoring and telemetry for our team and our customers.

This solution has improved our organization by giving us deeper insight into what's running in our clusters and their performance of it.

I have found the logging and tracing features the most valuable.

The documentation could be improved regarding setting up the agent properly and debugging.

I've been using this solution for a few months at my current company.

We primarily use the solution for monitoring applications and informing customers via Pagerduty and Statuspage. The monitoring and alerts can be personalized internally, and we are able to find problems and issues. The response time monitor has been great, and it has been validating upgrades. We can check in to see which step fails,

Previously, we had monitors scattered with different places and products, making troubleshooting harder and slower. Also, logs and monitors were on different platforms, making it harder to put the infrastructure puzzle together.

Datadog documentation on web pages has improved a lot and is pretty easy to follow and find.

Additionally, integrations with, for example, GCP, Network, component, and Software providers are much easier as everything is now centralized.

API and notification integrations are also a great benefit for our organization.

Datadog is listening actively for customer feedback and develops improvements for us effectively.

The most valuable features of the solution include the APM, log monitor, SQL monitors, network monitors, and integrations.

Alerting timing should be improved to be more fine-tuned and exact. The current problem is that monitoring is integrated with the Statuspage and the SLA.

Also, browser support for browsers other than Chrome should be added. Browser test recording is another problem, as it does not always work in normal mode. One needs to use incognito mode or a pop-up.

I've been using the solution for three years.

We are using the solution for scaling up the website for market data applications. EC2 and Datadog have enabled high-level monitoring of underlying infra and services.

The Datadog profiler comes in handy to pinpoint issues with resource utilization during peak hours, and traces/log management helps narrow down the root cause.

The network map is crucial in identifying bottlenecks and determining what needs more attention.

Host map helps identify problematic hardware and devise ways to counter issues that arise during scaling, and deploying solutions on the cloud.

While my team is relatively new to Datadog, I already see immense value in switching over to Datadog as the primary APM and NPM tool.

The arsenal of features it offers is bound to come in a clutch when facing production issues, and when finding out what went wrong is crucial.

The network map has helped to figure out the golden signals and optimize the infrastructure.

The synthetics have helped ensure the high availability of arch functions as intended.

The network map is useful. With it, we have the ability to see the data flow across the entire network path across all the applications is highly valuable as the data from this service helps identify network bottlenecks, non-performant applications, and bad endpoints.

This is especially crucial for a high-availability website aimed at market data applications where low latency is crucial.

The host map gives a clear picture of the entire infrastructure, and the ability to switch between logs, metrics, and traces is very handy when it comes to debugging issues on the fly.

I love the ability to install the integrations and agents quickly. This is a well-made product.

To be very fair, I haven't had enough experience with Datadog to pick out improvements.

My involvement with Datadog has largely been positive. I love the simplicity and intuitiveness it offers - even for nontechnical folks who just might be starting out with developing technical chops in their domain.

I've used the solution for three years.

Ingesting data from various sources to monitor the log metrics of the system and enabling an alert mechanism to notify the teammates if something goes wrong.

More specifically, having Datadog agents as integration to different services provides easy access and management.

The solution has helped out organization by allowing us to ingest data from various sources to monitor log metrics and enabling alert mechanisms to notify teams if something goes wrong.

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

The solution is useful for ingesting data from various sources. This helps to monitor the log metrics of the system. It has alert mechanisms that can be enabled to notify the teams if something goes wrong.

Datadog offers good integration to different services. It provides for easy access and management.

Ingesting data from various sources to monitor the log metrics of the system can always improve so that, if something goes wrong, the right teams are alerted. 

We've used the solution for close to 1.5 years.

We use a SaaS deployment.

We use Datadog incident management for our incident tooling. Whenever we run into an incident, we try to use it. It allows us to create a separate Slack channel for it.

Keeping track of incidents makes it possible to share learnings and follow-up. It is also required for SOC2 compliance.

It is great that creating an incident is possible from Slack while having all the relevant data in Datadog. It allows us to not think too much about organizing incidents, which is a highly stressful situation.

I would like the tooling to have better integration in Slack, specifically sending out reminders to the relevant people to take breaks, do a retrospective, and specify with emojis which messages to log.

Learning about the tooling could also be improved. It is a bit clunky to use, especially in an incident. It could definitely be more streamlined.

I've used the solution for about a year.

We primarily use the solution for the RUM, security monitoring, and streams.

We need to monitor users and what they access. We also need to identify security loopholes and attack patterns and identify and quickly respond to issues.

We can identify pushbacks, and get insight into application components that stack up with each other. We can understand which components, libraries, and code to alert teams.

Using Datadog, we can raise incidents, track incidents to completion, and be able to gather data for reporting and post-mortem.

The solution allows us to track fixes and tracks their test coverage. With it, we get confidence in the fix/improvement phase and be able to provide a response.

The solution has improved the organization by providing good insights into app performance and offering good dashboards.

With it, our company can track fixes and track test coverage. We get confidence in the fix/improvement and are able to provide a response.

I've been able to present data to the team/ management based on the team's dashboards.

It's helped us when we've needed to monitor users and what they access or needed to identify security loopholes and attack patterns. It can help identify and quickly respond to issues.

Datadog allows us to identify pushbacks, and get insight into application components (how they stack up with each other). When we need to know which component, libraries, code, and teams to alert, we can raise and track incidents to completion and gather data for reporting and post-mortems.

The APM and container monitoring are excellent aspects of the solution.

It helps with monitoring users and what they access.

We can identify security loopholes and attack patterns while quickly responding to issues.

Our team can now identify pushbacks and get insights into application components. We can gather data for reporting and post-mortem, and we can track fixes and test coverage. Datadog allows us to gain confidence in the fix/improvement.

I've been able to present data to the team/ management based on the team's dashboards.

We'd like Datadog to make the log storage cheaper. Right now, it makes our life difficult since logs are stored separately.

We should not have to rely on team members to educate themselves in Datadog features. There should be templates that anyone can select, and they should be able to create dashboards easily. This is really slowing us down. It takes time to to explore the full potential of Datadog.

I've used the solution for two years.

After a security incident, we needed to find and migrate to a different cloud provider, and after evaluating different competitors and the skill set of the team, we decided to move to AWS. AWS also enables the team to have finer control over how our apps are deployed and how security and access are managed. By leveraging AWS's functionality, we have increased our application's security and sped up the deployment process. We've even been able to handle higher workloads due to AWS's auto-scaling functionality.

Elastic container services have improved our organization by allowing us to deploy our application. While this problem was not solved by using the elastic container service since we had a previous solution on a different provider, the ease and flexibility of deploys have greatly benefited the ops team and the overall engineering organization. It is easy to use. However, there are many out-of-the-box features like metrics and seeing task definitions that make life easier.

The most valuable feature I have found is the elastic container service. That is where our apps get deployed. This feature is really important to us since, without it, we would not be able to deploy our apps the way we wanted to and would have to use a different vendor. 

This service is also valuable to our team as we have a lot of familiarity with it and adapting to the services from our previous one was fairly seamless. 

Another feature that is very valuable for our team is the IAM. It allows us to control security.

ECS could be improved by including more tutorials for beginners to reduce the barriers to entry. It would help to ensure understanding and learning in terms of how to use a cloud provider. Besides this, there are not any other areas of improvement needed since the service is very straightforward and there is ample documentation.

I've used the solution for one year.

We use metrics to track the metrics of our application. We use logging to log any errors or erroneous application behavior as well as successful behavior. We use events to log successful steps in our pipeline or failed steps in our deployment. We use a combination of all these features to diagnose bugs. 

It makes it much more efficient to look at all the data in one place. This speeds up our development speed so that we can be agile.

This spectrum of solutions has allowed us to track down bugs faster and more rapidly, which allows us to limit revenue lost during downtime. 

It also allows us to accurately record and project current and future revenue by measuring the application's metrics. This way, my team can accurately and rapidly create reports for upper management that are easy to read and understand. 

Datadog is also easy to read by non-technical personnel. This way, if there are any erroneous readings, everybody has a chance to find them.

We use metrics to track the metrics of our application. We use logging to log any errors or erroneous application behavior as well as successful behavior. We use events to log successful steps in our pipeline or failed steps in our deployment. 

We use a combination of all these features to diagnose bugs. It makes it much more efficient to look at all the data in one place. This speeds up our development speed so that we can be agile.

These features are the features that I use the most since it is incredibly difficult to track down intermittent bugs if I were to look directly under the hood in a CLI.

Datadog could make their use cases more visible either through their docs or tutorial videos. There are different implementations of certain features that we utilize to customize Datadog functionality and in that way, we sometimes get results that are not conducive to what Datadog thinks their features' use cases are.

I've used the solution for at least one year.

We have only used Datadog. We did not previously use a different product.

Log aggregation for us was a key component since we have a fairly old-school app running on VMs on bare metal. We previously didn't have much insight into our logs unless we manually tunneled them into each server.

The solution is reducing manual labor in troubleshooting problems in our environments server by server.

We also needed to monitor our Java app and MySQL database to understand their problems so that we could take action and resolve them.

Our use cases have since expanded to encompass all aspects of monitoring.

Before Datadog, all we had to go on was the gut reaction of the old guard on our team. While useful, the reactions and inherent knowledge only benefited a few folks.

Datadog has allowed us to create comprehensive dashboards and proactively send out alerts. We used the knowledge of people very versed with our products to help set up the platform and have since benefited from that.

The operative word here is visibility, and we've seen a huge improvement in that.

Seeing log trends and patterns and aggregate search was a huge first step for us. We then began using other features of the Datadog platform by enabling APM. After that, we did other integrations.

For us to have visibility into our app stack and the hardware we run has been highly beneficial.

We leverage APM, log management, and at least ten other integrations. Our DB, web servers, network, storage, and other areas are now monitored and hooked up to dashboards.

Dashboarding has also proven useful when information is going to be viewed by anyone in the organization.

Our experience has been overwhelmingly positive so far. That said, there is one area that could benefit from some polish. For example, I want to applaud the efforts in making the UI extremely usable and approachable. My suggestion would be to take another look at how the menu structure is put together, however. Even after using the platform mostly every day for months, I still find myself trying to find a service or feature in the menus.

I've used the solution for around six or eight months. We've had the Datadog agents deployed on our various environments.

So far, we have not had any issues with stability. It should be very stable and easy to update.

The solution is currently deployed on a limited scale. That said, we see the potential and benefits of deploying this in a cloud scenario.

Customer service and the support teams have been very responsive when we need them. They are very professional.

Positive

This was our first solution in this space.

The initial setup steps with the agent are only confusing when using the config files for the first time. The main file includes a lot that you can specify elsewhere and it's not readily apparent which one to use until you dig in more.

We did an in-house implementation.

Our ROI with Datadog has been very high. It's given us the ability to see how we're performing, which we didn't have before.

Ensure you have your ingestion pipelines dialed in, or you'll likely spend more than you were expecting.

We evaluated free and open-source options, however, ultimately, we decided that we didn't have the manpower as a small company to maintain them.

There is nothing that the documentation cannot help with; it's very good.