Datadog Reviews

We use actual user monitoring and have set up thresholds for alerts to PagerDuty, Sentry, Slack, and so on. We also have dashboards set up for tracking latency and error rates. 

As an individual contributor, I also try to set up dashboards for the individual feature projects I work on. I'd like to learn more ways to use this, though, especially when it comes to more proactive approaches to issues. A starter pack of common-use types would be nice.

The solution it has improved our organization by expanding the awareness of issues and alerts beyond SRE and really empowering software engineers at a team level to make changes to monitoring and incident responses.

There could still be more training to bring this even further. A lot of the time I get into Datadog and it's already an incident and I am not in the right mindset to learn about the product or set alerts up.

The feature I have found most valuable is when I can reuse existing monitors and alerts for new dashboards. 

It is nice that things are so integrated, and any individual thing I build out can likely be re-used across the suite.

Other features I have found useful include the stack traces, especially when it links back to something specific I can look for in the code base. I also like that you can group similar alerts together to look for trends over time instead of one in isolation and see if there have been regressions.

The biggest improvement would be around educational content and helping new users get started. I've only been using Datadog for a few months, and at first, it was frankly overwhelming in terms of both the UI and the available capabilities. The workshops today have helped with this, however, more could be done.

As a user, I have about six or more months of experience with the solution. As a company, I'm not sure.

I was not in charge of evaluating any other solutions.

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 use Datadog to monitor our product on the cloud.

The performance of Datadog is good.

Datadog has a lot of features kind of cramped into one dashboard. It's quite hard to get around what feature does exactly what. There was a steep learning curve, trying to navigate through menus.

The menu navigation could improve. If there was a more straightforward way of adding new functions or features to where each menu is placed that would be an improvement.

I have been using Datadog for approximately six months.

The solution is stable.

The scalability of Datadog is pretty good. However, we haven't arrived at the level that we need to test out the scalability.

We have five people using the solution.

I have not used the support from Datadog.

We have not used other solutions previously.

The initial setup is straightforward, and the process took approximately three days.

We have approximately six people that implement and support the solution.

Sometimes it's very hard to project how much it will cost for the monthly subscription for the next month when you add certain features.  Having better visibility of the cost would give a better experience.

There are not any additional costs for the use of this solution.

I would recommend this solution to others.

My advice to others is to use a few features of the solution before going full scale.

I rate Datadog an eight out of ten.

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. 

We use it for notifications, alerting, and capturing most of the information from Amazon, such as EC2 instances.

It has enhanced the performance of my team.

The product could do better with its notifications. 

I want more technical support than conferences because technical support helps with setting up the product much easier.

So far, it has been pretty stable. After we stand up and configure it, it works well.

We have managed to get up to 350 hosts in one of the clusters, and it works fine.

Datadog's support is pretty good.

The integration and configuration of the product in our AWS environment was easy. This was one of the many things that I liked about Datadog.

I have not seen ROI out.

We chose Datadog over the other products that we evaluated because it had better features: notifications, alerting, and metric capture. Also, Datadog had the skill sets that we wanted at the time.

Try out some of the other products in comparison. This is a good product if you are looking for notifications and custom metrics.

We have always used the cloud version of this product.

This product also integrates with Slack and PagerDuty.

We use it to monitor our infrastructure, particularly our different EC2 instances, and our containers. We also use it to capture our logs.

We have a better grasp of what is occurring during the deployment cycle. If something fails, we have an idea what has failed, where it has failed, and how it failed to better mitigate the situation.

It is a good one stop location where we keep all our data for our infrastructure, and it's also easier to navigate between different things.

We want to reduce having to go to different screens to obtain all the information. However, they are moving in the right direction from what we have noticed.

Stability has never been an issue. We throw all of our servers and containers at it. We have now started to throw our on-premise logs at it too.

At the beginning, when we started throwing logs at it, there was a bit of hiccup. However, this was during their beta period, so hiccups were expected.

It pretty much vacuums up any information that we throw at it. So, stability hasn't been an issue.

It scales depending on the time of year. Right now, we have about 25 to 50 instances, and in each instance there are probably five different containers, not including logging for all those containers.

We used their technical support, especially during rollout. They were really good. We worked hand in hand to try to figure out how to configure everything.

For the monitoring of different EC2 instances, you install them into Datadog. 

We use Chef to install Datadog's package, then that calls out all the information from the instance.

We did evaluate other vendors.

We chose Datadog because we were looking for an all-in-one package. They also do log caching and integrate with other systems well.

Take advantage of Datadog's trial period, and really beat it up, then give them a call.

We use the web service for this product.

We are providing managed services to our customers across multiple industries. Datadog is key to delivering these services by bringing the observability, monitoring, and alerting capabilities we need to operate at scale. 

We operate custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence. 

Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog allows us to operate more with less with good alerting right in time.

We are providing managed services to our customers across multiple industries. 

Datadog is key to delivering these services. It brings in observability, monitoring, and alerting capabilities - all of which we need to operate at scale. 

We operate custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence. 

Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog, help with getting alerts in real-time.

We are providing managed services to our customers across multiple industries. 

Datadog delivers observability, monitoring, and alerting capabilities we need to operate at scale. 

Operating custom cloud native workloads as well as ISV products such as Atlassian Jira or Confluence is also something we do. Integrating Synthetics, infrastructure, and application performance monitoring, as well as piping all logs through Datadog allows us to operate while grabbing alerts in real-time.

The current way accounts are billed could be vastly improved - especially when involving multiple organizations across multiple accounts in combination with reserved commitments. 

Being able to have an automatic materialized report on certain dashboards that could be exported as PDF to be shared with non-Datadog users could help a lot. 

Other than that, we are more than happy with the features we use regularly.

We have been using Datadog since 2015.

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.