Datadog Reviews

We're using the product for logging and monitoring of various services in production environments. 

It excels at providing real-time observability across a wide range of metrics, logs, and traces, making it ideal for DevOps teams and enterprises managing complex environments. 

The platform integrates seamlessly with our cloud services, but browser side logging is a little lagging. 

Dashboards are very useful for quick insights, but can be time consuming to create, and the learning curve is steep. Documentation is vast, but not as detailed as I'd like.

The solution has made logging and tracing a lot easier, and the RUM sessions are something we did not have previously. Datadog’s real-time alerting and anomaly detection help reduce downtime by allowing us to identify and address performance issues quickly. 

The platform’s intelligent alert system minimises noise, ensuring your team focuses on critical incidents. This results in faster Mean Time to Resolution (MTTR), improving service availability. 

It consolidates monitoring for infrastructure, applications, logs, and security into a single platform. This enables us to view and analyse data across the entire stack in one place, reducing the time spent jumping between tools.

Real user monitoring has made triaging any possible bugs our users might face a lot easier. RUM tracks actual user interactions, including page load times, clicks, and navigation flows. This gives our organization a clear picture of how our users are experiencing your application in real-world conditions, including slow-loading pages, errors, and other performance issues that affect user satisfaction. We can then easily prioritize these, and make sure we offer our users the best possible experience.

I'm not sure if this is on Datadog, however, Vercel integration is very limited. 

They need to offer better/more customization on what logs we get and making tracing possible on Edge runtime logs is a real requirement. It is extremely difficult, if not completely impossible, to get working traces and logs displayed in Datadog with our stack of Vercel, NexJs, and Datadog. This is a very common stack in front end development and the difficulty of implementing it is unacceptable. Please do something about it soon. Front end logs matter.

I've used the solution for a little over a year.

Our company has a microservice architecture, with different teams in charge of different services. Also, it is a start, which means that we have to build fast and move very fast as well. So before we were properly using DD, we often had issues of things breaking, but without much information on where in our system the breaking happened. This was quite a big-time sync as teams were unfamiliar with other teams' codes, so they needed the help of other teams to debug. This slowed our building down a lot. So implementing dd traces fixed this

DataDog has many features, but the most valuable have become our primary uses.

Also, thanks to frequent concurrent deployments, the DataDog alerts monitors allow us quickly detect issues if anything occurs.

The monitors can be improved. The chart in the monitors only goes back a couple of hours, clunky. Also, it can provide more info, like traces within the monitors. We have many alerts connected to different notification systems, such as Slack and Opsgenie. 

When the on-caller receives notifications fired by the alerts, we are taken to the monitors. Yet often, we have to open up many different tabs to see logs, traces and info that is not accessible on the monitors. I think it would make all of the on callers' lives easier if the monitor had more data

We've used the solution for three years.

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 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.

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.

We use it to monitor and alert our ECS instances as well as other AWS services, including DynamoDB, API Gateway, etc. 

We have it connected to Pagerduty for alerting all our cloud applications. 

We also use custom RUM monitoring and synthetic tests for both our internal and public-facing websites. 

For our cloud applications, we can use Datadog to define our SLOs, and SLIs and generate dashboards that are used to monitor SLOs and report them to our senior leadership.

Datadog has been able to improve our cloud-native monitoring significantly, as CloudWatch doesn't have enough features to create robust, sustainable dashboards that are easily able to present all the information in an aggregated manner in one place for a combination of applications, databases, and other services including our UI applications. 

RUM monitoring is also something we didn't have before Datadog. We had Splunk, which was a lot harder to set up than Datadog's custom RUM metrics and its dashboards.

I really enjoy the RUM monitoring features of Datadog. It allows us to monitor user behavior in a way we couldn't before. 

It's useful to be able to obfuscate sensitive information by setting up custom RUM actions and blocking the default ones with too much data. 

I also like being able to generate custom metrics and monitors by adding facets to existing logging. Datadog can parse logs well for that purpose. The primary method of error detection for our external website is synthetic tests. This is extremely valuable for us as we have a large user base.

At times, it can be hard to generate metrics out of logs. I've seen some of those break over time and have flakey data available. 

Creating a monitor out of the metric and using it in a dashboard to generate our SLIs and SLOs has been hard, especially in cases where the data comes from nested logging facets.

I've used the solution for two years.

The stability is pretty good.

The solution is pretty scalable! It's hard to set up all the infra (terraform code) required to link private links in Datadog to all of our different AWS accounts.

They offer good support. Solutions are provided by the team when needed. For example, we had to delete all our RUM metrics when we accidentally logged sensitive data and the CTO of Datadog stepped in to help out and prioritize it at the time.

Positive

We previously used Splunk and some internal tools. We switched due to the fact that some cloud applications don't integrate well with pre-existing solutions.

The initial setup for connecting our different AWS accounts via Datadog private link wasn't great. There was a lot of duplicate terraform that had to be written. The dashboard setup is way easier.

We installed it with the help of a vendor team.

Our return on investment is great and is so much better than CloudWatch. We can easily integrate with Pagerduty for alerting.

Our company set up the product for us, so the engineers didn't need to be involved with pricing. 

The pricing structure isn't very clear to engineers.

We looked into Splunk and some internal tools.

We use the product for recording loggers on our various services across different teams. For example, we use logs to keep track of info logs for events and error logs to catch exceptions. 

When users ask us to investigate a situation, we use logs to keep track of events and where the user's code traveled to. We also use synthetic testing and monitoring features to keep track of our many alerts in the production and QA environments.

We use Datadog mainly for debugging purposes. For example, we use it to navigate where the code trace is when an issue arises due to its ability to search through the logs. 

We also use it to address user queries. Sometimes users would ask us a certain question concerning our codebase, we use Datadog to track the code stack and also use time monitoring to get an idea of the time frame around when the use case happened.

The feature I have found to be the most valuable is the filtering feature in logs. It is really easy to type plus and minus to filter out different logs. I use it to navigate the noise. 

I use synthetic tests as well. It is easy to navigate the menu and create tests. 

Much of the UI is very straightforward, and I do appreciate the ability to search for any documentation on the various features when I need to as well. The DASH monitoring boards are nice to give an overview of various performances and allow us to track use cases.

This service could be less costly. Right now, we only keep 15 days worth of logs since we want to be more economical in terms of cost. It would be nice if I had the option to monitor logs beyond 15 days. For APM traces, we only keep a year worth of traces. The UI can be a little more straightforward as well. I found it to have too many options.

I've used the solution for three years.

The stability is good.

The scalability is good.

We currently use it for log aggregation and SEIM. We send logs from our AWS account (particularly our Cloudtrail and S3 logs) and use them to give us security signals. 

This has helped with our SOC2 certification process and has given us a window into our processes and the security holes in our system. 

We are also considering using the APM features to help with our development effort. We want to be able to profile all of our code and see what is going on with it.

It has allowed us to see into our systems with ease. We are a very small startup (Less than 30 people, and most of them are in sales and marketing). 

When it comes to managing systems, we just don't have time to do everything. However, Datadog has allowed us to do much more with fewer people and still sift through our data with ease. 

We hope to start using the APM feature set to extend this to our dev teams as well.

The ease of use is the primary aspect. I have used, at previous jobs, the ELK stack and Splunk for log management. Both of them were useful, yet required a lot of manual effort to get set up (and a lot of continuing effort to tweak. A simple monitoring solution turned into a full-time job! However, Datadog has so far been a breeze to use and set up. It looks at what I am sending it and figures out what it is almost by magic. Even the manual configuration makes sense and gives very fast and thorough results

One thing we have run into is that it is so easy to add monitoring that we turn on things without really understanding the costs. 

I would like a way to show a continuous indication of what my setup will cost on a daily or weekly basis.

I've used the solution for six months.