Datadog Reviews

We primarily use the solution for observability, metrics, logs, tracing, and end-to-end user flow monitoring. 

We are looking to implement this as a company-wide standard for cloud solutions.

At this time, we're currently in a POC, and we're interested in using either a Datadog agent or the OTel agent with a Datadog exporter. We have dashboards with panels that correlate metrics and allow you to link through to traces. Flame graphs to show latency across services and the various spans. 

While we are not security minded, we still require it and are interested in more. It's used for monitoring critical systems.

It has provided visibility with ease of implementation and allowed multiple teams to quickly onboard it. This provided a standard way to approach observability and visibility. 

Monitoring rules and alerting thresholds can also be set and exported to other teams for use. 

There is an issue with federated dashboards, as multiple teams running on different Datadog instances cannot use features like the service catalog or easily switch between services in a long business flow.

The K8 monitoring is extremely useful in Datadog. Preset dashboards that it provides help to speed up the work. 

The metrics summary is useful. Tracing with a span breakdown is helpful for us. We like the dashboarding with power packs and logging correlation with traces and logs. 

The Flame graph for tracing helps determine where the latency is the highest. 

Dashboards are created as a standard set and then exported into other Datadog instances for other teams. 

These dashboards would be updated regularly and pushed out to the teams. Unfortunately, there is no way to automatically push or deploy code in a quicker way. Each team I work with has its own Datadog instance.

Federated views for Datadog dashboards are critical as large companies utilize multiple instances of the product and cannot link the metrics or correlate the metrics together. This stunts the usage of Datadog. Additionally, using an OTel agent would be more acceptable and allow for easier adoption of Datadog across the hundreds of teams here.

I've used the solution for four months.

We primarily use the solution for observability.

The solution has helped with our POV phase.

The observability on offer is the most useful aspect of the product.

The FinOps needs improvement. 

The stability is good.

The scalability is good.

We previously used AppDynamics and Dynatrace.

We also evaluated AppDynamics and Dynatrace.

We primarily use the solution for monitoring and telemetry.

We use lots of log collections, log-based metrics, and dashboard visualization.  The logging, metrics, and APM are vital.  

My team focuses on the backend. Day-to-day monitoring includes observing metrics such as the CPU and memory until it gets too high. This solution provides an alert during the metric collection.  

APM and tracing are super useful. We use it for daily monitoring of CPU and memory. We can get alerts to tail to specific metrics.

We also find the tracing feature useful. We often run into bugs, and when a production issue happens, it is super useful to see the related services and sense where the problem is.

We would like to see smaller or shorter tutorials and video sessions. Also, the ability to provide a custom formula for monitoring is vital. Perhaps there can be more training materials on this. We often need to detect slow-running queries and slow network responses. We also focus a lot on the abuse of request limits. Having some form of rate limit features or metrics would be useful.  

Profiling could also be useful. Some services are CPU-intensive, and others are IO-intensive. Knowing where the bottleneck is, is crucial. 

We primarily use the solution for log monitoring across our entire cloud infra (EB, EC2, Batch, and Lambda).

This is in addition to Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch(https://docs.rstudio.com/ide/server-pro/server_management/logging.html#default-log-file-locations). 

We own several dozen of these servers, and we used to manage instance logs by tailing logs when incidents occurred. Datadog allows for much better visibility across our entire fleet and has saved us countless hours.

It is now way easier to search in one place rather than across all of Cloudwatch (and needing to know log groups, etc.). 

Primarily, we run several separate deployments of Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch. 

We own several dozen of these servers. We used to manage instance logs manually. 

Datadog allows for much better visibility.

We've found it most useful for managing Rstudio Workbench, which has its own logs that would not be picked up via Cloudwatch. 

Datadog allows for much better visibility across our entire fleet and has saved us countless eng hours as a result. 

We plan on trying out offerings such as APM moving forward too.

Some things that Datadog does very well:

• Technical documentation (the docs are clear, concise, and include realistic code samples)
• Overall education efforts (e.g. the codelabs/workshops)

We primarily use the log management functionality, and the only feedback I have there is better fuzzy text searching in logs (the kind that Kibana has). 

I've learned about a ton of other offerings, like APM, NPM, etc., over the course of workshops. Once I try those out, I'm sure I will have additional feedback.

I've used the solution for one year. 

We mostly use it to handle log aggregation, monitor our web application, and alert us on data pipeline failures. 

Our system is fully on AWS, and so we pipe in all of our Cloudwatch logs into Datadog to have a central place to index and search logs. 

Our web app is built on an Elastic Beanstalk backend, and we use the Datadog agent to keep track of all of the requests that hit our backend and all of their components. 

We also use the prebuilt AWS pipeline dashboards to monitor our batch jobs and lambdas.

Datadog has made it much easier to have a central place for people to look for logs and made it much easier to notify them of any elevated error rates or failures. 

It is also easier to get high-level views of platform health, whereas looking directly at AWS tends to provide very specific insight into particular surface areas or products. 

By having the whole team onboard onto Datadog, we also have a single source of truth that everyone can use when triaging and resolving incidents that occur across any surface area.

The ease of setting up metrics and alerting and integrating with Slack has significantly reduced the friction of keeping the team up to date on the platform's health. Before creating custom Cloudwatch metrics was never very intuitive, and also it was non-trivial to set up integrations with other services we use, especially Slack. 

It also provides a good way to gain the context needed when trying to fix issues, as it's a central place to look through logs, requests, AWS metrics, and more - overall contributing to the health of our platform.

The product is quite complex, and there are so many features that I either didn't know about or wasn't sure how to use.

One thing that could be improved is somehow surfacing interesting or relevant products that might be applicable given our infrastructure. 

Additionally, the billing can sometimes be confusing and opaque, especially around not making it obvious what the implications can be if you add different AWS integrations. This has caused some unexpected costs in the past due to engineers not understanding how Datadog pricing works.

We've used the solution for around two years.

This was the first solution we tried.

It is quite expensive, especially if you don't know how the pricing works.

One of the things we use it for is the same thing that we use FullStory for, which is to replay customer interactions with our platform. However, it also does the monitoring. It's like monitoring cloud tools. We're really mostly monitoring our own software to make sure that everything is functioning properly. We can check a bunch of things, and we can even play back customer sessions. It’s basically monitoring our application.

It really provides a lot of visibility in terms of how our software is working. If there are any problems, it surfaces them right away. We get alerts in Slack. It's really an essential tool for a company that provides software as a service.

I really like the replay, the ability to replay sessions, as I'm in sales engineering, so I sometimes need to know what my prospects are doing during a proof of value. I can actually see all the mouse moving and clicking on buttons and stuff like that. I can actually tell what they've been doing. There’s a lot of the other monitoring stuff as well. The development team uses it for monitoring and finds it very helpful.

It’s been kind of in the middle of many different things. The dashboards and the performance of the software have been great.

I haven't really noticed anything that they could improve upon. Maybe they could add in some features to go both ways, to maybe make some configuration changes, etc. That's a little bit outside of what Datadog does, though. It's really very full-featured, so I don't really have any complaints.

I haven't really fully looked at the documentation as I know where I need to go and look at things. It could probably be a little bit of a better user experience. There are so many functions there that sometimes navigating your way around is a little bit hard. They have a really nice menu system. However, there's so much there. It's possible that I skipped a guided tour when I started.

It’s not intuitive to everyone. There are a lot of technical features.

I’ve been using the solution for the last five months. However, the company may have used it for a year and a half.

The solution has been stable and reliable.

We haven’t had a problem with scalability. It’s been good.

We have 25 to 30 users on it currently. Our entire organization is under 60 people. Although not everyone is on it, a lot of our staff are. The sales, engineering, and customer success teams are all on it.

We may increase usage. No doubt that will come naturally with time. We’re hiring more people, and likely new hires will use it.

I have not had occasion yet to reach out to support.

We’ve also been using FullStory.

I wasn’t part of the implementation. The one thing I will say is that when they added the functionality to review sessions, it made our use of another product, FullStory, almost obsolete. I'll have to see if we will continue using FullStory or if we can rely completely on Datadog.

I am a customer and end-user.

We’re on the most recent version and keep it updated.

I’d rate it nine out of ten. The user experience could be slightly better.

My customers were using Datadog for monitoring purposes. They were using it only because the solution is running on AWS and it's a microservices-based solution. They were using an application called Dynatrace for their log.

Most of the features in the way Datadog does monitoring are commendable and that is the reason we choose it. We did some comparisons before picking Datadog. Datadog was recommended based on the features provided.

Most of the monitoring tools nowadays are have or are going to have embedded artificial intelligence and machine learning to make monitoring and logging more proactive and intelligent. Datadog has incorporated some artificial intelligence.

The solution does not require a lot of maintenance.

The solution had all the features we were looking for and we were able to create a central dashboard as per our requirements.

All solutions have some area to improve, and in Datadog they can improve their overall technology moving forward.

I have been using Datadog for approximately four months.

Datadog is a stable solution.

Datadog is a highly scalable solution because it is a SaaS solution. Having this solution be a SaaS is one of its most appealing attributes. When the vendor is going to manage data scaling and everything for you, you are only going to use the solution as per your requirements. Autoscaling is a great feature that they have.

The support from Datadog is exellent. If you're stuck on something or you are facing any issue, support from the vendor itself is available. You will receive a response instantly from the vendor on anything related to the requirement,  issues, or feature you are looking for. The responses have always been in a timely manner.

I rate the technical support from Datadog a five out of five.

I have used other similar solutions to Datadog and when I do a comparison between the other tools Datadog is on top, it is great.

Since Datadog is a SaaS solution we had not deployed the Datadog on-premise or in any Cloud. We were using the SaaS solution from the vendor itself. From the provisioning perspective or from the monitoring and dashboard perspective, we were using Terraform to create the typical monitoring as code. Everything was basically automated, we were not doing anything manually.

If someone wants to set up Datadog on-premise or in any of the Cloud machines, they have to consider a lot of things from the auto-scaling perspective.

My recommendation is Datadog is very good. Your team can mainly focus on the development rather than the solution itself. The vendor is going to take care of auto-scaling and maintenance and everything for you.

I rate Datadog a nine out of ten.

We are evaluating Datadog for observability and monitoring requirements that we have in our company. In our use case, our intention is to provide some kind of framework for multiple app teams to use the tool for our cyber ability and engineering practices.

They have a very good foundation in capturing metrics, logs, and traces. It's a very nice tool for that and it allows you to apply these monitoring tools in almost any technology.

Even if you have several layers, containers, EC2 instances, build machines or whatever you need in your infrastructure, Datadog can integrate with all of them across multiple cloud providers. It's a great product.

One of the improvement opportunities that we have identified in my project concerns how hard it is to manage an organizational structure when you have multiple things in one organization, and you want to provide some kind of isolation between them. At the same time, from the management perspective, you want to see an overall overview of what is happening in your business unit, or as a whole division. This is the kind of limitation we're facing.

I'm not sure what kind of features are in the roadmap right now, but I encourage the development of features for defining your organization, and allowing the visibility of what kind of metrics you can get. Those features would be really useful for us. 

I have been using Datadog for about six months.

It's a very scalable product. Right now we are using the SaaS version, so we don't need to worry about the infrastructure or whatever is needed for the platform it is running on. All the capturing of data is sent to the SaaS product and that can be as scaled as needed.

So far their support is pretty nice. They have established many meetings and training sessions, and they are supporting our requirements very well. I don't have any complaints with Datadog support.

While it is an expensive product, I would rate the pricing level at four out of five. 

Normally, the primary reason why people use these kind of tools is observability, but right from the beginning you have to understand what observability is, what it means for your company, and how the tool is going to help you to capture the proper metrics for making your applications observable.