Datadog Reviews

We use Datadog for monitoring the performance of our infrastructure across multiple types of hosts in multiple environments. We also use APM to monitor our applications in production. 

We have some Kubernetes clusters and multi-cloud hosts with Datadog agents installed. We have recently added RUM to monitoring our application from the user side, including replay sessions, and are hoping to use those to replace existing monitoring for errors and session replay for debugging issues in the application.

We have been using Datadog since I started working at the company ten years ago and it has been used for many reasons over the years. Datadog across our services has helped debug slow performance on specific parts of our application, which, in turn, allows us to provide a snappier and more performant application for our customers. 

The monitoring and alerting system has allowed our team to be aware of the issues that have come up in our production system and react faster with more tools to debug and view to keep the system online for our customers.

Datadog infrastructure monitoring has helped us identify health issues with our virtual machines, such as high load, CPU, and disk usage, as well as monitoring uptime and alerting when Kubernetes containers have a bad time staying up. Our use of Datadog's Application Monitoring, APM over the last six years or so has been crucial to identifying performance and bottleneck issues as well as alerting us when services are seeing high error rates, which have made it easier to debug when specific services may be going down.

We have found that some of the different options for filtering for logs ingestion, APM traces and span ingestion, and RUM sessions vs replay settings can be hard to discover and tough to determine how to adjust and tweak for both optimal performance and monitoring as well as for billing within the console. 

It can sometimes be difficult to determine which information is documented, as we have found inconsistencies with deprecated information, such as environment variables within the documentation.

I've been using the solution for ten years.

The solution seems pretty stable, as we've been using it for more than a decade.

The solution seems quite scalable, especially within Kubernetes. Costs are a factor.

SUpport has been very helpful whenever we need it.

Positive

We had tried some other APM monitoring in the past, however, it was too expensive, and then we added it to Datadog since we were already using Datadog and it seemed like a good value add.

The solution is straightforward to set up. Sometimes, it is complex to find the correct documentation.

We handled the setup in-house.

 Our ROI is ease of mind with alerts and monitoring, as well as the ability to review and debug issues for our customers.

Getting settled on pricing is something you want to keep an eye on, as things seem to change regularly.

We used New Relic previously.

Datadog is a great service that is continually growing its solution for monitoring and security. It is easy to set up and turn on and off its features once you have instrumented agents and tailored solutions to your needs.

The current use case for Datadog in our environment is observability.  We use Datadog as the primary log ingestion and analysis point, along with consolidation of application/infrastructure metrics across cloud environments and realtime alerting to issues that arise in production.  

Datadog integrates within all aspects of our infrastructure and applications to provide valuable insights into Containers, Serverless functions, Deep Logging Analysis, Virtualized Hardware and Cost Optimizations.

Datadog improved our observability layer by creating a consistent, centralized service for all of our varied cloud-based applications. All of our production and non-production environment applications and infrastructure send metrics directly to Datadog for analysis and determination of any issues that would need to be looked at by the Infrastructure, Platform and Development teams for quick remediation. Using Datadog as this centralized Observability platform has enabled us to become leaner without sacrificing project timelines when issues arise and require triage for efficient resolution.

All of Datadog's features have become valuable tools in our cloud environments.

Our primary alerts, based on metrics and synthetic transactions, are the most used and relied upon for decreased MTTA/MTTR across all of our platforms. This is followed by deep log analysis that enables us to quickly and easily get to a preliminary root cause that someone on the infrastructure, platform or development teams can take and focus their attention on the precise target that Datadog revealed as the issue to be remediated.

The two areas I could see needing improvement or a feature to add value are building a more robust SIM that would include container scanning to rival other such products on the market so we do not need to extend functionality to another third-party provider. The other expands the alerting functions by creating a new feature to add direct SMS notifications, on-call rotation scheduling, etc., that could replace the need to have this as an external third party solution integration. 

I've been a Datadog user for almost ten years.

Datadog is very stable, and we've only come across a few items that needed to be addressed quickly when there were issues.

Scalability is very favorable, aside from cost/budget, which limits the scalability of this platform.

Both customer service and support need a little work, as we have had a number of requests/issues that were not addressed as we needed them to be.

Neutral

Being an Observability SME, I have used many native and third party solutions, including Dynatrace, New Relic, CloudWatch and Zabbix. As previously mentioned, Datadog provides a superior platform for centralizing and consolidating our Observability layer. Switching to Datadog was a no-brainer when most other solutions either didn't provide the maturity of functions, or have them available, at all.

The initial setup was very straightforward, and the integrations were easily configured.

We implemented Datadog in-house.

For the most part, Datadog's ROI is quite impressive when you consider all of the features and functions that are centralized on the platform. It doesn't require us to purchase additional third-party solutions to fill in the gaps.

The setup was dead simple once the cloud integrations and agent components were identified and executed. Licensing falls into our normal third-party processes, so it was a familiar feeling when we started with Datadog. Cost is the only outlier when it comes to a perfect solution. Datadog is expensive, and each add-on drives that cost further into the realm of requiring justifications to finance expanding the core suite of features we would like to enable.

Yes, we evaluated several competing platforms that included Dynatrace, New Relic and Zabbix.

They should provide more inclusive pricing, or an "all you can eat" tier that would include all relevant features, as opposed to individual cost increases to let Datadog to become more valuable and replace even more third-party solutions that have a lower cost of entry.

We are using Datadog to improve our monitoring and observability so we can hopefully improve our customer experience and reliability.  

I have been using Datadog to build better actionable alerts to help teams across the enterprise. Also by using Datadog we are hoping to have improved observability into our apps and we are also taking advantage of this process to improve our tagging strategy so teams can hopefully troubleshoot incidents faster and a much reduced mean time to resolve. 

We have a lot of different resources we use like Kubernetes, App Gateway and Cosmos DB just to name a few.

As soon as we started implementing Datadog into our cloud environment people really like how it looked and how easy it was to navigate. We could see the most data in our Kubernetes environments than we ever could. 

Some people liked how the logs were color coded so it was easy to see what kind of log you were looking at. The ease of making dashboards has also been greatly received as a benefit. 

People have commented that there is so much information that it takes a time to digest and get used to what you are looking at and finding what you are looking for. 

The selection of monitors is a big feature I have been working with. Previously with Azure Monitor we couldn't do a whole lot with their alerts. The log alerts can sometimes take a while to ingest. Also, we couldn't do any math with the metrics we received from logs to make better alerts from logs.  

The metric alerts are ok but are still very limited. With Datadog, we can make a wide range of different monitors that we can tweak in real time because there is a graph of data as you are creating the alert which is very beneficial. The ease of making dashboards has saved a lot of people a lot of time. No KQL queries to put together the information you are looking for and the ability to pin any info you see into a dashboard is very convenient. 

RUM is another feature we are looking forward to using this upcoming tax season, as we will have a front-row view into what frustrates customers or where things go wrong in their process of using our site. 

The PagerDuty integration could be a little bit better. If there was a way to format the monitors to different incident management software that would be awesome. As of right now, it takes a lot of manipulating of PagerDuty to get the monitors from Datadog to populate all the fields we want in PagerDuty.  

I love the fact you can query data without using something like KQL. However, it would also be helpful if there was a way to convert a complex KQL query into Datadog to be able to retrieve the same data - especially for very specific scenarios that some app teams may want to look for.

I've used the solution for about two years.

We previously used Azure Monitor, App Insights, and Log Analytics.  We switched because it was a lot for developers and SREs to switch between three screens to try troubleshoot and when you add in the slow load times from Azure it can take a while to get things done.

I would advise taking a close look at logging costs, man-hours needed, and the amount of time it takes for people to get comfortable navigating Datadog because there is so much information that it can be overwhelming to narrow down what you need.

We did evaluate DynaTrace and looked into New Relic before settling on Datadog.

We use the solution to monitor and investigate issues with production services at work. We're periodically reviewing the service catalog view for the various applications and I use it to identify any anomalies with service metrics, any changes in user behavior evident via API calls, and/or spikes in errors.  

We use monitors to trigger alerts for on-call engineers to act upon. The monitors have set thresholds for request latency, error rates, and throughput. 

We also use automated rules to block bad actors based on request volume or patterns.

Datadog has made setting up monitors easier, more reliable, and more transparent. This has helped standardize our on-call process and set all of our on-call engineers up for success.  

It has also standardized the way we evaluate issues with our applications by encouraging all teams to use the service catalog.  

It makes it easier for our platforms and QA teams to get other engineering teams up to speed with managing their own applications' performance. 

Overall, Datadog has been very helpful for us.

The service catalog view is very helpful for periodic reviews of our application. It has also standardized the way we evaluate issues with our applications.  Having one page with an easy-to-scan view of app metrics, error patterns, package vulnerabilities, etc., is very helpful and reduces friction for our full-stack engineers.

Monitors have also been very valuable when setting up our on-call processes. It makes it easy to set up and adjust alerting to keep our teams aware of anything going wrong.

Datadog is great overall. One thing to improve would be making it easier to see common patterns across traces. I sometimes end up in a trace but have a hard time finding other common features about the error/requests that are similar to that trace. This could be easier to get to; however, in that case, it's actually an education issue.  

Another thing that could be improved is the service list page sometimes refreshes slowly, and I accidentally click the wrong environment since the sort changes late.

I've used the solution for about a year.

It is very stable. I have not seen any issues with Datadog.

It seems very scalable.

I've had no specific experience with technical support.

Neutral

We used Honeycomb before. We switched since Datadog offered more tooling.

Each application has been easy to instrument.

We implemented the solution in-house.

Engineers save an unquantifiable amount of time by having one standard view for all applications and monitors.

I am not exposed to this aspect of Datadog.

We did not evaluate other options. 

We use the solution to monitor production service uptime/downtime, latency, and log storage. 

Our entire monitoring infrastructure runs off Datadog, so all our alarms are configured with it. We also use it for tracing API performance; what are the biggest regression points. 

Finally we use it to compare performance on SEO metrics vs competitors. This is a primary use case as SEO dictates our position from google traffic which is a large portion of our customer view generation so it is a vital part of the business we rely on datadog for.

The product improved the organization primarily by providing consistent data with virtually zero downtime. This was a problem we had with an old provider. It also made it easy to transition an otherwise massive migration involving hundreds of alarms. 

The training provided was crucial, along with having a dedicated team that can forward our requests to and from Datadog efficiently. Without that, we may have never transitioned to Datadog in the first place since it is always hard to lead a migration for an entire company.

The API tracing has been massive for debugging latency regressions and how to improve the performance of our least performant APIs. Through tracing, we managed to find the slowest step of an API, improve its latency, and iterate on the process until we had our desired timings. This is important for improving our SEO as LCP, INP are directly taking from the numbers we see on Datadog for our API timings. 

The ease of dashboard creation and alarm monitoring has helped us not only stay competitive but be industry leaders in performance.

The product can be improved by allowing the grouping of APIs to add variables. That way, any API with a unique ID could be grouped together. 

Furthermore, SEO monitoring has been crucial for us but also a difficult part to set up as comparing alarms between us and competitors is a tough feat. Data is not always consistent so we have been toying and experimenting with removing the noise of datadog but its been taking a while. 

Finally, Datadog should have a feature that reports stale alarms based on activity.

I've used the solution for six months.

Its very stable and we have not experienced an issue with downtime on Datadog.

Datadog works well for scalability as volume has not seemed to slow.

We haven't talked to the support team. 

Positive

We switched to Datadog as we used to have a provider that had very inconsistent logging. Our alarms would often not fire since our services were not working since the provider had a logging problem.

The initial setup was somewhat complex due to the built-in monitoring with services. This is not always super comprehensive and has to be studied as opposed to other metrics platforms that just service all your endpoints, which you can trace them with Grafana.

We implemented the solution through an in-house team.

The ROI is good.

Users must try to understand the way Datadog alarms work off the bat so that they can minimize the requirements for expensive features like custom metrics. 

It can sometimes be tempting to use them; however, it is not always necessary as you migrate to Datalog, as they are a provider that treats alarms somewhat differently than you may be used to.

We have evaluated New Relic, Grafana, Splunk, and many more in our quest to find the best monitoring provider.

We were looking for an all-in-one observability platform that could handle a number of different environments and products. At a basic level, we have a variety of on-premises servers (Windows/Mac/Linux) as well as a number of commercial, cloud-hosted products. 

While it's often possible to let each team rely on its own means for monitoring, we wanted something that the entire company could rally around - a unified platform that is developed and supported by the very same people, not others just slapping their name on some open source products they have no control over.

Datadog has effortlessly dropped in to nearly every stage of observability for us. We appreciate how it has robust cross-platform support for our IT assets, and for integrating hosted products, enabling integrations often couldn't be easier, with many of them including native dashboards and even other types of content packs. 

Over the last couple of years, we have onboarded a number of engineering teams, and each of them feels comfortable using Datadog. This gives us the ability to build organizational knowledge.

Datadog's learning platform is second to none. It's the gold standard of training resources in my mind; not only are these self-paced courses available at no charge, but you can spin up an actual Datadog environment to try out its various features. 

I just hate when other vendors try to upsell you on training beyond their (often poorly-written) documentation. Apart from that, we appreciate the variety of content that comes from Datadog's built-in integrations - for common sources, we don't have to worry about parsing, creating dashboards, or otherwise reinventing the wheel.

Datadog's roadmap can be a bit unpredictable at times. For instance, a few years ago, our rep at the time stated that Datadog had dropped its plans to develop an incident on-call platform. However, this year, they released a platform that does exactly that.

They also decided to drop chat-based support just recently. While I understand that it's often easier to work with support tickets, I do miss the easy availability of live support. 

It would be nice if Datadog continued to broaden its variety of available integrations to include even more commercial platforms because that is central to its appeal. If we're looking at a new product and there isn't a native integration, then that's more work on our part.

Our primary use of Datadog involves monitoring over 50 microservices deployed across three distinct environments. These services vary widely in their functions and resource requirements. 

We rely on Datadog to track usage metrics, gather logs, and provide insight into service performance and health. Its flexibility allows us to efficiently monitor both production and development environments, ensuring quick detection and response to any anomalies. 

We also have better insight into metrics like latency and memory usage.

Datadog has significantly improved our organization’s monitoring capabilities by centralizing all of our logs and making them easily searchable. This has streamlined our troubleshooting process, allowing for quicker root cause analysis. 

Additionally, its ease of implementation meant that we could cover all of our services comprehensively, ensuring that logs and metrics were thoroughly captured across our entire ecosystem. This has enhanced our ability to maintain system reliability and performance.

The intuitive user interface has been one of the most valuable features for us. Unlike other platforms like Grafana, as an example, where learning how to query either involves a lot of trial and error or memorization almost like learning a new language, Datadog’s UI makes finding logs, metrics, and performance data straightforward and efficient. This ease of use has saved us time and reduced the learning curve for new team members, allowing us to focus more on analysis and troubleshooting rather than on learning the tool itself.

While the UI and search functionality are excellent, further improvement could be made in the querying of logs by offering more advanced templates or suggestions based on common use cases. This would help users discover powerful queries they might not think to create themselves. 

Additionally, enhancing alerting capabilities with more customizable thresholds or automated recommendations could provide better insights, especially when dealing with complex environments like ours with numerous microservices.

I've used the solution for five years.

We have never experienced any downtime.

We previously used Sumo Logic.

Our primary use case is for custom and vendor-supplied web application log aggregation, performance tracing, and alerting. 

We run a mix of AWS EC2, Azure serverless, and colocated VMWare servers to support higher education web applications. We're managing a hybrid multi-cloud solution across hundreds of applications is always a challenge. 

Datadog agents are on each web host, and we have native integrations with GitHub, AWS, and Azure to get all of our instrumentation and error data in one place for easy analysis and monitoring.

Through use of Datadog across all of our apps, we were able to consolidate a number of alerting and error-tracking apps. Datadog ties them all together in cohesive dashboards. Whether the app is vendor supplied or we built it ourselves, the depth of tracing, profiling, and hooking into logs is all obtainable and tunable. Both legacy .NET Framework and Windows Event Viewer and cutting edge .NET Core with streaming logs all work. The breath of coverage for any app type or situation is really incredible. It feels like there's nothing we can't monitor.

When it comes to Datadog, several features have proven particularly valuable. 

The centralized pipeline tracking and error logging provide a comprehensive view of our development and deployment processes, making it much easier to identify and resolve issues quickly. 

Synthetic testing has been a game-changer, allowing us to catch potential problems before they impact real users. 

Real user monitoring gives us invaluable insights into actual user experiences, helping us prioritize improvements where they matter most. And the ability to create custom dashboards has been incredibly useful, allowing us to visualize key metrics and KPIs in a way that makes sense for different teams and stakeholders. 

Together, these features form a powerful toolkit that helps us maintain high performance and reliability across our applications and infrastructure, ultimately leading to better user satisfaction and more efficient operations.

I'd like to see an expansion of the Android and IOS apps to have a simplified CI/CD pipeline history view. 

I like the idea of monitoring on the go, yet it seems the options are still a bit limited out of the box. 

While the documentation is very good considering all the frameworks and technology Datadog covers, there are areas - specifically .NET Profiling and Tracing of IIS hosted apps - that need a lot of focus to pick up on the key details needed. 

In some cases the screenshots don't match the text as updates are made.

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

We have been impressed with the uptime. It offers clean and light resource usage of the agents.

The solution scales well and is customizable. 

Customer support is always helpful to help us tune our committed costs and alerting us when we start spending out of the on demand budget.

We used a mix of a custom error email system, SolarWinds, UptimeRobot, and GitHub actions. We switched to find one platform that could give deep app visibility regardless of whether it is Linux or Windows or Container, cloud or on-prem hosted.

The implementation is generally simple. .NET Profiling of IIS and aligning logs to traces and profiles was a challenge.

We implemented the setup in-house. 

We've witnessed significant time saved by the development team assessing bugs and performance issues.

Set up live trials to asses cost scaling. Small decisions around how monitors are used can have big impacts on cost scaling. 

NewRelic was considered. LogicMonitor was chosen over Datadog for our network and campus server management use cases.

We're excited to dig further into the new offerings around LLM and continue to grow our footprint in Datadog.