Datadog Reviews

• Monitoring
• Analytics
• Tracing
• APM

It's hosted. We don't have to do it, and they handle a large amount of data with backups and all of the other things that we no longer have to manage. 

There are things about it that we would like to be fixed, such as it is taking averages of average. This results in data that we don't expect, but overall we are happy with it.

It is incredibly stable.

We have had no issues with scalability.

We have needed technical support because we were dealing with averages of averages.

The integration and configuration are incredibly simple. The SaaS offering is remarkably easy to set up, especially if you're coming from a Graphite environment or anything that uses a StatsD. Datadog is a custom StatsD client, and it adds additional functionality, like tags, etc. However, out-of-the-box should work with native StatsD, so it is incredibly easy to drop in replace if you are using StatsD for metrics.

In terms of employee time: While the instructor costs were transferred to Datadog, it freed up our engineers to work on things which were of valuable to our business rather than maintaining a service that we don't make money on.

It costs the same amount it would if we were hosting it ourselves, so we are incredibly happy with the cost.

We did look at several vendors. What it came down to is we did not want to manage the metric services ourselves anymore, and Datadog matched what it cost for us to host it ourselves.

Check out Datadog. It is awesome.

The ingestion points are unlimited and support customization. We haven't had anything yet that we haven't been able to integrate with it.

We have only used the SaaS offering, but not AWS nor on-premise.

We mainly use it to send metrics about CV and memory usage, in addition to the number of files descriptors on a socket.

We are working as an SMS segregator. Therefore, we send a lot of SMS message to customers. This product holds one of the most important dashboards for our traffic from each server or cluster on our Gateway. It gives us very good information, mainly for the operations team and other sales guys, about what each account is sending, how often, etc.

Using the data, our operation teams works with the dashboards to get their statistics, analytics, etc.

The ability to create dashboards and matrices with graphs. This information is useful to us.

I would like testing for data in the future. That would be really nice.

Also, I would like some additional enhancement in the visuals.

The stability is good. For every message that we send, we have a corresponding metric. We send one to two million messages per server a day.

The scalability is good.

We have over a 1000 accounts on three servers. There are other servers, which work as a helper server. However, the servers which help aid traffic or do the heavy lifting are three main servers, currently. They are hosted on Amazon: large machines with large instances.

I have not used Datadog's technical support.

The AWS integration and configuration is pretty good. It has multiple languages and platforms.

Give it a try. It is a good tool for creating statistics and analytics with data. 

Anyone who uses a large amount of data and want insights on the analytics of their data. They can just dump into the tool, and it will do all the heavy lifting.

We primarily use the solution for monitoring applications and informing customers via Pagerduty and Statuspage. The monitoring and alerts can be personalized internally, and we are able to find problems and issues. The response time monitor has been great, and it has been validating upgrades. We can check in to see which step fails,

Previously, we had monitors scattered with different places and products, making troubleshooting harder and slower. Also, logs and monitors were on different platforms, making it harder to put the infrastructure puzzle together.

Datadog documentation on web pages has improved a lot and is pretty easy to follow and find.

Additionally, integrations with, for example, GCP, Network, component, and Software providers are much easier as everything is now centralized.

API and notification integrations are also a great benefit for our organization.

Datadog is listening actively for customer feedback and develops improvements for us effectively.

The most valuable features of the solution include the APM, log monitor, SQL monitors, network monitors, and integrations.

Alerting timing should be improved to be more fine-tuned and exact. The current problem is that monitoring is integrated with the Statuspage and the SLA.

Also, browser support for browsers other than Chrome should be added. Browser test recording is another problem, as it does not always work in normal mode. One needs to use incognito mode or a pop-up.

I've been using the solution for three years.

We primarily use the solution for security monitoring and anomaly detection.

The Datadog suite has allowed us to easily integrate log collection into all of our services and quickly detect unexpected changes in system data to declare security incidents.

The dashboards are great. They allow for the visualization of log data over time to detect anomalous behavior.

The ease of implementation needs improvement. I did not personally experience the initial deployment of Datadog in our environment, however, after some conference workshops, some of the pain points with deployment and turning on certain settings or features were revealed. I would not know how to deal with these issues (or sometimes where to begin) if I were starting from scratch.

Datadog is very easy to deploy at scale when implemented early in the deployment process and if made a deployment requirement.

We did not previously use a different solution. 

I was not involved with the initial setup. 

The solution is deployed as a SaaS.

We use it for our infrastructure network and servers.

From our customer's and our perspective, it has not brought much change in terms of the process. Datadog did not change anything about the process. It only provided a single tool instead of having to use multiple tools.

Its logs are most valuable.

Its pricing model can be improved. Its settings should be improved for a better understanding of billing. They should also provide some alerts when there is an increase in usage. For example, if there is a 20% more increase from one week to another, the customer should get an alert.

I have been using this solution for two years.

It is pretty stable.

The perimeter in which we worked was not big enough to get any information about scalability.

Their technical support is fine.

One of our customers was using another solution. They switched because of the scalability of the perimeter. Their previous solutions were limited in terms of the kind of servers and virtual machines. They had multiple tools, and Datadog provided them a single tool for architecture and infrastructure.

Its initial setup is complex.

We installed it ourselves.

I am not satisfied with its licensing. Its payment is based on the exported data, and there was an explosion of the data for three or four weeks. My customer was not alerted, and there was no way for them to see that there has been an explosion of data. They got a big invoice for one or two months. The pricing model of Datadog is based on the data. The customer was quite surprised about not being alerted about this explosion of data. They should provide some kind of alert when there is an increase in usage.

We evaluated various in-house and external options.

I would advise others to take a step-by-step approach rather than a from-zero-to-everything approach.

I would rate Datadog an eight out of ten.

We use Datadog incident management for our incident tooling. Whenever we run into an incident, we try to use it. It allows us to create a separate Slack channel for it.

Keeping track of incidents makes it possible to share learnings and follow-up. It is also required for SOC2 compliance.

It is great that creating an incident is possible from Slack while having all the relevant data in Datadog. It allows us to not think too much about organizing incidents, which is a highly stressful situation.

I would like the tooling to have better integration in Slack, specifically sending out reminders to the relevant people to take breaks, do a retrospective, and specify with emojis which messages to log.

Learning about the tooling could also be improved. It is a bit clunky to use, especially in an incident. It could definitely be more streamlined.

I've used the solution for about a year.

Sometimes it's more user friendly for development teams. There are some parts of Datadog that are more understandable for development teams. For example, the APM in Datadog works more manually and works like the tools in New Relic or Grafana or Elastic. It is easier to understand for software development teams. 

It could use some additional features when working with metrics like Grafana or like New Relic has. Datadog does not use library technologies like Dynatrace does. Datadog has machine learning too, but it does not have this option in all layers of monitoring like infrastructure service process in applications. 

I've used Datadog for about one month. 

Datadog is a great tool, but it does not cover 100% of all libraries and technologies, like Dynatrace does.

I give Datadog an eight out of ten. 

We use this solution to monitor our Kubernetes clusters, nodes, deployments, daemon sets, replica sets, and pods.

I've been using this solution for a few months at my current company as a member of the Kubernetes team we use Datadog to provide monitoring and telemetry for our team and our customers.

This solution has improved our organization by giving us deeper insight into what's running in our clusters and their performance of it.

I have found the logging and tracing features the most valuable.

The documentation could be improved regarding setting up the agent properly and debugging.

I've been using this solution for a few months at my current company.