Datadog Reviews

Ingesting data from various sources to monitor the log metrics of the system and enabling an alert mechanism to notify the teammates if something goes wrong.

More specifically, having Datadog agents as integration to different services provides easy access and management.

The solution has helped out organization by allowing us to ingest data from various sources to monitor log metrics and enabling alert mechanisms to notify teams if something goes wrong.

Datadog agents act as an integration to different services, providing easy access and management.

The solution is useful for ingesting data from various sources. This helps to monitor the log metrics of the system. It has alert mechanisms that can be enabled to notify the teams if something goes wrong.

Datadog offers good integration to different services. It provides for easy access and management.

Ingesting data from various sources to monitor the log metrics of the system can always improve so that, if something goes wrong, the right teams are alerted. 

We've used the solution for close to 1.5 years.

We use a SaaS deployment.

The RUM is implemented for customer support session replays to quickly route, triage, and troubleshoot support issues which can be sent to our engineering teams directly. 

Customer Support will log in directly after receiving a customer request and work on the issue. Engineers will utilize the replay along with RUM to pinpoint the issue combined with APM and Infra trace to be able to look for signals to find the direct cause of the customer impact. 

Incident management will be utilized to open a Jira ticket for engineering, and it integrates with ITSM systems and on-call as needed.

The RUM solution has improved our ability to triage faster and hand more capabilities to our customer support.

The RUM is implemented for customer support. It can quickly route, triage, and troubleshoot support issues that are sent to our engineering teams. 

Customer support can log in and start troubleshooting after receiving a customer request. The replay and RUM help pinpoint the issue. This functionality is combined with APM and Infra trace to be able to look for the cause of the issue. Incident management is leveraged to open a Jira ticket for engineering, and it can integrate with ITSM systems and on-call as needed.

RUM with session replay combined with a future use case to support synthetics will help to identify issues earlier in our process. We have not rolled this out yet but plan for it as a future use case for our customer support process. This, combined with integrated automation for incident management, will drive down our MTTR and time spent working through tickets. Overall, we are hoping to use this to look at our data and perfection rate over time in a BI-like way to reduce our customer support headcount by saving on time spent.

I would like to see retention options greater than 30-days for session replay. I'd also like to see forwarding options for retention to custom solutions, and a greater ability to event and export data from the tooling overall to BI/DW solutions for reporting across the long term and to see trends as needed.

I've used the solution for about nine months.

So far, stability has been great.

I'd like to see more bells and whistles added over time. Widgets are coming soon to help with RUM.

Support is very good. They are responsive and gave us the help we need.

Positive

We have utilized New Relic, however, not for RUM. We went with Datadog to potentially switch the entire platform into an all-in-one solution that makes sense for a company of our size.

We started on the beta, and the documentation was lagging behind. We also needed direct instructions and links from the customer support/account representative that was not immediately available by searching online.

We implemented the solution ourselves.

Ideally, this will inform our strategy to not increase our customer support headcount as significantly into 2023 and beyond.

The pricing is a bit confusing. However, the RUM session replay, in general, is very inexpensive compared to whole solutions.

We looked into LogRocket and New Relic.

I'd advise other users to try it out.

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.

We use Datadog for application monitoring, to help identify errors. It is also used to monitor application performance.

It helps organizations to understand User Experience with user behaviour pattern

Helped to reduce production issues in a defined timeframe

Helped to refine UX

Datadog has a very good visualization for my complete infrastructure and network traffic, which enabled me to create a capacity plan.

This product is great because it shows you the SQL and your application request in a single view.

The error traceability is an area that can be improved. This is something that helps us to pinpoint the area where a problem is occurring. It is a function stack, and it should be showing us how each function is defined.

We have been using Datadog for the past couple of Years.

I have not worked on it long enough to properly comment on stability, yet, because it has to be tested across my other platforms.

We have not done a full evaluation yet, but given that it is cloud-based, DataDog has to be scalable.

I have not needed to contact technical support.

Positive

We were using New Relic prior to implementing Datadog. In terms of application monitoring, Datadog is not up to the level that New Relic is. It is a better product but the price is too high, which is why we switched.

Yes. It is not complex. It allows you to get a certification of DataDog prior deployment of associates to administration and configuration

Inhouse. We got our Admin team certified.

Time to resolution production issue

The price is better than some competing products.

NewRelic

This is a good product and I can recommend it to others, although New Relic is still my first choice. Datadog is my second choice.

Overall, it is a good product and my main complaint is that it needs better error traceability.

I would rate this solution a nine out of ten.

We use Datadog as a monitoring platform to achieve visibility into our container environments.

Almost all of our workloads are containerized and with DataDog, we are able to get metrics, logs, alerts, and events about all the containers that we are running. Our developers also extensively use APM to find and diagnose performance issues that might appear.

We use Terraform to automatically create all of the necessary monitors and dashboards that our developers need to make sure that our level of service is sufficient.

We implemented Datadog around the same time as the company was growing from 30 to 150 people. Before that, we didn't have a standard stack for monitoring. Each team used their own logging solutions, metrics were missing or non-existent, and it was impossible to correlates metrics collected by different teams. DataDog provided us with an out-of-the-box solution that allowed us to focus on putting in place practices and processes around monitoring, rather than focus on implementation details.

Every squad is now confident in their ability to quickly identify and diagnose issues when they arise.

The fact that everything is under a single pane of glass is really valuable, as developers don't have to spend their time copying correlation IDs across tools to find what they need.

Thanks to the unified tagging system, it's really easy to jump around the different Datadog products without losing the context. That makes debugging really easy for developers because they can go from APM to logs to metrics in a few clicks.

Watchdog is also a great feature that helped us identify overlooked issues more than once.

The incident management beta looks promising, but it is still missing the ability to automatically create incidents based on certain alerts.

SLOs are also a great way to visualize how you are doing with regard to the level of service that you are providing but it missing crucial components like:

• The ability to visualize the remaining error budget and how it evolved during the month. An error budget burndown graph would be helpful.
• The ability to display a different level of alert on an SLO based on how fast it is consuming the error budget. This is the slow burn versus fast burn.

We've been using Datadog for a bit more than two years.

There is extensive documentation and the support is very reactive.

Prior to using Datadog, each team was using their own solutions. This included a mix of custom tooling, third-party tools, and AWS tools.

The initial setup is very easy. 

Our primary use of Datadog includes: 

• Keeping a close look into our AWS resources. Monitoring our multiple RDS and ElastiCache instances play a big role in our indicators.
• Kubernetes. We aren't using all of the available Kubernetes integrations but the few of them that work out of the box adds great value to our metrics.
• Monitoring and alerting. We wired our most relevant monitoring and alerts to services like PagerDuty, and for the rest of them, we keep our engineers up to date with constant Slack updates. 
  

Observability is something that a lot of Companies are trying to achieve. Having a clear view, not only of our infrastructure but our apps and services as well, has brought a great added value to our customers.

For a logging solution, we use to have Papertrail. It did the trick but having a single point that manages and indexes all the logs is a BIG improvement. Also, having the option to generate metrics from logs is a game-changer that we're trying to include in our monitoring strategy.

I would like to say the same about APM but the support for PHP seems to be somewhat lacking. It works but I think this service could provide us more information.

With respect to logs, we used to integrate various kinds of tools to achieve very basic tasks and it always felt like a very fragile solution. I think logs are by far the most useful feature and at the same time, the one that we could improve.

APM - This is either a hit or miss, allow me to explain: we use various programming languages, mainly PHP and Ruby, and the traces generated don't always provide all of the information we want. For example, we get a great level of detail for the SQL queries that the app generates but not so much for the PHP side. It's hard to track where exactly where all of the bottlenecks are, so some analysis tools for APM could make a good addition.

Please add PHP profiling; you already have it for other popular programming languages such as Python and Java, which is great because we have a little bit of those, but our main app is powered by PHP and we don't have profiling for this yet. I guess it's only a matter of time for this to be added, so in the meanwhile, you can consider this review as a vote for the PHP profiling support.

The pricing model could be simplified as it feels a bit outdated, especially when you look at the billing model of compute instances vs the containers instances.

We have been using Datadog for one year.

It's pretty stable for the main integrations. There was only one time where Datadog was down and that was scary since all of our monitoring is handled by Datadog. There was a lot of uncertainty while the outage was in place.

For everyday use, it's adequate, but for very specific tasks, not so much. There was a time where I had to do a big export and as expected, the API is somewhat limited. Since it was a one-time task, it was not a big deal but if this was a regular task, I wouldn't be happy about it.

For small tasks, I think it's great. For specialized support, it feels like you're under-staffed, having to wait days/weeks for a solution is a big NO-NO.

I've used a few other products such as NewRelic and AppDynamics. The switch is usually affected by two factors: pricing and convenience.

Getting APM metrics out of Kubernetes is always a painful task. We got support to take a look at this and we had to go through various iterations to get it right, and then AGAIN the next year. This was a bad experience.

It was all implemented in-house. The documentation is fairly up to date, for the most part.

Pricing is somewhat affordable compared to other solutions but in order to really lower the costs of other products you need to plan very carefully your resources usage, otherwise, it can get expensive real quick.

Unfortunately, it wasn't my call to include Datadog for this Company but sure I'm glad that the Lead Architect took this decision. It brought many improvements in a small span of time.

Please add PHP profiling soon!

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.

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.