Datadog Reviews

We utilize Datadog to monitor both some legacy products and a new PaaS solution that we are building out here at Icario which is Micro-Service arch. 

All of our infrastructure is in AWS with very few legacies being rackspace. For the PaaS we mainly just utilize the K8s Orchestrator which implements the APM libraries into services deployed there as well as giving us infra info regarding the cluster. 

For legacies, we mainly just utilize the Agent or the AWS integration. With APM in specific places. We monitor mainly prod in Legacy and the full scope in the PaaS for now.

Datadog has greatly improved the time needed to investigate issues. Putting everything into a single pane of glass. Allowing us to get ahead of infra/app-based issues before they affect customer experience with our products. 

Outside of that, the ease of management, deployment of agents, integrations etc. has greatly helped the teams. There isn't much leg work needed by the devs to manage or deploy Datadog into their stacks. This is with the use of Terraform, pipelines and the orchestrator. All in all, it has been an improvement.

The two most valuable aspects are the Terraform provider for Datadog and the K8s Orchestrator. People don't take that into account when buying into a tooling product like Datadog in this age where scalability, management, and ease of implementation is key. Other tools not having good IaC products or options is a ball drop. Orchestration for the tools agent is good. Not having to use another tool to manage the agents and config files in mutiple places/instances is a huge win!

A big problem with Datadog is the billing. They need to make the billing more user-friendly. I know it like the back of my hand at this point, yet trying to explain it to the C-suite as to why costs went up or are what they are is many times more complicated than it needs to be. I can't even say "why" due to of the lack of metadata tied to billing. For instance, with the AWS Integration Host ingestion, I cant say well this month THESE host got added and thats what caused cost to go up. The billing visibility really needs to be resolved!

I'd rate the solution for more than four years.

Datadog has always been extremely stable, with outages really only ever creating delays, never actual downtime of the service, which is amazing and impressive.

The solution is very scalable if implemented right and not on top of complicated architecture.

Support is excellent. They are always looking for a resolution, and a ticket is never left unresolved unless the feature just can't exist or isn't currently possible.

We did have New Relic, Datadog, Sumo Logic, Pingdom, and some other custom or third-party tooling. We switched because we wanted everything to be in a single pane and because Datadog is a better solution than the competitors.

For us, set-up is a mixed bag as we support legacy apps and architectures as well as a new microservice architecture. That being said, legacy is somewhat complex just due to the nature of how those apps stack and the underlying infra and configuration and setup. Microservice is a breeze and straight-forward for most of the out-of-the-box stuff.

Our Team of SRE Engineers, Platform Engineers and Cloud Engineers implemented the solution.

I can't really speak to ROI; however, from my perspective, we definitely get our money's worth from the product.

Users just just really need to make sure they stay on top of costs and don't let all of the engineers do as they please. Billing with Datadog can get out of hand if you let them. Not everything needs to be monitored.

We didn't really need to evaluate other options.

The primary use case of Datadog within our organization encompasses providing a comprehensive and sophisticated solution that caters to the diverse needs of our internal customers. We have strategically implemented Datadog to serve as a centralized platform for monitoring, analyzing, and optimizing various aspects of our operations. With a robust suite of functionalities, Datadog empowers us to meet the dynamic requirements of over 40 internal customers efficiently.

Through Datadog, we offer a wide array of services to our internal stakeholders, allowing them to access and leverage its capabilities to enhance performance, troubleshoot issues, and make data-driven decisions. The tool's versatility enables different teams within our organization to monitor and track distinct metrics, such as application performance, infrastructure health, and logs, tailored to their specific requirements.

Moreover, Datadog serves as a pivotal component in our organizational ecosystem by streamlining processes, enhancing collaboration, and fostering a culture of data-driven decision-making. By harnessing the power of Datadog, our internal customers can proactively address issues, optimize resources, and ultimately improve operational efficiency across the board.

In essence, the primary use case of Datadog in our organization revolves around empowering our internal customers with a comprehensive and feature-rich solution that enables them to monitor, analyze, and optimize various aspects of our operations seamlessly and effectively. This strategic implementation of Datadog plays a vital role in enhancing our overall performance, fostering transparency, and driving continuous improvement within our organization.

Datadog has significantly contributed to enhancing the overall effectiveness and efficiency of our organization through various key improvements. One of the standout benefits has been the accelerated resolution of issues. By leveraging Datadog's monitoring and alerting capabilities, we have been able to swiftly detect, diagnose, and address issues before they escalate, resulting in minimized downtime and enhanced operational continuity.

Moreover, the implementation of Datadog has had a tangible positive impact on customer satisfaction. With improved visibility into our systems and applications, coupled with proactive monitoring and performance optimization, we have been able to deliver a more reliable and seamless experience to our customers. This has translated into higher customer satisfaction scores and strengthened relationships with our stakeholders.

Another notable improvement brought about by Datadog is the streamlining of our toolset. By identifying and removing multiple unused or redundant features and tools, Datadog has helped optimize our workflows and resources. This decluttering of unnecessary functionalities has not only increased operational efficiency yet also streamlined our processes, allowing us to focus on the tools and features that truly add value to our operations.

In summary, Datadog's impact on our organization has been profound, enhancing our ability to resolve issues rapidly, improving customer satisfaction levels, and streamlining our toolset for increased efficiency and focus. These improvements have led to a more robust and resilient operational environment, enabling us to better meet the needs of our internal and external stakeholders.

Within our organization, we have found the Agents feature in Datadog to be exceptionally valuable due to its rich set of functionalities and capabilities. The Agents play a crucial role in our monitoring and data collection processes, providing a comprehensive and reliable means to gather crucial performance metrics and insights across our systems and applications.

One of the key reasons why the agents feature stands out as particularly valuable is its versatility. The Agents offer a wide range of monitoring and data collection options, allowing us to capture diverse metrics and performance data with precision. This flexibility enables us to tailor our monitoring strategy to meet the specific needs of different teams and use cases within our organization.

Moreover, the agents feature in Datadog enhances the overall observability of our infrastructure and applications. By deploying Agents strategically across our environment, we can gather real-time metrics, logs, and traces, enabling us to monitor the health, performance, and behavior of our systems comprehensively. This deep level of observability empowers us to proactively identify issues, optimize performance, and make informed decisions based on accurate and timely data.

Furthermore, the agents feature in Datadog plays a pivotal role in driving actionable insights and facilitating efficient troubleshooting. With the detailed data collected by the Agents, we can perform in-depth analysis, detect anomalies, and troubleshoot issues quickly and effectively. This proactive approach to monitoring and analysis ultimately enhances our operational efficiency and resilience.

In essence, the agents feature in Datadog stands out as a valuable asset within our organization due to its robust functionality, versatility, and role in providing comprehensive monitoring and observability capabilities. By leveraging the power of the Agents feature, we can effectively monitor, analyze, and optimize our systems and applications to ensure seamless operations and performance excellence.

In assessing areas for potential improvement, one key aspect where Datadog could enhance its service is in the realm of billing CSV reports. Presently, the billing CSV reports provide insights into billing-related information yet are somewhat limited in functionality, typically offering reports with only three columns. Expanding the capabilities of the billing CSV reports to include more detailed and customizable information would greatly benefit users by allowing them to gain a deeper understanding of their usage, costs, and billing trends within Datadog.

Additionally, in considering features for inclusion in the next release of Datadog, the development of more robust and customizable billing CSV reports could be a significant enhancement. By allowing users to tailor their billing reports to specific metrics, timeframes, and parameters of interest, Datadog could provide greater transparency and control over billing data, enabling users to make informed decisions regarding resource allocation, cost optimization, and budget planning.

Moreover, the inclusion of features such as cost forecasting, budget tracking, and customizable alerts related to billing thresholds could further empower users to manage their expenses effectively and proactively monitor and control costs within Datadog. These additions would not only enhance user experience and satisfaction, however, also contribute to a more holistic and actionable approach to financial management within the Datadog platform.

By refining the functionality of billing CSV reports and incorporating advanced features for cost analysis, forecasting, and monitoring, Datadog can elevate its service offering and provide users with enhanced tools for optimizing their usage, expenses, and financial oversight within the platform.

I've used the solution for over three years.

Datadog is easy to scale. However, it's scaled for price, so be sure to measure what you need and not push all logs to the solution, or your price will skyrocket quickly.

We use multiple APM tools to have both price and value correlations relevant to the teams using them.

Request a test account during the POC phase to determine if the tool is the right fit; all providers do that for free.

We did POC with over five products. I can't name them due to the related NDA.

We use Datadog across the enterprise for observability of infrastructure, APM, RUM, SLO management, alert management and monitoring, and other features. We're also planning on using the upcoming cloud cost management features and product analytics.

For infrastructure, we integrate with our Kube systems to show all hosts and their data.

For APM, we use it with all of our API and worker services, as well as cronjobs and other Kube deployments.

We use serverless to monitor our Cloud Functions.

We use RUM for all of our user interfaces, including web and mobile.

It's given us the observability we need to see what's happening in our systems, end to end. We get full stack visibility from APM and RUM, through to logging and infrastructure/host visibility. It's also becoming the basis of our incident management process in conjunction with PagerDuty.

APM is probably the most prominent place where it has helped us. APM gives us detailed data on service performance, including latency and request count. This drives all of the work that we do on SLOs and SLAs.

RUM is also prominent and is becoming the basis of our product team's vision of how our software is actually used.

APM is a fundamental part of our service management, both for viewing problems and improving latency and uptime. The latency views drive our SLOs and help us identify problems.

We also use APM and metrics to view the status of our Pub/Sub topics and queues, especially when dealing with undelivered messages.

RUM has been critical in identifying what our users are actually doing, and we'll be using the new product analytics tools to research and drive new feature development.

All of this feeds into the PagerDuty integration, which we use to drive our incident management process.

Sometimes thesolution changes features so quickly that the UI keeps moving around. The cost is pretty high. Outside of that, we've been relatively happy.

The APM service catalog is evolving fast. That said, it is redundant with our other tools and doesn't allow us to manage software maturity. However, we do link it with our other tools using the APIs, so that's helpful.

Product analytics is relatively new and based on RUM, so it will be interesting to see how it evolves.

Sometimes some of the graphs take a while to load, based on the window of data.

Some stock dashboards don't allow customization. You need to clone them first, but this can lead to an abundance of dashboards. Also, there are some things that stock dashboards do that can't yet be duplicated with custom dashboards, especially around widget organization.

The "top users" widget on the product analytics page only groups by user email, which is unfortunate, since user ID is the field we use to identify our users.

I've used the solution for three and a half years.

The solution is pretty stable.

The solution is very scalable.

Support was excellent during the sales process, with a huge dropoff after we purchased the product. It has only recently (within the past year) they have begun to reach acceptable levels again.

Neutral

We did not have a global solution. Some teams were using New Relic.

The instructions aren't always clear, especially when dealing with multiple products across multiple languages. The tracer works very differently from one language to another.

We handled the setup in-house.

We have built our own set of installation instructions for our teams, to ensure consistent tagging and APM setup.

We did look at Dynatrace.

The service was great during the initial testing phase. However, once we bought the product, the quality of service dropped significantly. However, in the past year or so, it has improved and is now approaching the level we'd expect based on the cost.

The primary use case for this solution is to enhance our monitoring visibility, determine the root cause of incidents, understand end-user behaviour from their point of view (RUM), and understand application performance.

Our technical environment consists of a local dev env where Datadog is not enabled, we have deployed environments that range from UAT testing with our product org to ephemeral stacks that our developers use to test there code not on there computer.  We also have a mobile app where testing is also performed.

Datadog has greatly improved our organization in many ways. Some of those ways include greater visibility into application performance, understanding of end-user behavior, and a single pane of glass view into our entire infrastructure.  

Regarding visibility, our organization previously used New Relic, and when incidents or regressions happened, New Relic's query language was very hard to use. End-user behavior in RUM has improved our ability to know what to focus on. Lastly, the single pane of glass view with maneuvering between products has helped us truly understand root causes after incidents.

APM has been a top feature for us. I can speak for all developers here: they use it more often than other products. Due to a standard in tracing (even though it is customizable), engineers find it easier to walk a trace than to understand what went wrong when looking at logging.  

Another feature that I find valuable, though it isn't the first one that comes to mind, is Watchdog. I have found that has been a good source of understanding anomalies and where maybe we (as an organization) need more monitoring coverage.

I am not 100% sure how this is done or if it can be though I've had a lot of education I've had to do to ramp developers up on the platform. This feels like the nature of just the sheer growth and number of products Datadog now offers.  

When I first started using the Datadog platform, I thought that was a big pro of the company that the ramp-up time was much quicker, not having to learn a query language. I still believe that to be true when comparing the product to someone like New Relic though with the wide range of products Datadog now offers it can be a bit intimidating to developers to know where to go to find what they want.

I have been using the solution at my current company for almost four years, and have used it at my previous company as well.

A while ago, we used New Relic, and we switched due to Datadog being a better product.

We did the implementation in-house.

The value compared to pricing is reasonable, though it can be a bit of a sticker shock to some.

We did not evaluate other options. 

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.

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. 

The technology itself is generally very useful and the interface it great.

There should be a clearer view of the expenses.

I have used the solution for four years.

The solution is stable.

I have not personally interacted with customer service. I am satisfied with tech support.

Neutral

I am using ThousandEyes and Datadog. Datadog supports AI-driven data analysis, with some AI elements to analyze, like data processing tools and so on. AI helps in Datadog primarily for resolving application issues.

It was not difficult to set up for me. There was no problem.

I can confirm there is a return on investment.

I find the setup cost to be too expensive. The setup cost for Datadog is more than $100. I am evaluating the usage of this solution, however, it is too expensive.

I would rate this solution eight out of ten. 

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.