Datadog Reviews

Datadog is expensive. 

The tool's deployment is easy. 

The solution's pricing depends on project volume. 

I rate Datadog a seven out of ten. 

We mainly use the product to monitor our infrastructure and apps. It is the go-to tool when we want to check that things are running properly. We use Datadog synthetic monitors to ensure our app works across different locations in the United States. 

We also have set up Datadog monitors to send alerts if things stop working as expected. 

We use Continuous Integration Pipeline visibility to make sure our developers are not being blocked by infrastructure and other things that might be out of their control.

Datadog helps us detect issues early on and helps in troubleshooting. Creating Service Level Objectives and defining monitors is helping us to stay on top of potential issues that might affect our users. 

We take advantage of Application Performance Monitoring to ensure our applications are working as expected, and our users can get the healthcare they need at a price they can afford. 

Synthetic monitoring also helps us in testing our application in different browsers.

The most valuable aspects of the solution include: 

CI visibility, which helps us in making sure our CI systems are running efficiently and are not blocking our developers from releasing new software and fixing bugs.

Logs, which help us in debugging issues where we can search for logs and can make sure they are relevant to the issues we are looking at.

APM, which can help us to stay on top of our applications by giving us the confidence that our apps are running.

Monitoring. We use monitoring a lot to ensure we know about potential issues and fix them before they affect our customers.

Overall, we really like the quality and relevance of all of the Datadog products that are currently being used. 

The documentation is very well organized and is the go-to place for us to find answers to our questions. 

We would really like to see more from the Service Catalog. It is something that we are interested in. However, some might think it lacks some key features at this time. We will definitely keep our eye out for this and adopt it when all the features are implemented. 

We're really looking forward to all the great things DD will do.

I've used the solution for three years.

The stability is great.

The scalability is great.

Technical support is great.

We handled the initial setup in-house.

I don't have any insights into pricing.

We primarily use the product for tracing, metrics, and alarms in various deployment environments.

The product has provided our company with improved observability, which has helped make the incident response more targeted and quicker.

APM is great and has provided low-effort out-of-the-box observability for various services. 

Monitors are helpful, and definitions are simple. 

Terraform support is nice as it allows us to create homogenous monitoring environments in various deployment environments with little additional effort. It also facilitates version control of monitor definitions, etc. 

The Golang profiler is generally good with the exception of delta profiles; it has provided helpful observability into Heap Allocations which has helped us reduce GC overhead.

Delta traces on the Golang profiler are extremely expensive concerning memory utilization. In a Kubernetes environment where we would like to set per-pod memory allocations as low as possible, the overhead of that profiler feature is prohibitive. In one case, our pods (which were provisioned to target 250 MB and max at 500 MB memory) got stuck in a crash loop due to out-of-memory, which was caused entirely by the delta profiles feature of the profiler.

Multistep Datadog synthetics lack the feature of basic arithmetic. For our use case, performing basic arithmetic on the output of previous steps to produce input for subsequent steps would be extremely useful.

I've used the solution for nine months.

We primarily use the solution for logging and APM, and for real user metrics.

The solution has helped out organization gain improved visibility.

The most useful aspects of the solution include RUM, session replay, and APM.

The product needs a better Datadog agent installation.

I've used the solution for one year.

We previously used App Dynamics.

Before choosing Datadog, we looked at Splunk.

We use Datadog to monitor our Kubernetes clusters. 

We have 3 different clusters for different parts of the SDLC. We run the Datadog agent DaemonSet as well as the Datadog cluster agent. Our services have the APM installed by default. 

To create monitors, we use Terraform. This is provided out-of-the-box for our service owner. 

We run EKS on top of K8s, therefore, we also make use of some of the AWS monitoring capabilities that can be integrated into Datadog. 

We are hugely reliant on Datadog for all aspects of our system.

With Datadog, we were able to gain observability in our system. 

The installation step is pretty straightforward. 

It's easy to use by non-DevOps users. For instance, our engineers do not interact with K8s often; therefore, it is hard for them to debug. However, with Datadog, they are able to view their containers and deployments with a single click. 

We also heavily use the tags to help us identify who the service owners are. This is super useful when we need to track owners for patching or pick up new features we implemented.

The APM and K8s monitoring are the most valuable aspects of the solution. The K8s monitoring allows all customers to view their infra, even if they do not use K8s daily. They can just click on a few tabs to get all of the information they need. 

It is also very easy to install on our system. APM has helped debug applications on our system as well. We were able to view why a service has suddenly shut down.

We also use Datadog for SLOs/SLAs as well. We check the live endpoint of services to ensure they are still up and running.

There is not much that needs to be improved. 

The UI is super user-friendly. The deployment process is easy. We enjoy using the integrations with Slack and PagerDuty. 

Customer support is awesome from our experience. There is a lot of documentation for us to be able to use if we need to. 

I'm not sure if Datadog can monitor K8s deployments in real-time. For instance, being able to see a deployment step by step visually. This would be helpful if there were any incidents during the deployment. 

In general, Datadog is a great solution.

I've used Datadog since I joined my company about a year ago.

We haven't had issues with the stability.

The scalability is really great.

We've had no issues with the product or support. 

The initial setup is super simple, and the documentation was helpful.

We managed the initial setup process in-house.

We've witnessed ROI in our DevOps.

We primarily use Datadog for alerts. If we're running out of database connections or CPU credits we want to find out in Slack. Datadog provides nice features for that.

Secondarily, we use Datadog for analyzing historical trends and forecasting potential issues.

I'm trying to learn how to add in Continuous Profiler in our primary backend servers and set up Synthetic Tests for monitoring our front end.

Everything is mostly on AWS, and the Datadog integrations help a ton.

Datadog has helped us a ton by allowing us to set up a multitude of easily configurable alarms across our tech stack and infrastructure. It doesn't matter if it's in AWS Lambda or a Docker container in AWS EC2, Datadog's intuitive interface makes alarms incredibly easy to configure, reducing our resolution time for incidents.

A lot of the value comes from how frictionless the integrations are. Adding in a Datadog agent or flipping a switch on the Datadog UI to start streaming Lambda data makes the product so incredibly appealing for my company.

The monitoring feature has been the most valuable.

I really like the dashboard. Monitoring has a straightforward tie-in to business value at my company (i.e. declaring incidents, etc). Things like having a dashboard and APM make my job easier. That said DevX is a little bit of a harder sell to executives in my company.

The dashboard feature makes it so easy to inspect multiple metrics at once across services. It's truly been a lifesaver when I'm personally trying to understand why performance degradation is happening.

I found the documentation can sometimes be confusing. I tried configuring APM for some of our Python containers, and I had to cross-reference multiple blog posts and the official documentation to figure out which Datadog-agent to use. If I needed a ddtrace trace, what environment variables I should set, etc. 

Furthermore, to generate my own traces, I wasn't aware that ddtrace adds its own "monkey patching," which led to headaches with respect to configuring the service for RabbitMQ.

A more unified and up-to-date documentation suite would be greatly appreciated.

I've used the solution for about two years.

I don't recall seeing an incident from Datadog in the past couple of years and that's been wonderful.

The solution is incredibly scalable! To be fair, our data throughput to Datadog isn't super huge, however, we have never seen issues as it scaled to handle more of our data.

We used to use AWS Cloudwatch for a lot of our monitoring needs. That said, the interface felt clunky, confusing, and limited.

We don't have hard numbers on ROI. That said, overall, it has been a wonderful addition to our tooling suite.

We also looked at Honeycomb and are currently using both in production.

The product is primarily used for the DevOps team. 

It has helped us build pipelines for ops review and other functions.

Datadog is providing efficiency in the products we develop for the wireless device engineering department. We had to provide more developer integration tools and also needed to help in creating easy queries that would help in creating efficient toolsets for management to make decisions based on these metrics.

We need more integration functionality, including certain metrics integration. We should be able to monitor devs and need it to build more monitoring tools and offer leadership metrics.

I've used the solution for almost six months.

Our use case is mainly deploying into our applications for monitoring/logging observability. We currently have our microservices feed into an actuator that exists in each instance of our application that extends to a local and central Grafana for client and internal visibility. The application we use is Grafana.

Logging captures application and system logs that are ported to each application instance for querying.

Whenever anything occurs that is considered unhealthy from a range of health checks, we have notification rules configured internally and externally for a prompt response time.

We have been able to be a more confident, knowledgeable, and capable team when everything is being ported into a centralized format. Beforehand, knowledge was isolated to individuals. Knowledge in terms of what information represented and where it was led to a lack of confidence. By having everything in one place, rules out that confusion and allows us to respond better to issues.

It also allows for personal growth as our team is learning the application from the ground up, and each person is enhancing their own skills.

The valuable features include the following: 

• We are currently utilizing a decentralized distributed framework for our deployment, including our monitoring/logging observability capabilities. Centralizing them, if contingent on our company privacy guidelines, will be a big help in tracking and responding to issues that come up and have the means to understand the origin of the log management tools that were demonstrated.
• The ability to fiddle around and manipulate how logs are outputted.
• The ability to track AWS Lambda functions, Cloudformation, and Cloudwatch allow someone that is not savvy to dip their toe into understanding their own product.
• The integration into AWS is key as well as our software is currently bound to AWS.

The ability to find what you are looking for when starting out could be improved. It was a bit overwhelming trying to figure out what is the best solution. It led to many prototypes or time spent just perusing documentation. If we were able to select bundles or template use cases, we would hit the ground running quicker.

I've used the solution for one year.