Datadog Reviews

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.

We’re currently using logging, monitoring, metrics, APM, etc.  

We've started to use קSLOs. However, it takes a bit of time to work through those.

RUM (Real User Monitoring) has been very useful. I have used this in the past to debug problems in production, which has been great.

We also want to start using synthetics and tracing more. 

Our application currently runs in many different environments based on our customers' requirements. This allows us to see everything in one place and still filter by the environment as required, which is extremely useful. 

The solution has offered increased visibility via logging APM, metrics, RUM, etc. Going from almost nothing to something that didn’t take a lot of time to set up has been great since we have so little time to spare

As a startup, we have limited resources, and no one has enough time for anything. There are so many easy integrations and things configurable by YAML making everything easy to set up without needing a full-time employee. Just configuring monitoring solutions is very desirable. 

The APM is very useful for tracking down anything that’s gone wrong. I’ve been using it to make sure everything is working correctly after deployment, to make sure we don’t suffer performance degradation, and to track down anything that’s gone wrong in real-time  

The logs are great and necessary for any application. Before this, our solution would have been to SSH into a machine and tail log files. This is untenable for many reasons, and one of the first things I wanted to change.

The RUM is useful for seeing how real users interact with our website.

Sometimes it’s difficult to customize certain queries to find specific things, specifically with the logging solution. I’ve used other logging platforms in the past that have extensive and mature query languages, which might not be super friendly to start out with, yet they can be very powerful. I wish there was more of an emphasis on that instead of the UI-based tooling that Datadog provides. Even though it is powerful on its own, UI-based design lacks the elegance, efficiency, and complexity that something like that can provide. 

I've been using the solution for six months or so.

We did not previously use a different solution.

I don’t handle the licensing or pricing.

We did look into Splunk.

The main use case is observability and reliability as part of a platform/delivery engineering solution. We use the product to assist tenants and clients within the company to get more ramped up on SRE/DevOps.

The solution has provided us with a lot more insight into service-level metrics, which is especially useful with APM/tracing. It gives us all-up dashboards and alerts to assist with incident management.

The most useful aspects of the product are the dashboards and APM/tracing. The tools are powerful and intuitive to set up as well.

Custom-level metrics could be improved.

Billing should be more transparent.

I've used the solution for three to four years at multiple companies.

The solution is very stable.

The scalability is fantastic so far.

Technical support has been great so far.

The initial setup is straightforward. The documentation we use is very clean and concise.

We handled the initial setup in-house.

I would advise others to be cautious around custom metrics and be picky when setting them up.

We use an enterprise version of a CMS platform which is enabling businesses to transmit content to their customers. The tool is fully customizable to the end user, including out-of-the-box integrations as well as APIs for custom plugin support. 

Our systems fully manage content using AWS as the back-end cloud provider. Assets are kept in secure buckets and utilize the Kubernetes infrastructure to deliver our product to end users and internal authors. Using the CMS allows for business people to manage content without needing development efforts.

This tool is the sole purpose of my company and the solutions we provide around it. Enabling customers to manage their content in a fast, reliable, and highly user-friendly setup has been critical to our success. 

Offering our product at SaaS, PaaS, cloud, and on-prem editions has enabled us to provide a solution for all types of customers. 

The platform appeals to companies spanning many industries on a global scale. 

Being based in SUI allows us to secure both the EU and other companies more easily.

The enterprise version of a CMS platform enables businesses to transmit content to their customers. 

The tool is fully customizable to the end user, including out-of-the-box integrations as well as APIs for custom plugin support. 

The content is fully managed within our systems using AWS as the back-end cloud provider. Assets are kept in secure buckets, and we can utilize the Kubernetes infrastructure to deliver our product to end users and internal authors. 

Using the CMS allows for business people to manage content without needing development efforts. Allowing this type of setup for business users has been a key reason for our success.

There is always room for improvement when dealing with cloud-based technologies. Mainly, I would say, it's just increasing our offerings to attract various other types of industries and businesses across more fields. 

New features as requested by our existing customers will help make the product better.

The product was self-developed.

We use a SaaS deployment. 

We primarily use the dashboard/metric with many tags.

It makes the system easier to debug. 

We really like the charts and visualization.

Sometimes, it takes a long time to load the dashboard if we have many charts.

I've used the solution for five years.

I'm not sure which version we're using, although I believe it to be the latest. 

We essentially use the solution in advance of performance testing, performance monitoring, and troubleshooting.

The solution affords us many uses when it comes to troubleshooting and application. Should we encounter issues while troubleshooting under load, we can take advantage of Datadog usage metrics to see which method or area is having difficulty, in order that we may resolve the issue. 

The solution has valuable troubleshooting and instrumentation features. 

The setup was a bit complex. 

As Datadog is a bit on the expensive side, I would recommend it for simple, uncomplicated, solutions.

I have been using Datadog for nearly four or five years. 

The solution is sufficiently stable. 

As our company does not have many users who are making use of the solution at present, I have not encountered issues with its scalability. 

I do not have plans to increase the usage at the moment. 

I cannot comment on tech support, as I have not made use of them, although my team members have. It seems okay. 

We mainly work with Datadog, although with Dynatrace, as well. 

The initial setup was a bit complex. 

We hired a separate team to handle the implementation. 

At present, not much staff is needed for the deployment and maintenance. 

I am not in much of a position to address the question of return on investment, although I believe it is for the better that my organization employs the solution.

I do not have knowledge of the licensing costs. 

As Datadog is a bit on the expensive side, I would recommend it for simple, uncomplicated, solutions. 

We are just the users of the solution. There are not many of us in our company. 

The solution is good for complex integations which have lots of downstream and involve multiple combined systems. This is where it is useful. 

I rate Datadog as a nine out of ten. 

• Monitoring
• Analytics
• Tracing
• APM

It's hosted. We don't have to do it, and they handle a large amount of data with backups and all of the other things that we no longer have to manage. 

There are things about it that we would like to be fixed, such as it is taking averages of average. This results in data that we don't expect, but overall we are happy with it.

It is incredibly stable.

We have had no issues with scalability.

We have needed technical support because we were dealing with averages of averages.

The integration and configuration are incredibly simple. The SaaS offering is remarkably easy to set up, especially if you're coming from a Graphite environment or anything that uses a StatsD. Datadog is a custom StatsD client, and it adds additional functionality, like tags, etc. However, out-of-the-box should work with native StatsD, so it is incredibly easy to drop in replace if you are using StatsD for metrics.

In terms of employee time: While the instructor costs were transferred to Datadog, it freed up our engineers to work on things which were of valuable to our business rather than maintaining a service that we don't make money on.

It costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost.

We did look at several vendors. What it came down to is we did not want to manage the metric services ourselves anymore, and Datadog matched what it cost for us to host it ourselves.

Check out Datadog. It is awesome.

The ingestion points are unlimited and support customization. We haven't had anything yet that we haven't been able to integrate with it.

We have only used the SaaS offering, but not AWS nor on-premise.

We use the solution for logs from all our applications. In Datadog, for monitoring logs, our team creates an automation for implementing massive logging in all our systems. Now, we are deploying it in our core systems.

We are using it to indicate issues and alert our operations team. With this, we better monitoring of our applications and logs.

However, the main difficulty is implementing the solutions in our Kubernetes cluster, separated just as logs to the specific namespace as the volume of logs is tremendous.

The solution is useful for monitoring logs. 

We can create an automation for implementing across all our systems. Right now, we are deploying it in our core systems.

The greatest value is integrated monitoring as it is able to quickly detect issues and improve our time to market.

Our main challenge is implementing the solutions in our Kubernetes cluster, separated just as logs to the specific namespace since the volume of logs is tremendous.

I've used the solution for one year.