Datadog Reviews

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.

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.

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 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 for APM, AWS, Lambda, logging, and infrastructure. We have many different things all over AWS, and having one place to look is great.

We have all sorts of different AWS things out there that are in C# and Node. Having a single place to log and APM into is very important to us.

Keeping track of the cloud infrastructure is also important. We have Lambda, containers, EC2, etc.

Having a super simple interface to filter the searching for APM and logging is great. It is super easy to show people how to use. This is super important to us.

Finding issues quickly is super important. Being able to create dashboards and alert on issues.

Having the ability to create dashboards has really taught us how to utilize the searching part of the system. We are able to share them, and build upon them so easily. Many iterations later people are putting some solid information out there.

Alerting is also important to us. We have set up many alerts that help us spot issues in the platform before they become bigger issues. This has enabled my teams to use incidents and address the issues so they are no longer problems.

Alerting on running systems is very helpful. Finding issues is quick. We have one place for logging, searching through. Being able to save these and reference them in the future and build upon them.

The logging in general is one of my favorite features. The search is so straight forward and easy to use. Just being able to click on a field and add it to search has taught me so much about the interface, It might not be as useful without a shortcut like that to teach me the system. We have Cloudflare logs in there, and I have no idea sometimes how to filter on such a buried piece of JSON. That is where the interface helps me by clicking on the add to search I get what I need.

The "Pager Duty" replacement is something we are very interested in. We only really use pager duty to call the team when things are down.

I love to have some DD guru come in and do a department training directly at our setup. We would love to have someone come in and show us the things we could do better within our current setup.

Also saving a bit of cash would also help if there are things we are doing that are costing us. It's a big enough tool that it is tough to have someone dedicated to manage. 

I've used the solution for a bit over a year at this point.

The stability seems good here too.

Scalability seems good to me. I have no complaints

I get answers from our contact, and one team member did reach out. It went well.

Positive

We used Loggly. 

We switched because we wanted an all-in-one tool

Some parts of our setup were tough. Some Windows container setups cost us a lot of time.

The AWS infrastructure was tough to fully turn on due to the large cost of everything being run.

We handled the setup ourselves in-house.

This cost us more overall. ROI is hard to sell. That said, I can find issues way faster and see what is going on in my entire platform. I pay back the cost every month with productivity. 

It is going to cost you more than you think to keep everything running. We saw value in the one-for-all solution, however, it came at a premium to what we were paying. 

We did evaluate Dynatrace.

Our company deploys the solution for our customers as an observability tool to define SLOs and SLIs along with logs and metrics. 

The solution includes incident, post-mortem, and root cause analysis that provides a level of truth for incidents and issues with applications. 

We have SREs and teams in operations, management, and applications who all access to the solution and ensure proper integrations. 

Our company is adopting SRE practices and the solution helps us to align the practices with our site reliability. We get more insights about issues at the outset which helps us to make better decisions such as continuing with agility or stopping to fix issues. 

We are at the beginning stages of using the solution but are defining it as our company standard for use by all teams. 

The solution allows flexibility and heightened observability for presenting data, creating indicators, and setting service-level objectives. There are interesting options for monitors and features that offer flexible ways to analyze data.

The solution should provide alerts for cloud outages that would allow us to report potential service impacts directly to applications or on the dashboard. Alerts are important because there is a need to determine the impact on your SLAs, SLOs, and SLIs to decide whether to move toward disaster recovery or another environment. 

I would like the ability to share dashboard screenshots via email rather than having to direct others to the dashboard because it sometimes requires permissions. 

I have been using the solution for one year. 

Our SRE teams report that the solution is very solid, stable, and reliable. 

We are using the SaaS model so do not manage scalability because the product takes care of our scaling needs with no issues. 

I don't have direct access with support but our SRE teams work with them and are satisfied. 

Our company used other products in the past but wanted to move to the cloud. We found that the solution was a very good fit for us. 

The initial setup is not that easy because there are many choices for configuration, workloads, servers, and containers. 

We utilized technical support to help us understand integration and prepare patterns for other applications. 

We created small configurations and then utilized technical support to configure an application selected from our portfolio. 

We utilize a team approach for implementations that sometimes includes SREs. 

The solution is fairly priced but history and log storage can get costly depending on your needs. 

I rate the cost a four out of ten. 

The solution is appropriate for companies that are moving to the cloud and want a very solid tool for observability, logging, and everything related to SRE practices. 

I rate the solution a nine 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.

We monitor our multiple platforms using Datadog and post alerts to Slack to notify us of server and end-user issues. We also monitor user sessions to help troubleshoot an issue being reported. 

We monitor 3.5 platforms on our Datadog instance, and the team always monitors the trends and Dashboards we set up. We have two instances to span the 3.5 platforms and are currently looking to implement more platform monitoring over time. The user session monitoring is consistent for one of these platforms. 

Datadog has improved our response time and cost-efficiency in bug reporting and server maintenance. We're able to track our servers more fluidly, allowing us to expand our outreach and decrease response time. 

There are many different ways that Datadog is used, and we monitor three and a half platforms on the Datadog environment at this time. By monitoring all of these platforms in one easy-to-use instance, we're able to track the platform with the issue, the issue itself, and its impact on the end user. 

The server monitoring, service monitoring, and user session monitoring are extremely helpful, as they allow us to be alerted ahead of time of issues that users might experience. More often than not, an issue is not only able to be identified, but solved and released before an end user notices an issue. 

We are currently using this as an investigative tool to notice trends, identify issues, and locate areas of our program that we can improve upon that haven't been identified as pain points yet. This is another effective use case. 

I would like to see a longer retention time of user sessions, even if by 24 to 48 hours, or even just having the option to be configurable. By doing this, we're enabled to store user sessions that have remained invisible for a long time, and identify issues that people are working around. 

I would also like to see an improvement in the server's data extraction times, as sometimes it can take up to ten minutes to download a report for a critical issue that is costing us money. Regardless, I am very happy with Datadog and love the uses we have for the program so far.  

I've used the solution for more than four years.

We did not previously use a different solution.