Datadog Reviews

We use the solution primarily for distributed tracing, service insight and observability, metrics, and monitoring. We create custom metrics from outbound service calls to trace the availability of back-office systems. 

We use the flame graph to get insights into our GraphQL implementation. It helps highlight how resolvers work. 

However, it's lacking in tracing which GraphQL queries are run, and we use custom spans for that.

Prior, the team only had Instana, and few people used it. The main barriers to entry were the access (since it was not integrated into our SSO) and the user experience, which made it hard to follow. We had an on-prem version, and it wasn't the snappiest. The APM has made observability and tracing more accessible to developers.

Flame graphs are pretty useful for understanding how GraphQL resolves our federated queries when it comes to identifying slow points in our requests. In our microservice environment with 170 services. There are complex transactions over the course of a single user request since we essentially operate as a middle layer with 90 back office systems we integrate to.

I often have issues with the UI in my browser. I tend to have a lot of tabs open, yet have issues with it not responding or not showing data. A couple of times, pasting the URL into an incognito window shows the data that's there.

I've used the solution for two years. 

The initial setup was complex and required a bit of tweaking to get everything configured correctly and into our pipelines.

We use the solution for application hosting and a little bit of everything when it comes to supporting a worldwide logistics tracking service. It's used as a central service for collecting telemetrics and logs. We find it does the same work as all of our old tools combined, including Prometheus, Kibana, Google Logs, and more; putting all of this information in a single platform makes it easy to corroborate information and associate a request with the data, which might be lost when it is saved as logs.

At my organization, we have plenty of microservices written in different languages. Different teams prefer one or the other framework or library within those languages.

With Datadog, we can get in a single line and march in the same direction; our logs and metrics are collected in the same fashion, making it easy to find bugs or integration problems across services and understand how they interact with other systems.

I primarily prefer to utilize the profiling and tracing feature. It can potentially be used as a more-informed alternative to logs.

Beyond that, anything I've wanted to do, I found a way to get it done through Datadog. It allows for testing, logging, hardware monitoring, system performance, memory consumption, advanced observability, AI assistance, cross-team collaboration, and business analytics. Datadog helps some of the world’s biggest brands transform faster with the help of true AIOps, AI-assisted answers, UX and business analytics, cloud observability, and smart AI assistance.

It's all supporting my desire to build a great application, and in a centralized SaaS application, it's hard to say anything can beat it.

The storage of logs is a little bit unexpected; most services generate gigabytes of logs, and their size is not excessive. When it comes to storing the logs with Datadog, I'm not sure why it costs so much to store gigabytes or terabytes of information when it's a fraction of the cost to do so myself.

I've used the solution for one year.

We have no concerns with stability.

It appears to be that there are no issues with scaling.

Technical support is slow. It takes forever to get responses from the support team.

Neutral

I've previously used Kibana and Prometheus. We are still using these.

Setting up through the environment variables made it unbelievably easy to get started.

We've implemented the solution in-house.

I do not have this number off-hand, as I am not the finance guy. I just like the product.

I'd advise new users not to start off by sending logs.

We did not really look at other options.

We collect all data logs from all operating systems, such as Windows, Linux, VMware, and bare metal data centers. We also automatize the installation of the agent on servers. 

Now we are starting a POC to analyze the APM module. In the feature, the next step is to do a POC of security modules. 

The final idea is to have a unique portal for observability. This will make it easy to troubleshoot and for layer levels 1 and 2. 

We are looking into a lot of modules. We collect all data logs from all operating systems, including Windows, Linux, VMware, and bare metal data centers. We also automatize the installation of the agent on servers. 

We're developing POCs for APM and security modules. We'll also have a unique portal for observability. This will make it easy to troubleshoot. 

The most valuable aspect is for us to have everything in one place.

We're investigating many modules. We collect all data logs from all operating systems (Windows, Linux, VMware, and bare metal data centers). We also automatize the installation of the agent on servers. 

We're doing POCs in APM and security. 

Soon, we'll have a unique portal for observability. This will make troubleshooting easy at levels 1 and 2. 

The most valuable aspect for us is to have everything in the same place.

We need a lot of modules since we collect all data logs from all operating systems. 

The most important module for us is log management. The second is the security module. The third one is the APM.

We've used the solution for one year.

We are using the solution for migrating out of the data center. Old apps need to be re-architected. We plan to move to multi-cloud for disaster recovery and avoid vendor lockouts. The migration is a mix between an MSP (Infosys) and in-house devs. The hard part is ensuring these apps run the same in the cloud as they do on-prem. Then we also need to ensure that we improve performance when possible. With deadlines approaching quickly, it is important not to cut corners which is why we needed observability.

The product has created a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in service now. This wouldn't scale, as teams wouldn't be able to create monitors on the fly and would have to wait on us to contact the ServiceNow team to create a custom lookup. Now, in real-time, as new instances are spun up and down, they are still guaranteed to be covered by monitoring. This used to require a change request, and now it is automatic.

For use, the most valuable features we have are infrastructure and APM metrics. The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers that Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having also to make them add them at the agent level. Then we use Datadogs conditionals in the monitor to dynamically alert hundreds of teams, and with the ServiceNow integration, we can also assign tickets based on the environment. Now, our top teams are using APM/profiler to find bottlenecks and improve the speed of our apps.

The real issue with this product is cost control. For example, when logs first came out, they didn't have any index cuts. This leads to runaway logs and exploding costs. 

It seems that admin cost control granularity is an afterthought. For example, synthetics have been out for over four years, yet there are no ways to limit teams from creating tests that fire off every minute. If we could say you can't test more than once every five minutes that would save us 5X on our bill.

I've been using the solution for about three years. 

The solution is very stable. There are not too many outages, and they fix them fast.

It is easy to scale. It's why we adopted it. 

Before premium support, I would avoid using them since it was so bad.

Neutral

We previously used App Dynamics. It isn't built for the cloud and is hard to deploy at scale.

The initial setup was not complex. We just had to teach teams the concept of tags.

We implemented the solution in-house. It was me. I am the SME for Datadog at the company.

We have seen an ROI. It has saved months of time and reduced blindspots for all app teams.

We'd advise new users to be careful with logs, and the APM as those are the ones that can get expensive fast.

We looked into Dynatrace. However, we found the cost to be high.

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 are using the solution for migrating out of the data center. Old apps need to be re-architected. We are planning on moving to multi-cloud for disaster recovery and to avoid vendor lockouts. 

The migration is a mix between an MSP (Infosys) and in-house developers. The hard part is ensuring these apps run the same in the cloud as they do on-premises. Then we also need to ensure that we improve performance when possible. With deadlines approaching quickly it's important not to cut corners - which is why we needed observability

Using the product has caused a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in ServiceNow. This wouldn't scale, as teams wouldn't be able to create monitors on the fly and would have to wait on us to contact the ServiceNow team to create a custom lookup. Now, in real-time, as new instances are spun up and down, they are still guaranteed to be covered by monitoring. This used to require a change request, and now it is automatic.

For use, the most valuable features we have are infrastructure and APM metrics.

The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having to also make them add it at the agent level. Then we use Datadog's conditionals in the monitor to dynamically alert hundreds of teams. 

With the ServiceNow integration, we can also assign tickets based on the environment. Now our top teams are using the APM/profiler to find bottlenecks and improve the speed of our apps

The real issue with this product is cost control. For example, when logs first came out they didn't have any index cuts. This caused runaway logs and exploding costs. 

It seems that admin cost control granularity is an afterthought. For example, synthetics have been out for over four years, yet there is no way to limit teams from creating tests that fire off every minute. If we could say you can't test more than once every five minutes, that would save us 5X on our bill.

I've used the solution for about three years. 

The solution is very stable. There are not too many outages, and they fix them fast.

It is easy to scale. That is why we adopted it.

Before premium support, I would avoid using them as it was so bad.

Neutral

We previously used AppDynamics. It isn't built for the cloud and is hard to deploy at scale.

The initial setup was not difficult. We just had to teach teams the concept of tags.

We did the implementation in-house. It was me. I am the SME for Datadog at the company.

The solution has saved months of time and reduced blindspots for all app teams.

I'd advise users to be careful with logs and the APM as those are the ones that can get expensive fast.

We looked into Dynatrace. However, we found the cost to be high.

I use the solution to manage security-related logs and metrics, as well as create detection rules for security events. I am a security engineer, so one area of interest is the CSPM product, giving us the ability to look at findings across the cloud environment. 

The great part about the Datadog security products is that they incorporate the context of the resources/hosts where the security event is found. This allows us to see exactly what is running on a host that we see as a security alert.

The greatest impact it has had is on the ability to democratize observability and put monitoring into the hands of the people. Teams can quickly get the information they need, without needing a bunch of training, since the UI is super intuitive and easy for beginners. This helps reduce time to resolution during incidents and gives context to developers quickly and easily. Context is really important since seconds matter when the ship is down, and you don't know why.

I find the greatest feature is being able to search across logs from various microservices. As a member of the security team, I find that I often need visibility into other teams' services in order to get a good picture of our security posture.

I also am a fan of the ability to easily create monitors and get alerts into Slack quickly, without too much overhead. For example, I often need to create monitors where I am not too sure where the baseline lies. Having the ability to create anomaly monitors makes this process much more straightforward. Anomaly monitors are great for a security team.

One area where I was really looking for improvement was the CSPM product line. I had really wanted to have team-level visibility for findings, since the team managing the resources has much more context and ability to resolve the issue, as the service owner. However, this has been added to the announcement in a recent keynote. 

Personally, I've used it my entire time employed here, more than three years.

We use Datadog for observability and system/application health, mainly for product support, triaging, debugging, and incident responses.

We use a lot of the logging and the Datadog agent to collect logs, metrics, and traces from our GKE workloads. We use APM and continuous profiling for latency and performance measurement. We use RUM to observe frontend user events, such as tracing on request and what actions they take before errors occur. We also use error tracking and source maps to debug production failures.

We are still relatively new to the product, and we are planning to use more of the notebook functionality and power packs to record run books and break knowledge silos. We also need to utilize dashboards and continuous profiling more for performance measurement and integrate Datadog alerts for incident response.

We have way more observability than what we had before - on the application and the overall system. That includes the GKE cluster, nodes, and pods. It's helped with our cloud-run instances, databases, and data storage.

We also started observability in the CI pipeline to measure our CI performance, as it was a pain point for us. We are aiming to do incremental deployments and releases, and the bottleneck so far has been our CI performance. The visibility on which actions or functions take the most time allows us to pinpoint and focus on improving configurations on these.

We use structure logging a lot to triage production issues. The querying, attributes and tags manipulation, and customization have been very helpful in isolating and filtering environments. The integration with Winston logger has also been a breeze.

First and foremost, was that structured logging, tags, and attributes have not only allowed us to narrow down to a problem quickly in production, they have also let us create dashboards from these logs to understand more user behaviors, such as how many users stop and leave our application before an upload has completed. That helps us understand how important processing time is to a user.

We also intend to use distributed tracing more to understand where the error has occurred in a particular request.

Definitely, documentation could use improvement. As I navigated and try to find instrumentation and implementation details, I discovered inconsistency among SDKs based on languages. 

There are also places where highlighting can be improved. I once created an issue on GitHub, and it was resolved right away by an engineer. He pointed out that it was actually in the documentation. I looked again and found it was not very obvious. We were stuck on the problem for days.

Auto instrumentation on tracing has not been very easy to find in the documentation. We ended up using OpenTelemetry, yet the conversion between tracing contexts has been difficult.

We've used the solution between six months and a year. 

Customer service and support are generally very fast. I did experience one ticket, which involved changing the log index retention period, not being responded to. Any support tickets related to technical issues were resolved pretty fast.

Positive

We used to use GCP Stackdriver for logging and monitoring since our infrastructure is all GCP based. It was lacking a lot, particularly on tracing and structured logging. We often had a lot of trouble triaging and diagnosing a production problem. Datadog's specialty is observability. Since we started using the product, we were able to create dashboards, and utilize APM, continuous profiling, RUM, and distributed tracing for production support and user trends.

Datadog also offers labs and workshops for its products, which is very helpful.

We implemented the product ourselves.

I'm not sure what our ROI would be.

We started with on-demand pricing as we were re-writing our product, and we weren't sure about the total usage. After we went into production and released the product, we experienced a price surge. Fortunately, our Datadog account manager reached out to us and suggested a monthly subscription, which is what we'll be switching to.

I'd advise keeping an eye on the usage and possibly setting up some monitoring on price. We didn't have much of a setup cost; we started with a free trial and continued with on-demand after the trial ended.

We didn't evaluate many of the other options. However, we do also use OpenTelemetry, which is vendor agnostic and integrates with Datadog.

We always keep the Datadog agent to the latest version.

I primarily use the solution to learn, watch and monitor business and engineering metrics in the production and QA environments of my team. 

We create monitors on key business metrics and observe regressions and anomalies.

Less often, I leverage the events ability in Datadog to get notified about significant activities happening in my teams' deployments.

We learn about Datadog monitor alerts through Slack and often attempt to create SLOs using Terraform.

We use APM for observability.

Most recently, I learned about WatchDog Alerts that I will be heavily looking into.

Datadog simplified my ability to watch easily and add monitors on any metric emitted by any team at my organization.

Datadog APM immensely improved our ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly to understand the time spent at each critical stage of a production request is helpful. This, combined with Datadog's historical ability to show business metrics aside, helped get more powerful insights much more quickly.

Datadog's seamless integration with Slack and PagerDuty helped us to receive alerts right to the most common notification methods we use (our mobile devices and Slack).

The most valuable aspects include:

• The ability to monitor any team's metric in my company (transparency)
• The ability to create/clone dashboards for myself (ease of use)
• Its integration with Slack (it is very powerful)
• The ability to add monitors on any metric emitted by any team at my organization
• (Through Datadog APM) the ability to understand the reasons behind production issues. Its ability to navigate across services seamlessly in order to understand the time spent at each critical stage of a production request is key. This, combined with Datadog's historical ability to show business metrics aside, helped me get more powerful insights much more quickly.
• (Through integrations like Slack and PagerDuty) the ability to receive alerts right to the most common notification method we use (our mobile devices and Slack), which saves a lot of time and helps us maintain focus. 

I would like better navigability across pages. The UI/UX is powerful, yet less intuitive. A lot of times, I somehow navigate across buttons and pages, and I end up forgetting how to get back to a particular view that was more insightful. 

Particularly as Datadog starts offering more platform capabilities like APM, Watchdog, Shift left initiatives like instrumentation, continuous testing, intelligent test runner, and Synthetic and real user monitoring, the UI can become more and more clunky, giving users a very frustrating experience. 

I've used the solution for five to six years.

We primarily use the solution for the service catalog.

We use this type of offering for our Microservices applications, and it gives a good view of flow. It is a must when we have different developers working on different services.

Having the trace and log features are useful for locating the microservice for the on-call person.

We would like to see some more useful applications for health monitoring where we can customize the cases based on data from the database.

It needs to have the facility to monitor data inside tables and the status of the UI.

The service catalog helped improve our organization by giving a good view of the flow for our microservices applications. It's important when we have different developers working on different services and having the trace and log features help the on-call person locate the microservice.

The application performance monitoring has also been useful. This module had a few functionalities that we needed for the application health check. This needs to have some more features to consolidate the view in one tree. We may need more of a one-stop shop on top of the dashboard, and that is missing in Datadog. We'd like to be able to scrap our existing monitoring tool.

The service catalog is very useful. We use this type of offering for our Microservices applications, and it gives a good view of flow. It is a must when we have different developers working on different services. Having the trace and log features have been useful in order to locate the microservice for the on-call person.

The dashboard is great. It is helpful to get a view of specific things that we need to monitor for our application. It has been a good way to watch specific things and add them together.

The application performance monitoring is an excellent aspect. This module had a few functionalities that we needed for the application health check. This needs to have some more features to consolidate the view into one tree, however.

The dashboard could be improved. It would be helpful to get a view of specific things that we need to monitor for our application. However, it was a good way to watch specific things and add them together.

The application performance monitoring module had very few functionalities that we needed for the application health check. This needs to have some more features to consolidate the view into one tree.

I've used the solution for one month. 

We previously used ITRS Geneos.

We are using the latest version of the solution. 

I'd rate the solution seven out of ten.