Datadog Reviews

We are trying to get a handle on observability. Currently, the overall health of the stack is very anecdotal. Users are reporting issues, and Kubernetes pods are going down. We need to be more scientific and be able to catch problems early and fix them faster.

Given the fact that we are a new company, our user base is relatively small, yet growing very fast. We need to predict usage growth better and identify problem implementations that could cause a bottleneck. Our relatively small size has allowed us to be somewhat complacent with performance monitoring. However, we need to have that visibility.

We are still taking baby steps with Datadog. Hence, it's hard to come up with quantifiable information. The most immediate benefit is aggregating performance metrics together with log information. Having a better understanding of observability will help my team focus on the business problems they are trying solve and write code that is conducive to being monitored, instead of reinventing the wheel and relying on their own logic to produce metrics that are out of context

The most useful feature is the APM. Being able to quickly view which requests are time-consuming, and which calls have failed is invaluable. Being able to click on a UI and be pointed to the exact source of the problem is like magic. 

I'm also very intrigued by log management, although I haven't had quite a chance to use it very effectively. In particular, the trace and span IDs don't quite seem to work for me. However, I'm very keen on getting this to work. This will also help my developers to be more diligent and considerate when creating log data.

As a new customer, the Datadog user interface is a bit daunting. It gets easier once one has had a chance to get acquainted with it, yet at first, it is somewhat overwhelming. Maybe having a "lite" interface with basic features would make it easier to climb the learning curve.

Maybe the feature already exists. However, I'm not sure how to keep dashboard designs and synthetic tests in source control. For example, we may replace a UI feature, and rebuild a test accordingly in a pre-production environment, yet once the code is promoted to production, the updated test would also need to be promoted.

We have just started using the solution and have only used it for about two months.

We're new at this. That said, so far, there haven't been any issues to report.

I have not had the opportunity to evaluate the scalability.

Customer support is full of great folks! We're beginning our Datadog journey, so I haven't had that much experience. The little I have had has been great.

Positive

This is all new. 

We used to work with New Relic. New Relic has an amazing APM solution. However, it also became cost-prohibitive

Since we are relatively greenfield, it was relatively painless to set up the product. 

Our in-house DevOps team did the implementation.

I don't know what the ROI is at this stage.

I'm not sure what the exact pricing is. 

So far, it's been great!

The product is used for APM solutions for the metrics and traces for the REST API requests and service maps to understand the upstream and downstream services.

We are creating dashboards and widgets to monitor the status. We are creating alerts and monitors as well. We integrated the alerts and ticketing system in our organization with SNOW and Netcool.

We are using Kubernetes, AWS, and infrastructure metrics. We are using Kafka and Aurora Postgres logs as well, and we are using HTTP status codes to identify the error types.

So far, the solution works very well and solves most of the problems we have. Currently, we are trying to integrate the trace ID into Datadog and correlate the logs and metrics. However, Datadog is not supporting the spring-generated trace IDs, and they are not shown in the Datadog UI. It works in reverse. This means Datadog injects the DD-specific trace ID into the application logs, and those logs can be in other tools, for example, Cloud Watch and Splunk. 

The most valuable aspect is the APM which can monitor the metrics and latencies. There's a low error rate, and any alerts can be tagged to the service requests and sent via email to the required DLs. 

We can create incidents as well in our internal tools, like SNOW and Netcool.

The monitoring enables different dimensions of metrics to monitor the services and infrastructure. 

We have cloud infrastructure monitoring in Kubernetes nodes, pods containers, and ingress metrics.

Alerts are sent to an email in case of any issues. The metrics are used to create alerts.

The solution offers good dashboards, service maps, traces and flame graphs, HTTP status codes, power packs, service catalogs, and profiling.

While the logs module is not activated, we are using all other modules.

The correlation between the logs and the metrics needs improvement as most cases, we might use another logging tool (that is cheaper in cost) which then we have to link together. 

They can improve the SSO logging as well. Currently, we are logging in every two to three days by sending the login link explicitly.

I've been using the solution for two years. 

The stability is awesome. 

We are expanding beyond observability right now.

They offer pretty awesome customer support.

Positive

We did not previously use a different solution.

The initial setup was easy.

We implemented the solution with the help of a vendor team.

I'd rate the ROI ten out of ten.

I would recommend Datadog to others.

We also evaluated ECE and Splunk.

The solution has a great support model.

We use the solution for application hosting and a little bit of everything when it comes to supporting a worldwide logistics tracking service. It's used as a central service for collecting telemetrics and logs. We find it does the same work as all of our old tools combined, including Prometheus, Kibana, Google Logs, and more; putting all of this information in a single platform makes it easy to corroborate information and associate a request with the data, which might be lost when it is saved as logs.

At my organization, we have plenty of microservices written in different languages. Different teams prefer one or the other framework or library within those languages.

With Datadog, we can get in a single line and march in the same direction; our logs and metrics are collected in the same fashion, making it easy to find bugs or integration problems across services and understand how they interact with other systems.

I primarily prefer to utilize the profiling and tracing feature. It can potentially be used as a more-informed alternative to logs.

Beyond that, anything I've wanted to do, I found a way to get it done through Datadog. It allows for testing, logging, hardware monitoring, system performance, memory consumption, advanced observability, AI assistance, cross-team collaboration, and business analytics. Datadog helps some of the world’s biggest brands transform faster with the help of true AIOps, AI-assisted answers, UX and business analytics, cloud observability, and smart AI assistance.

It's all supporting my desire to build a great application, and in a centralized SaaS application, it's hard to say anything can beat it.

The storage of logs is a little bit unexpected; most services generate gigabytes of logs, and their size is not excessive. When it comes to storing the logs with Datadog, I'm not sure why it costs so much to store gigabytes or terabytes of information when it's a fraction of the cost to do so myself.

I've used the solution for one year.

We have no concerns with stability.

It appears to be that there are no issues with scaling.

Technical support is slow. It takes forever to get responses from the support team.

Neutral

I've previously used Kibana and Prometheus. We are still using these.

Setting up through the environment variables made it unbelievably easy to get started.

We've implemented the solution in-house.

I do not have this number off-hand, as I am not the finance guy. I just like the product.

I'd advise new users not to start off by sending logs.

We did not really look at other options.

We are using the solution for migrating out of the data center. Old apps need to be re-architected. We plan to move to multi-cloud for disaster recovery and avoid vendor lockouts. The migration is a mix between an MSP (Infosys) and in-house devs. The hard part is ensuring these apps run the same in the cloud as they do on-prem. Then we also need to ensure that we improve performance when possible. With deadlines approaching quickly, it is important not to cut corners which is why we needed observability.

The product has created a paradigm shift in how we deploy monitoring. Before, we had a one-to-one lookup in service now. This wouldn't scale, as teams wouldn't be able to create monitors on the fly and would have to wait on us to contact the ServiceNow team to create a custom lookup. Now, in real-time, as new instances are spun up and down, they are still guaranteed to be covered by monitoring. This used to require a change request, and now it is automatic.

For use, the most valuable features we have are infrastructure and APM metrics. The seamless integration between Datadog and hundreds of apps makes onboarding new products and teams a breeze. 

We rely heavily on the API crawlers that Datadog uses for cloud integrations. These allow us to pick up and leverage the tags teams have already deployed without having also to make them add them at the agent level. Then we use Datadogs conditionals in the monitor to dynamically alert hundreds of teams, and with the ServiceNow integration, we can also assign tickets based on the environment. Now, our top teams are using APM/profiler to find bottlenecks and improve the speed of our apps.

The real issue with this product is cost control. For example, when logs first came out, they didn't have any index cuts. This leads to runaway logs and exploding costs. 

It seems that admin cost control granularity is an afterthought. For example, synthetics have been out for over four years, yet there are no ways to limit teams from creating tests that fire off every minute. If we could say you can't test more than once every five minutes that would save us 5X on our bill.

I've been using the solution for about three years. 

The solution is very stable. There are not too many outages, and they fix them fast.

It is easy to scale. It's why we adopted it. 

Before premium support, I would avoid using them since it was so bad.

Neutral

We previously used App Dynamics. It isn't built for the cloud and is hard to deploy at scale.

The initial setup was not complex. We just had to teach teams the concept of tags.

We implemented the solution in-house. It was me. I am the SME for Datadog at the company.

We have seen an ROI. It has saved months of time and reduced blindspots for all app teams.

We'd advise new users to be careful with logs, and the APM as those are the ones that can get expensive fast.

We looked into Dynatrace. However, we found the cost to be high.

We share dashboards, set up alerts, and monitor everything that happens in our system. We use it in staging, features, production, and our load test environment. It is exceptionally helpful for making our engineering more data-driven. 

I came from a company that believes we should focus on being telemetry driven. Instilling this in a smaller, less mature engineering organization has been challenging. However, it is much easier while using Datadog.

The dashboards are great. They are an easy way to give visibility into what we need to watch with others who are not SMEs.

I enjoy the custom metrics. With this, we can take things that were once logs and then retain them longer.

We are able to parse logs. To be honest, this was only useful due to the fact that we had not yet set up the Datadog agent properly in PHP. Once we did this, the Datadog log parsing was no longer needed.

The ability to pin to a date and time is very helpful. This allows us to pinpoint exactly what was happening.

We need more advanced querying against logs. While most issues I have had here can be alleviated by way of sending better-formatted logs, it would be cool to do SQL-type queries against our data.

We need a way to see dashboard metadata. We launched a huge customer, and we saw more people using Datadog than ever across the entire organization, yet had no way to tell.

It would be ideal if we had some way to compare arbitrary date times more easily. We would love to use the Diff Graph command against some hard-coded value, for instance, against some known event.

I've used the solution for eight months.

The scalability is great!

We previously used New Relic. I was not part of the decision-making team that made the switch.

The ROI is the speed at which we can debug live sites. It has been excellent. It's amazing how many incidents we can capture before customers notice.

We looked into New Relic and a home-brewed solution as potential other options.

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. 

Datadog is a SaaS solution we tried for URL and synthetic monitoring. You record a transaction going into a website and replay that transaction from various locations. Datadog is mainly used by the admin, but three or four other guys had access to the reports and notifications, so it's five altogether.  

We probably tried no more than 8 percent of what Datadog can do. There are so many other bits and modules. I've only gone into about half of what APM can do in the Datadog stack.

We could detect outages on particular websites or problems in specific locations. If I had paid for the full solution, I'm sure I could get a lot of value out of Datadog.

I like the amount of tooling and the number of solutions they sold with their monitoring. Datadog was highly intuitive to use. 

Datadog needs more local Asia-Pacific support, and if they don't have a SaaS solution in Asia-Pacific, they should offer an on-prem version. I'm told that's not possible. 

I have used Datadog for about two or three years.

I was only using Datadog to monitor on a small scale. 

I'd rate Datadog support four out of 10. It was primarily an issue with support in the Asia-Pacific region. I sent them several emails, and they responded around three weeks later. 

They said it went around the houses. Nobody knew who to respond to. That's not good enough. They should have at least told me they'd received the email. I used to work in support.

Neutral

We were just trying Datadog, and we've switched temporarily to Site24x7. We're looking for one of the bigger ones. They've all given us proposals, whereas Datadog hasn't come forward with a proposal for what they could do.

I used Datadog because I already had a relationship with them at a previous company. However, that guy's moved on now, and I wanted to see how good they were. 

Setting up Datadog is pretty straightforward. I have a lot of experience doing that sort of thing. It took maybe a day and a half to deploy because I was picking externally facing websites.

I deployed it by myself. One person is enough for the small system we had. However, if we were moving forward, I'd recommend at least two or three people to manage it. 

Datadog would've cost around $850 a month based on the loads we were doing, and you could estimate roughly what you would be paying monthly. I liked their pricing model. It was flexible, so you only paid for what you used. I rate Datadog pricing eight out of 10. 

We looked at several URL and APM monitoring solutions like Site24x7 and Pingdom. They weren't big players like Dynatrace or any of the those that had already provided us a request for information. 

Even with our negative experiences, I'd still give Datadog an eight out of 10. Datadog is a complete solution with easy-to-use templates and excellent scalability. People should know exactly what they're going to configure before they try it out. The trial is brief. Don't start a trial until you know exactly what you're going to do. 

You must be certain that you can meet any internal security requirements. If you're in the Asia-Pacific region, you might not be able to run something that's running abroad.

The most valuable features of Datadog are the flexibility and additional features when compared to other solutions, such as AppDynamics and Dynatrace. Some of the features include AI and ML capabilities and cloud and analysis monitoring 

Datadog could improve the flexibility with AI and ML concepts. This will allow customers to be more leveraged towards publishing.

I have been using Datadog for approximately one year.

Datadog is stable. We did not have a single outage.

I have found Datadog to be scalable.

We have approximately 2,000 users using the solution in my organization.

The support from Datadog is excellent.

I have previously used AppDynamics and Dynatrace. 

Datadog's initial setup is easy because they have helped us come up with the easiest way of instrumenting any of the features which need to be deployed.  We worked on it with their engineers and we were able to happily do it. We have done approximately 60 application monitoring through Datadog since our deployment.

We have a very tiny team of four members that do the maintenance of Datadog.

The price of Datadog is reasonable. Other solutions are more expensive, such as AppDynamics.

Datadog is far better than any other monitoring tool in introducing any of the new capabilities because they think before Amazon AWS and Microsoft Azure before they introduce the concepts. Datadog is a good tool to have for monitoring your own infrastructure.

I rate Datadog a ten out of ten.