Datadog Reviews

We use it for custom metrics of our applications and monitoring of our systems.

My current company didn't have very good monitoring in the past. We had been using basic CPU monitoring. We have been able to set very specific CPU and memory alerts, at the very base level, then we started to pull real business value, like 99th percentile response rates for our API calls. 

It has turned into an operational dashboard. If you felt something is going wrong, you can immediately open up Datadog. It has been our go to application because we know the answer will be there.

Their interface is probably one of the easiest things to use because it lets non-developers and non-engineers quickly get access to metrics and pull business value out of them. We could put together dashboards and give it to people who are non-technical, then they can see the state of the world. 

They have a very good ecosystem for their integrations. They have a lot of different integrations, and we use a lot of them. We have integrations with Amazon for ECS, RDS, and all of the subsystems of Amazon. We also have Docker and Splunk integrations. The integrations are great because they're definitely vetted and not third-party integrations. They're part of the Datadog ecosystem and seamless.

The way data is represented can be limiting. They have added their own little query language that you can use to manipulate things, so you can graph and relate two different metrics together. This is relatively new this year. When I first tried it out a long time ago, you could graph a metric and another metric, and they'd overlay, but you couldn't take the ratio between the two. However, it looks like this is the direction that they're going, and that's a good direction. I think they should continue adding things that way.

I like being able to put the formulas in myself. I don't want the average. I want a rolling average over three minutes, not five minutes. They're getting better at letting the user customize this.

When I started using it years ago, it had stability problems. I remember, specifically, we ran everything in Docker containers. There were some problems getting it into a Docker container with very specific memory limits. We couldn't nail down exactly what the limits and the application needed. Once we did that, we were good. However, it was tricky to get the limit in the first place.

It has always scaled for us. Cost scales up too, but that is not necessarily a bad thing. It's reasonable for what they're providing. I haven't had any concerns about scaling.

We use between a 100 to 500 servers at any given point in time.

For the most part, the technical support is pretty good. Every now and again, you will get stuck with a support rep who could have better training, but in general, they are very good and responsive. They're willing to talk about new features, etc.

The integration and configuration processes have been very smooth because everything is very well-documented. The documentation is phenomenal. 

We can see trends a lot easier than if we didn't have the solution. The management can see the changes which are being made, whether it being performance or in the number of hosts that went down. We recently made internal improvements to some of our internal APIs, so we reduced the number of servers that we needed. So, you could see that the load on the system went down and the number of servers went down. Thus, it was easy to visualize.

Pricing and licensing are reasonable for what they give you. You get the first five hosts free, which is fun to play around with. Then it's about four dollars a month per host, which is very affordable for what you get out of it. We have a lot of hosts that we put a lot of custom metrics into, and every host gives you an allowance for the number of custom metrics. We have not had a problem with it.

My company now is pretty good at looking at alternatives. Also, I evaluated alternative solutions at my last company. 

There are some other competitors. For example, I know one of them started doing metrics and their licensing is very cheap because the metric size is very small and it's per megabyte. They charge you per storage, and it's very small. However, the interface and integrations aren't there. and there are some other competitors, 

The other thing is granularity. Datadog gives you one second granularity for a year. Whereas, some of the competitors would roll up, so after about a week you don't have one second, you have five seconds. Then, after a month, you don't have five seconds, you have a minute. So, you start to lose the granularity, whether it be that it averages it or maxes it, you start to lose the ability to see incidents historically, which is super valuable. If we have an incident, which we think we've seen this before, and want to look back historically, we can zoom right in and see in the database where it peaked.

Give Datadog a try. It's the leader in this space. 

I have only used the AWS version of the product.

They have a thing for the color purple, but it is all good.

Our primary use case would be using the dashboards and getting proper insights based on the dashboards.

The monitoring, SLO, and SLA have been better and easier since we started using the Terraform infrastructure. APM has been easier as we had to enable it through the CronJob directly.

Profiling has been made easier. We are able to get many insights into the code. Profiling provides really good insights right now. 

Logs are the most valuable and the best solution so far. Datadog can help solve any slow queries or database-related errors. 

The primary use case would be using the dashboards and getting proper insights based on the dashboards.

Monitoring has been better and easier since we started using the Terraform infrastructure.

APM has been easier as we had to enable it through the CronJob directly.

Profiling has made it easier in terms of getting many insights into the code.

The logs are the most valuable and the best solution. Datadog can help us to solve any slow queries or database-related errors.

Profiling provides really good insights, and APM has really good tracing visibility. 

The SLA and SLO definitions and the monitoring are also really important and very valuable parts of the product and make great Datadog features. 

Datadog support is also really valuable as they provide support for the product through the chat as well. 

The Datadog premium support has helped us to provide faster outcomes for a problem. 

Also, rather than having an email thread, it would be better to get the support on call and sort out the issue, which is the support we get from Datadog CSM.

Integration should have been easier. It is very tough to go to all the services and enable Datadog integration for each AWS service. 

We can add the AWS services and the services on one page and show only the services that are enabled. A similar approach should be for any other integration.

Lately, chat support has a longer waiting time. We would love to get faster chat support. We also need additional support for sending the flare files

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 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.

We use Datadog for observability and monitoring primarily. Various cross-functional teams have built various dashboards, including Developers, QA, DevOps, and SRE. 

There are also some dashboards created for senior leadership to keep tabs on days to day activities like cost, scale, issues, etc. 

Also, we've set up monitors and alarms that kick off when any metrics go beyond the threshold. With Slack and PagerDuty integration, correct team members get alerted and react to solve the issue based on various runbooks.

Using Datadog metrics has helped the organization a lot in many manners. With one centralized monitoring place, it's a lot less effort to keep track of the system and applications' health. 

Using this also helps teams be proactive in dealing with any issues before they get escalated by customers. 

Lastly, having so many integrations makes the DevOps and SRE's lives a lot easier when automating the detection and resolution of any issues hidden in the system or applications. Overall, it has helped a lot.

My favorite feature is creating dashboards as that empowers me to sleep calmly at night and not to keep watch on critical system metrics. Be it DB metrics or computer-related metrics, it's always easy to view them. 

The ease of correcting these dashboards and widgets when needed is amazing. 

The only issue I face is when more than one person editing these dashboards simultaneously, one or the other person sometimes loses his/her work. That said,  they will resolve that soon. With the variety of widgets, it's so easy to plot the data in a timely manner, and that makes monitoring a lot easier.

The solution can be improved in a few areas. 

The parallel editing of the dashboards should not cause users to lose the work of another person. 

Secondly, we would like to see more demos of tools that are in beta version, when they come live. I am sure they will help us a lot.

I've been using the solution for slightly over two years.

I find the solution to be very stable.

I totally love it. It is scalable. 

We previously used Sumo Logic.

The initial setup is not so difficult.

We implemented the solution in-house.

The ROI is very fair so far.

I can't recommend the licensing.

I was not involved in any pre-evaluation process.

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.

We primarily use Datadog for logs, APM, infrastructure monitoring, and lambda visibility.

We have built a number of critical dashboards that we display within our office for engineers to have a good understanding of the application performance, as well as business partners to understand at a high level the traffic flowing through the app.

We started with logging, as our primary monitor, and have shifted to APM to get a deeper understanding of what our system is doing, and how the changes we are making impact the apps.

Datadog has given us near-live visibility across our entire cloud platform. We are finally in a state where we are alerting our users about degraded performance well before the helpdesk tickets start rolling in.

We are making major architectural decisions based on the data we are getting from Datadog. It also gives us an idea of where the complexity really lies in some older, monolithic apps. 

We have used the APM endpoint monitoring to prioritize work on slower endpoints because we can see the total count, as well as the latency. That has been a big driver in our refactor work prioritization.

We have struggled to get more business-centric measures in our code to surface actual business values in our reports, but that is our next initiative.

We started with Log analytics in the beginning stages of our monitoring journey. Those were very insightful, but obviously only as useful as we made them with good logging practices.

The dashboards we created are core indicators of the health of our system, and it is one of the most reliable sources we have turned to, especially as we have seen APM metrics impacted several times lately. We can usually rely on logs to tell us what the apps are doing.

APM and Traces have been crucial to understanding how users are actually using the app. That drives a lot of our decisions around refactoring and focusing our limited engineering resources.

Continued improvement around cost and pricing model is needed. It is pretty complex and takes a fair amount of intimate knowledge to know exactly how turning on a single function is going to impact your bill, especially when you don't see the metrics for a day or two. 

We have recently had a number of issues with stability and delays on logging, monitoring, metric evaluation, and alerts. More often than not in the past month, it seems that we get the banner across the to of our dashboards that some service is impacted. They don't always show up on the incident page, either.

We have been using Datadog for two years.

Overall, it has been fairly stable for us. There are the occasional issues with importing data, that has usually been resolved in a short time. We have never had an issue where that data was lost, just delayed, and eventually backfilled. 

It seems (anecdotally, of course) that there have been a few more stability issues lately. We have noticed several days that we are getting in-app alert banners indicating that some metric or log ingestion was delayed, or the web app itself was experiencing severe slowness. 

Overall, these issues are resolved rather quickly - kudos to their engineering teams. I hear that they actually use Datadog to monitor Datadog. 

Datadog is very scalable but just watch the cost.

Technical support is hit and miss; there are a number of nuances to how this tool should be implemented, and it is difficult to re-explain how our infrastructure and applications are set up every time we need an in-depth investigation to understand what is broken.

Previously, we used AppDynamics. The pricing model didn't seem to fit with actual cloud spend. Now we may have swung the pendulum a little too far, and seem to be dealing with pricing on every facet of the application. 

The initial setup was pretty straightforward. Additional tweaks and configuration have been a bit more difficult as we get deeper and deeper into the guts of the integrations. Making sure we are keeping up with a rapid release schedule, and keeping our server clients in sync with our app packages has been troublesome. There have been some major changes in the APM that have introduced a number of bugs and broken some of our dashboards and alerts.

Our in-house team handled the deployment, with a lot of tickets created for the Datadog team.

ROI is difficult to measure completely. Our first year spend compared to our second and now going into the third year spend have been significantly different.

My advice is to really keep an eye on your overage costs, as they can spiral really fast. We turned on some additional span measures and didn't realize until it was too late that it had generated a ton.

Frankly, we love the visibility it gives us into our applications, but it is a bit cumbersome to ensure we are paying for the right stuff. Overall, the cost is worth it, as it helps us keep system-critical applications up and running, and reduces our detection and correction times significantly.

We evaluated Dynatrace and AppD before choosing this product.

Datadog requires pretty close supervision on the usage page to ensure you aren't going out of control. They have provided a bunch of new features to assist in retention percentage, but it can be a bit confusing on what is being retained, and what can be viewed again after triggering an alert. It's a difficult balance of making sure you are getting the right data for alerts, and still having the correct information still available for research after the fact.

We were in need of a cloud monitoring tool that was operationally focused on the AWS Platform. We wanted to be able to responsibly and effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, and key AWS Services.

Tooling that highlighted and detected problems, anomalies, and provided best practice recommendations. Tooling that expedites root-cause analysis and performance troubleshooting.

Datadog provided us the ability to monitor our cloud infrastructure (network, servers, storage), platform/middleware (database, web/applications servers, business process automation), and business applications across our cloud providers.

Datadog provided us the tooling to help us effectively monitor, troubleshoot, and operate the AWS platform, including Server, Network, Database, and key AWS Services. It highlights detected problems and anomalies and provides best practice recommendations, expedites root-cause analysis, and performance troubleshooting.

Datadog provides analytics and insights that are actionable through out-of-the-box visualizations, dashboards, aggregation, and intuitive searching that shortens the time to value and account for our limited time & resources we have to operate in production.

The most valuable feature is the dashboards that are provided out of the box, as well as ones we were able to configure. Specific Dashboards that were provided that made things easier were EC2, RDS, Kubernetes dashboards.

We also use the logging tool, which makes searching for specific error logs easier to do.

Datadog Logging provides the capability for us to use AWS logs such as VPC Flow Logs, ELB, EC2, RDS, and other logs that provide lots of relevant operational data but are not actionable. Datadog provides a tool that can provide us analytics and insights that are actionable for visualizations, dashboards, alerting, and intuitive searching.

More pre-configured "Monitor Alerts" would be helpful. Datadog's knowledge of its customers and what they are looking for in terms of monitoring and alerting could be taken advantage of with pre-canned alerts. They have started this with "Recommended Monitors".  That feature was very helpful when configuring our Kubernetes alerts. More would be even better. 

Datadog tech support is very good. One area that could be more helpful is actually talking to someone or sharing your screen to help troubleshoot issues that arise. For new cloud engineers just coming into the cloud monitoring field, there is a learning curve. There is a lot to learn and figure out. For example, we still ran into some issues configuring the private link and more videos of how to do things could be of use.

We have been using Datadog for one year.

We have not run into any issues with stability.

The scalability of Datadog is very good.

Customer service has been excellent.  I communicate weekly a Datadog Customer Success Manager.  He helps me followup on any open issues or questions that we may have.  Technical support has been very good. Opening tickets is easy.  Sometimes a Tech Engineer may take a bit of time to get back with you.  Communicating with Tech Engineer has to be done via ticket/email - no phone assistance is available.

we did not.

Procedures for setup seemed straightforward but once you got going, there were some issues. For us, getting our private link to work needed additional tech support. They were able to help us resolve the issue we were experiencing. I think the procedures could be done a bit better to help you with setup.

We deployed it ourselves.

Datadog helps us minimize downtime and helps us resolve issues quickly.  

Pricing seemed easy until the bill came in and some things were not accounted for. The issue may have been that we didn't realize what was being accounted for, such as the number of servers and the number of logs being ingested.

Datadog had really good pre-sale reps that work with us but need to make sure all the details are covered.

The solution we were looking for needed to provide out-of-the-box capabilities that shorten the time to value. We had limited time & limited resources. Datadog had high recommendations in these areas, so we decided to do a trial with them.

We are very pleased with Datadog overall.

Datadog has assigned an account rep to us that meets with us regularly to make sure all our needs are being met and help us get answers to any questions or issues we are running up against. They have been of great helping us standup monitoring of our Kubernetes environment.