Datadog Reviews

We have used this solution primarily for application performance monitoring. To do this, we needed to make sure we had the right data in the system so that people could be able to monitor their applications end-to-end.

The infrastructure monitoring capabilities are really valuable. You can just log on and see everything that is happening within an IT environment.

Their security features could be improved. We looked at their Security Monitoring feature but it was early in its development. Datadog are just getting into the security space so I'm sure this will improve in the future.

This is a stable solution. 

This is a scalable solution. 

To get started with this solution, I would recommend front-loading it with some sort of data or process and filter out to view only the information you need.

I would rate this solution a nine out of ten. 

I am using the solution for monitoring metrics, logs, traces, etc. It's mainly for making dashboards as well as monitoring our services. 

We also use Datadog to help centralize our incident management to show the logs, where issues spiked, and some metrics. 

We use Datadog to do troubleshooting in Kubernetes, specifically in our Azure Kubernetes service. Beyond that, we are looking to use open telemetry in tandem with Datadog to further our log-tracing efforts. In the future, this may be expanded.

This solution improves our organization as now we have higher visibility into our application that we otherwise would not have. 

Since the Datadog agent comes in three forms, agentless, scraping, and through the API, it is very flexible. It is this flexibility in how to report our logs that keeps our logs centralized and organized. 

One major drawback of Datadog is the cost. Sometimes we set up flows in place to monitor resources that end up logging more than we thought, and the bill is too high.

Dashboards have been marrying the most valuable parts of Datadog. Dashboards use metrics that are very helpful for monitoring services. I recently used metrics to monitor the number of pods in Kubernetes, the spikes in requests in Kubernetes, and overall CPU and memory usage in our Kubernetes clusters. 

We can also use log analytics to further our understanding. We can handle debugging and find out why things are breaking in our applications. 

The log portion of Datadog has robust features to debug the applications we are running. I really appreciate the ability to use facets to par down the logs.

The documentation leaves a lot to be desired for new users. The documentation is way too much text and has no real information just to help get people started. Sometimes it doesn't help to read an entire essay just to get a grasp on how the logs or metrics work.

I've used the solution for two years.

We have deep integration with Datadog for observability and monitoring.

We use everything from APM, logs, and RUM to monitor and dashboards for tracking system health. 

We are trying to move from many different solutions for error tracking/observability to a single platform (Datadog).

We are currently in the process of setting up logging in Datadog in order to maintain our logs better. We are looking to create more insights into the real user flows by using real user monitoring (RUM) too.

We use Datadog quite extensively. I primarily work with APM traces and logs to debug issues and unblock myself in my day-to-day role. I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions, it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality (such as the very useful error-tracking dashboard mentioned in my review above).

As I mentioned above, we use Datadog quite extensively. In my day-to-day role, I primarily work with APM traces and logs to debug issues and unblock myself. 

I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Additionally, the flexibility to create notebooks and dashboards and fully customize them gives us a lot of power to track the exact services and endpoints we are working on.

Furthermore, we are also using monitoring to page us if things break, and the Slack integration provides us instantaneous feedback on how things are performing.

We need more visibility into the error tracking dashboard. I only learned about it during a demo at Dash Con. That said, it seems to be a very useful tool.

Additionally, we want to export our dashboards and monitors to source control, and there doesn't seem to be any easy way to do so.

I've used the solution for four years.

For logging, we are moving from LogDNA to Datadog to have access to everything in one place. Also, searching and traversing through logs seems easier in Datadog

I did not evaluate others, however, my team probably did.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions; it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality. For example, the very useful error-tracking dashboard that I just discovered.

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're in the process of doing a Proof of Concept with the solution right now.

One of the solution's greatest aspects is its overall simplicity. It is very easy to use. It's easier to handle than other brands we have access to.

The monitoring capabilities that the solution provides are very good.

We find they have a very helpful alert system.

The product has been very stable. We've liked the performance provided.

The initial setup is simple and easy to handle. It's not hard at all.

The experience we've had with technical support has been very positive so far. They are helpful.

The integration capabilities have been pretty good overall. We have no complaints.

We haven't used the solution too much yet to assess what features it is missing or would improve. The support in Latin American is a point that would mark as a point to improve

The stability of the solution is excellent. There are no bugs or glitches. It doesn't crash or freeze. It's very reliable in terms of performance.

Technical support has been helpful overall. They are knowledgeable and responsive. We are quite satisfied with the level of service they provide to our company. 

We are actually still using actually Elasticsearch and Kibana, however, we prefer to use Datadog due to the fact that it is very simple. We really enjoy how it operates

We found the initial setup to not be overly complex. It's rather straightforward and easy to execute. A company shouldn't have any issues with the process.

I'm not sure if the license is expensive or cheap. It's managed by the customer and we've recently come on board.

I am an end-user and customer. I don't have any business relationship with the product itself. We work with clients on the infrastructure and IT developer infrastructure mostly. We work with a variety of solutions, including Dynatrace, Datadog, Elasticsearch, etcetera.

In general, due to its simplicity and ease of use, I would rate the solution at a nine out of ten. We've been very satisfied with the solution overall.

We can build dashboards as fast we roll out new systems, which can be fast.

We use standard and custom metrics for every new system we roll out for 360 degree visibility into our systems.

The most valuable features have been: Sharable dashboards, TimeBoards, dogstatsd API, Slack Integration, Event logging API. CloudTrail Events, Tags, alerts, and anomaly detection. EBS Volume Snapshot Age, which they added upon request. We used PagerDuty integration for a while as well.

More granular control over dashboard sharing. Timeboard sharing.

There are infrequent hiccups, which have been decreasing over the time we have used it.

No.

Customer Service:

Never seen better. Questions answered usually almost immediately, even on weekends. An in-stream with your event stream.

Technical Support:

High.

Overall they have always had an amazing team, and quality has been maintained as the company has grown.

Complementary to other tools we used.

Setup is generally easy. They provide an large number of integrations, some are more complex than others, which is to be expected.

In house implementation.

We didn’t calculate explicitly, but as we used the product to track down underutilized instances, it more than paid for itself in the first month.

Pricing overall in this segment has standardized in the last several years.

A few, including Zabbix and Icinga.

One of the fastest and most flexible tools we have used in this area..

We primarily use the solution for charting application metrics.

We use it for all our application metrics, host metrics, and monitors with a PagerDuty integration. 

We integrate our application logs. It is great to be able to tie our metrics and our traces together.

We use the APM module with traces. It is great to be able to link APM, logs, and metrics in one go, as it shortens our troubleshooting and RCA dramatically.

We are loving the tool; it is great to have all those insights in one place. 

We hope that they keep making my life and our engineers' life easier.

The solution improved our organization with:

• Data-driven decision making
• Dashboards we can share with our customer success team
• Dashboards we can share with our sales engineers
• Help during incidents
• Help with preventing incidents
• Integration with PagerDuty.

The most valuable aspects of the solution include:

• The charting application metrics
• help with the business, prioritization, software design, and infrastructure design.

The pricing model hurts and forces us to work around the tool sometimes.

On top of application performance metrics, it would be great to have host performance metrics, suggesting changes to better use a cluster like: "You are over-provisioning this host" or "based on historical data, you will need to scale up in X days."

Adding a module to extract data from Datadog so we can use the data in our own system would be helpful.

I've used the solution for six or more years.

We previously used New Relic, which was a great tool. That said, Datadog is a more complete solution.

The pricing should be less of a surprise. They should allow us to cap costs which would lead to less frustration.

We need better documentation on the pricing.

It might be helpful if they added a pricing simulator.

We primarily use the product for tracing, metrics, and alarms in various deployment environments.

The product has provided our company with improved observability, which has helped make the incident response more targeted and quicker.

APM is great and has provided low-effort out-of-the-box observability for various services. 

Monitors are helpful, and definitions are simple. 

Terraform support is nice as it allows us to create homogenous monitoring environments in various deployment environments with little additional effort. It also facilitates version control of monitor definitions, etc. 

The Golang profiler is generally good with the exception of delta profiles; it has provided helpful observability into Heap Allocations which has helped us reduce GC overhead.

Delta traces on the Golang profiler are extremely expensive concerning memory utilization. In a Kubernetes environment where we would like to set per-pod memory allocations as low as possible, the overhead of that profiler feature is prohibitive. In one case, our pods (which were provisioned to target 250 MB and max at 500 MB memory) got stuck in a crash loop due to out-of-memory, which was caused entirely by the delta profiles feature of the profiler.

Multistep Datadog synthetics lack the feature of basic arithmetic. For our use case, performing basic arithmetic on the output of previous steps to produce input for subsequent steps would be extremely useful.

I've used the solution for nine months.