Datadog Reviews

My main use case for Datadog is to monitor the logs and capture metrics like CPU metrics, memory, and traces across different services in a cloud-based monitoring system where I initially worked, specifically to debug failing systems and systems which are slow, mainly for monitoring my servers in AWS.

The best features of Datadog for me are the user-friendly real-time dashboard and its ability to easily integrate with AWS, Azure, Kubernetes, Kafka, and provide a centralized log management system, which gives me excellent visibility into the microservice architecture.

Datadog has impacted my organization by providing a centralized monitoring system so that each person can trace what is happening in the VM servers, and it has given us a centralized dashboard view.

Since adopting Datadog, it has reduced the manual effort by around seven to eight hours per week, making the process completely automated.

Datadog has improved the collaboration across the teams and cross-functional teams, making it very fast and allowing us to easily track what is wrong.

If I could change one thing about Datadog, it would be the pricing, as it has extraordinary functionality, but the pricing is somewhat expensive, and as we increase the number of servers and monitoring services, the cost increases. A more predictable and flexible pricing structure would be beneficial, along with additional customization options and reporting features.

I have been familiar with Datadog for more than two years.

I have not yet faced any frustration with Datadog.

Before I landed on Datadog, I used to review the CloudWatch logs in AWS, and we initially had the tool Checkmk for monitoring.

When I first implemented Datadog, it took me around thirty to forty minutes for the basic setup because we had a very large application to monitor metrics. After the configuration, the data actually appeared within three to four minutes.

We did not have any formal training on Datadog. Instead, we referred to Google documentation regarding what Datadog is, how to set it up, and what the use cases are, and based on that, we initially set up Datadog.

When evaluating options before choosing Datadog, I compared it with tools such as New Relic and Grafana Labs with Prometheus. The main reason I chose Datadog is that it is a single platform where I can see metrics, logs, traces, and alerts, and it easily integrates with Kubernetes and other services such as Kafka.

Our workflow is both team-wide and individual, as we check the end-to-end observability and the monitoring of our end-to-end application, infrastructure, and cloud services individually as well as in a team.

When I open Datadog, the first thing I do is see the home dashboard, which will have the active alerts and the system health status, as well as listing out all the monitored resources, including the servers, virtual machines, Kubernetes pods, and nodes. I will also see the CPU usage and memory usage, including the disk utilization.

Datadog is used by the cloud infrastructure monitoring team and the application team within the company, and everyone uses it on the same level as I do.

I have not experienced any features during implementation of Datadog that I am not really using in practice.

As of now, for my use case, I am satisfied with what Datadog offers, and I do not wish for any specific features that it currently lacks.

My advice to someone considering Datadog who has a similar workflow to mine is to read the entire documentation and work on it. I would rate my overall experience with Datadog as an eight out of ten.

My main use case for Datadog is dashboards and monitoring.

We use dashboards and monitoring with Datadog to monitor the performance of our Nexus Artifactory system and make sure the services are running.

The best features Datadog offers are the dashboarding tools as well as the monitoring tools.

What I find most valuable about the dashboarding and monitoring tools in Datadog is the ease of use and simplicity of the interface.

Datadog has positively impacted our organization by allowing us to look at things such as Cloud Spend and make sure our services are running at an optimal performance level.

We have seen specific outcomes such as cost savings by utilizing the cost utilization dashboards to identify areas where we could trim our spend.

To improve Datadog, I suggest they keep doing what they're doing.

Newer features using AI to create monitors and dashboards would be helpful.

I have been using Datadog for six years.

Datadog is stable.

I am not sure about Datadog's scalability.

Customer support with Datadog has been great when we needed it.

I rate the customer support a nine on a scale of 1 to 10.

Positive

We did not previously use a different solution.

In terms of return on investment, there is a lot of time saved from using the platform.

I was not directly involved in the pricing, setup cost, and licensing details.

Before choosing Datadog, we evaluated other options such as Splunk and Grafana.

I rate Datadog an eight out of ten because the expense of using it keeps it from being a nine or ten.

My advice to others looking into using Datadog is to brush up on their API programming skills.

My overall rating for Datadog is eight out of ten.

I intend to use Datadog for application performance monitoring, digital user experiences, and troubleshooting to find the root cause analysis of tickets that will be generated in my managed environment. Digital user experience happens to be the priority for me, as I am evaluating this feature across some competing products.

The best features Datadog offers are digital user experience, troubleshooting, and remediation capabilities, which help identify what is going wrong and where. I focused on the root cause analysis of incidents and tickets, as examining the RCAs makes it easier to find remediations and helps with shifting incidents left. Datadog will positively impact my organization by allowing me to handle ticket resolutions at a much faster pace and bring productivity by reducing the number of support engineers required at the monitoring level. If I integrate Datadog with my managed environment or cloud environment, the RCAs and all the left shift will be automated, and with automation, I will be able to reduce the number of support engineers.

Datadog could be improved with a simpler graphical user interface that can be extended to non-technical users, such as a CXO, if they want to review the dashboard overall for current tickets and the ticketing dashboard. It would be beneficial to have documentation auto-generated while examining remediations or integration with existing systems.

I have been working for more than fifteen years in data center, disaster recovery solutions, and cloud computing, which includes private, public, and hybrid environments.

Datadog seems to be more stable, and I really want to have a complete demo before making a call to decide on this.

I hope that Datadog will be able to extend to digital users, even if they are on a scale of thousands for an organization and connect to corporate bandwidth, and the server should be pretty much scalable on the server side.

I find the customer support impressive from what I have heard about Datadog, and I really want to onboard this solution for my customers.

Positive

As of now, we are using cloud-native monitoring with CloudWatch and Azure Monitor for our multi-cloud environment, and we really want to extend it to greater detail that will cover deliberations at greater depth. We have looked at ManageEngine and SolarWinds before choosing Datadog, but they were not very impressive, as the amount of Datadog functionality is not available in these two platforms.

I am looking to deploy Datadog on AWS and Azure for multi-cloud management support and really want to extend it at the server side and at the end-user side for digital user experience. I will start with AWS and extend it to Azure six months down the line. I plan to purchase Datadog through the AWS Marketplace once I have the demo.

I am looking at metrics that will help me decide whether I need to really deploy Datadog, and the metrics will primarily be centered around reducing the number of employees and cost optimization.

I did not get the complete information regarding the licenses and commercials associated with Datadog, and I would like to have some idea about the license.

I hope to have some literature on how I can leverage my managed support for cloud environments, plus how I can integrate this with my managed support at the end-user devices. Finding the root cause analysis at greater depth, reducing the number of employees to manage or monitor infrastructure incidents, and increasing satisfaction on the application performance monitoring part are the advice I would give to others looking into using Datadog. I give this review a rating of eight.

My main use case for Datadog is for security SIEM, log management, and log archiving.

In my daily work, we send all our logs from different cloud services and SaaS products, including Okta, GCP, AWS, GitHub, as well as virtual machines, containers, and Kubernetes clusters. We send all this data to Datadog, and we have numerous different monitors configured. This allows us to create different security features, such as security monitoring and escalate items to a security team on call to create incident response. Archiving is significant because we can always restore logs from the archive and go back in time to see what happened on that exact day. It is very helpful for us to investigate security incidents and infrastructure incidents as well.

Regarding our main use case, we use the Terraform provider for Datadog, which is probably one of the biggest benefits of using Datadog over any other similar tool because Datadog has great Terraform support. We can create all our security monitoring infrastructure using Terraform. Even if something goes wrong and the Datadog tenant becomes completely compromised or if all our monitors were to get erased for whatever reason, we can always restore all our monitoring setup through Terraform, which provides peace of mind.

The best features Datadog offers are not necessarily about having the best individual features, but rather the sheer quantity of different features they offer. I appreciate how you can reuse a query across different indexes for logs or security monitoring. The syntax remains consistent for everything, so you do not have to learn multiple languages. Similarly, for different types of monitors, you can always reuse the same templating language, which makes things much more efficient.

Datadog positively impacted our organization by making us more cautious about how we manage our logs. Before Datadog, we would ingest substantial amounts of data without considering indexing priorities. We became more strategic about what we index, particularly for security and cloud audit logs. We improved our approach to indexing retention and determining which types of logs are important. Overall, we enhanced our internal log management practices.

After implementing Datadog, we observed specific improvements in outcomes and metrics. We started analyzing our logs more thoroughly than before, identifying different patterns, and determining log importance levels. We began looking for more signals from audit logs and distinguishing between critical and non-critical information. The most significant metric improvement has been reduced incident investigation time.

Datadog can be improved by addressing billing and spend calculation methods, as it would be better if these were more straightforward. Currently, these calculations can be complex. Additionally, while we use Terraform extensively, not everything is available in Terraform. It would be beneficial to have more features supported in Terraform, particularly some security features that have been available for a while but still lack Terraform support.

I have been using Datadog for about four years.

Datadog is very stable.

Datadog's scalability is excellent. We have never encountered any issues.

The customer support is good. I have never had any issues.

I would rate the customer support as nine out of ten.

Positive

We previously used New Relic and switched because it was not very effective.

My experience with pricing, setup cost, and licensing indicates that it was somewhat expensive.

I have seen a return on investment with Datadog, particularly in time saved responding to incidents. Regarding staffing requirements, that metric isn't applicable for our use case since log management and security monitoring inherently require personnel to respond. However, it has definitely improved our efficiency in terms of response time, though this isn't a hard metric but rather based on experience.

I do not remember evaluating other options before choosing Datadog as it was a long time ago.

I would rate Datadog an eight out of ten because while it is expensive, it offers numerous features, though sometimes it attempts to do too much.

My advice to others considering Datadog is to explore other products and calculate potential spending carefully. If Terraform support is important to your organization, then Datadog is an excellent choice. However, keep in mind that costs will increase significantly as you scale, and different features have varying pricing structures.

Overall rating: 8/10

Our main use case for Datadog is collecting metrics, specifically things such as latency metrics and error metrics for our services at Procore.

To give a specific example of how I use Datadog for those metrics in my daily work, I had to create a new service to solve a particular problem, which was an API. I used Datadog to get metrics around successful requests, failure requests, and 400 requests. I then created dashboards that showed those metrics along with some latency metrics from the API, and I also built a monitor that triggers and sends an alert whenever we're over a certain number of the failure metrics.

The single biggest improvement has been breaking down the silos between our teams. Before we adopted it, our developers, operations, and SRE teams all lived in separate tools. Ops had their infrastructure graphs, Devs had their log files, and no one had a complete picture.

Here’s where we’ve seen the most significant impact:

1. We Find and Fix Problems Drastically Faster: The "single pane of glass" is a real thing for us. When an alert fires, our on-call engineer can see the infrastructure metric spike (like CPU), pivot directly to the application traces (APM) running on that host, and see the exact, correlated logs from the services causing the problem—all in one place. We've cut our Mean Time to Resolution (MTTR) significantly because we're no longer "swivel-chairing" between three different tools trying to manually line up timestamps.
2. We Are More Proactive and Less Reactive: Features like Watchdog (its anomaly detection) have been crucial. We've been alerted to a slow-building memory leak and an abnormal spike in error rates on a specific API endpoint before they breached our static thresholds and caused a user-facing outage. It's helped us move from a "firefighting" culture to one where we can catch problems before they escalate.

The best features of Datadog include a great dashboard, a super simple and easy to use Python library, and an easy monitor, which together provide a really great UI experience.

What makes the dashboard and Python library stand out for me is that they save a lot of time, getting right to the point and being super intuitive.

Datadog has positively impacted my organization by allowing us to have a link to a dashboard for most services.

We have dashboards across the company, which can easily be passed around, making it super easy for everyone to understand the metrics they are looking at.

Oh, that's a great question. We actually have a running list of things we'd love to see. Even though we get a ton of value from it, no tool is perfect. Our feedback generally falls into two categories: making the current experience less painful and adding new capabilities we think are the logical next step.

Honestly, our biggest frustrations aren't about a lack of features, but about the management of the platform itself.

1. Cost Predictability and Governance: This is, without a doubt, our number one issue. It's not just that Datadog is expensive—it's that the cost is incredibly complex and hard to predict. Our bill can fluctuate wildly based on custom metrics, log ingestion, and traces from a new service. We've had to dedicate engineering time just to managing our Datadog costs, creating exclusion filters, and sampling aggressively, which feels like we're being punished for using the product more.
   • How to improve it: We need a "cost calculator" inside the platform. Before I enable monitoring on a new cluster or turn on a new integration, I want Datadog to give me a concrete estimate of what it will cost. We also need better built-in tools for attributing costs back to specific teams or services before the bill arrives.
2. The Steep Learning Curve and UI Density: The UI is incredibly powerful, but it's dense. For a senior SRE who lives in the tool all day, it's fine. For a new engineer or a developer who only jumps in during an incident, it's overwhelming. We've seen people "click in circles" trying to find a simple stack trace that's buried three layers deep. Building a "perfect" dashboard is still too much of an art form.

I have been using Datadog for about five years.

Datadog is stable.

I did not previously use a different solution.

I did not deal with any of the pricing, setup cost, or licensing.

I do not know if we purchased Datadog through the AWS Marketplace.

My advice to others looking into using Datadog is to just try using it and see how easy it is to use. I found this interview great. On a scale of 1-10, I rate Datadog a 10.

My main use case for Datadog is logging security signals and monitoring account activity and suspicious behavior within our company.

For monitoring suspicious behavior, we look for alerts with things like unusual sign-in locations, unusual sign-in times, or registering new multi-factor devices in unusual circumstances or locations.

In addition to that, we also look for patterns and frequency of how often MFA is being prompted from individuals.

The best features Datadog offers include the ability to generate reports very quickly and put in extensive filtering to get very specific information.

The report generation and filtering help me in my day-to-day work by assisting in generating reports for higher-ups and turning data into actionable items.

Since using Datadog, it has positively impacted our organization by giving us a one-stop shop for multiple applications and services that we can analyze in one spot.

Having a one-stop shop has made things easier for my team, and we have seen specific outcomes such as saving a lot of time.

Datadog could be improved if the menu system was a little clearer and less cluttered, making it easier to navigate.

Additionally, more documentation is always beneficial to have.

I have been using Datadog for about three years.

Datadog is very stable.

Its scalability is good, and it has kept up as our organization has grown or changed.

I have not had to reach out to customer support, so I cannot comment on that experience.

Negative

I did not previously use a different solution before Datadog.

While I don't have any specifics on money saved, I can say that it has definitely improved our efficiency overall.

My experience with pricing, setup cost, and licensing for Datadog shows that the pricing is very fair and setup has been very simple and easy to do.

Before choosing Datadog, I did not evaluate other options.

My advice to others looking into using Datadog is to read the documentation. I would rate this product a 9 out of 10.

We primarily use the solution for a variety of purposes, including:

• Watching RUM data for frontend site, using LCP and INP metrics to compare across the old and new architecture to inform rollout decisions.
• Watching APM data for backend services, observing how the backend server reacts (CPU util, memory, requests/second) to make sure the backend can handle the load.
• Using Datadog CCM during our free trial period to get visibility over our AWS spend across accounts and resources and looking at recommendations and acting on those.
• Browsing the service catalog to look at the current state of services that are running and what resources it uses. 

This provides a single place to find monitoring data. Prior to DD, we had some metrics living in New Relic, some in Grafana, and some in Circonus, and it was very confusing to navigate across them. Understanding different query languages is challenging. Here, there's a single UI to get used to, and everything is so sharable.

DD has led to teams making more decisions based on data that they observe about their service metrics and RUM metrics. I've seen decisions get made based on what has been observed in DD, and less based on anecdotal data.

I really enjoyed using CCM since it showed cloud cost data easily next to other metrics, and I could correlate the two.

Across CCM and the rest of Datadog, I like how sharable everything is. It's so easy to share dashboards and links with my teammates so we can quickly get up to speed on debugging/solving an issue.

I also have really enjoyed K8s view of pods and pod health. It's very visual, and as a non-K8s platform owner at my company, I can still observe the overall health of the system. Then I can drill in and have learned things about K8s by exploring that part of the product and talking with the team.

We've had some issues where we had Datadog automatically turned on in AWS regions that we weren't using, which incurred a small but steady cost that amounted to tens of thousands of dollars spent over a few weeks. I wish there was a global setting that lets an admin restrict which regions DD is turned on in as a default setup step.

Sometimes, the APM service dashboard link isn't sharable. I click something in the service catalog, and on that service's APM default view, I try to share a link to that with a teammate, and they reach a blank or error screen. 

I wish there was more organization and detail in the suggestions when I use the query editor. I'm never quite sure when the autofill dropdown shows up if I'm seeing some custom tag or some default property, so I have to know exactly what I'm looking for in order to build a chart. It's hard to navigate and explore using the query autofill suggestions without knowing exactly what tag to look for.

It's been a bit hard to understand how data gets sampled or how many data points a particular dashboard value is using. We've had questions over the RUM metrics that we see and we had to ask for help with how values are calculated, bin sizes, etc to get confidence in our data.

I've used the solution for six months.

I've only been aware of a recent outage that affected the latency of data collection for one of our production tests. Outside of that, the solution seems stable.

The solution seems like it can scale very well and beyond our needs.

Technical support has been stellar. We love working with a team that responds fast, in great detail, and with great empathy. I trust what they say.

Positive

We used New Relic, Grafana, and Circonus. Circonus was flakey, always having downtime and we were always on the phone with them. New Relic and grafana, different metrics lived in either and it was hard for consumers of the data to easily find what they need. And we had licensing issues across the 3 so not everybody could easily access all of them.

I didn't do this portion of the product setup.

We use Datadog for log aggregation and management, metrics aggregation, application performance monitoring, infrastructure monitoring (serverless (Lambda functions), containers (EKS), standalone hosts (EC2)), database monitoring (RDS) and alerting based on metric thresholds and anomalies, log events, APM anomalies, forecasted threshold breaches, host behaviors and synthetics tests.

Datadog serves a whole host of purposes for us, with an all-in-one UI and integrations between them built in and handled without any effort required from us.

We use Datadog for nearly all of our monitoring and information analysis from the infrastructure level up through the application stack.

Datadog provides us value in three major ways:

First, Datadog provides best-in-class functionality in many, if not all, of the products to which we subscribe (infrastructure, APM, log management, serverless, synthetics, real user monitoring, DB monitoring). In my experience with other tools that provide similar functionality, Datadog provides the largest feature set with the most flexibility and the best performance.

Second, Datadog allows us to access all of those services in one place. Having to learn and manage only one tool for all of those purposes is a major benefit.

Third, Datadog provides significant connectivity between those services so that we can view, summarize, organize, translate and correlate our data with maximum effect. Not needing to manually integrate them to draw lines between those pieces of information is a huge time savings for us.

I use log management and monitors most often.

Log management is a great way for me to identify changes in behavior across services and environments as we make changes or as user behavior evolves. I can filter out excess or not useful logs, in part or in full, I can look for trends and I can group by multiple facets.

Monitors allow me to rest easy knowing that I'll be alerted to unexpected changes in behavior throughout our environments so that I can be proactive without having to dedicate active cycles to watching all facets of our environments.

In my four years using the product, the only feature request I, or anyone on my team, has had was the ability to view query parameters in query samples. 

Otherwise, improvements are already released faster than we can give them sufficient time and attention, so I'm very happy with the product and don't have any specific requests at this time.

The cost does add up quickly, so it can be some effort to justify the necessary outlay to those paying the bills. That said, Datadog provides sufficient benefits to warrant our continued use.

I've used the solution for four years.

In four years of daily use I haven't noticed any periods of downtime.

It's amazing to me how performant Datadog is given how much data we pass to it.

We've opened probably six or eight support tickets in four years of use. In some cases, the problem or question was complex and took some time to resolve. That said, customer support was always able to debug the issue and find a solution for us, so my experience has been very positive.

Positive

I've used New Relic, Honeycomb, Grafana, Splunk, Prometheus, Graylog and others.

Given the breadth of configuration options, the initial setup was fairly involved for us. We also use several services and deploy the agent in various ways because we're using traditional servers, serverless, and K8s.

We implemented the solution in-house.

The solution can be pricey if you're using many services and/or shipping lots of data, but in my opinion, the value is greater than the cost, so I would suggest doing an evaluation before making a decision.

We use Datadog for monitoring and observing all of our systems, which range in complexity from lightweight, user-facing serverless lambda functions with millions of daily calls to huge, monolithic internal applications that are essential to our core operations. The value we derive from Datadog stems from its ability to handle and parse a massive volume of incoming data from many different sources and tie it together into a single, informative view of reliability and performance across our architecture.

Adopting Datadog has been fantastic for our observability strategy. Where previously we were grepping through gigabytes of plaintext logs, now we're able to quickly sort, filter, and search millions of log entries with ease. When an issue arises, Datadog makes it easy to track down the malfunctioning service, diagnose the problem, and push a fix.

Consequently, our team efficiency has skyrocketed. No longer does it take hours to find the root cause of an issue across multiple services. Shortened debugging time, in turn, leads to more time for impactful, user-facing work.

Our services have many moving parts, all of which need to talk to each other. The Service Map makes visualizing this complex architecture - and locating problems - an absolute breeze. When I reflect on the ways we used to track down issues, I can't imagine how we ever managed before Datadog.

Additionally, our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a
multitude of programming languages. We haven't found a case yet where we
needed to roll out our own solution for communicating with our instance.

A tool as powerful as Datadog is, understandably, going to have a bit of a learning curve, especially for new team members who are unfamiliar with the bevy of features it offers. Bringing new team members up to speed on its abilities can be challenging and sometimes requires too much hand-holding. The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data. This would give them the confidence to navigate the tool and make the most of all it offers.

The company was using it before I arrived; I'm unsure of how long before.

We have multiple nodes integrated into our Azure infrastructure and our AKS clusters. These nodes are integrated with traces (as APM hosts).

We also have infrastructure Hosts integrated to see the metrics and the resources of each hosts mainly for Azure VMs and AKS nodes. Additionally, we also have hosts from our VMs in Azure which act as Activemq and we integrate them as messaging queues to show up in the Activemq dashboard.

We have recently added Activemq as containers in the AKS and we are also integrating those as messaging queues to show up in the Activemq dashboard integration 

Logs are great. Having all services with different teams sending the logs to Datadog and having all logs in the same place is very helpful for us to understand what is going on in our app; filtering of the logs a huge help and adding special custom filters is easy, filters are fast. Documentation is better than average, with little room for improvement.

Dashboards are simple, and monitors are very easy to configure and get notified if something is wrong.

With the aggregated logs, we can now see logs from other systems and identify problems in other areas in which we had no visibility before.

Dashboards are the most valuable. We need the observability. We have given the dashboards to a dedicated team to monitor them off working hours and they are reporting whatever they see going red. This helps us since people without any knowledge can understand when there is a problem and when to react and when to inform others by simply looking if the monitor (showing the dashboards) turns up red. 

Traces being connected to each other and seeing that each service is connected through one API call is very helpful for us to understand how the system works.

The monitors need improvement. We need easier root cause analysis when a monitor hits red. When we get the email, it's hard to identify why the trigger has gone red and which pod exactly is to blame in a scenario where the pod is restarting, for example.

Prices are a very difficult thing in Datadog. We have to be very mindful of any changes we make in Datadog, and we are a bit afraid of using new features since, if we change something, we might get charged a lot. For example, if we add a network feature to our nodes, we might get charged a lot simply by changing one flag, even though we are only going to use one small feature for those network nodes. However, due to the fact that we have more than 50 nodes, all of the nodes will be charged for the feature of "Network hosts".

This leads us to not fully utilize the capabilities of Datadog, and it's a shame. Maybe we can have a grace period to test features like a trial and then have datadog stop that for us to avoid paying more by mistake.

I've used the solution for five years.

The solution is stable enough. We found it to be down only a few times, and it's reasonable.

The solution offers very good scalability. When we added more logs and more hosts, we did not notice any degradation in the service.

Support is very good. They answer all of our questions, and with a few emails, we get what we need

Positive

We previously used Elastic. We had to set up everything and maintain it ourselves.

Datadog has very good support and it is not so complicated to set up.

We set up the solution in-house. We integrated everything on our own.

We found the product to be very valuable.

I'd advise others to start small and then integrate more stuff. Be mindful when using Datadog.

We evaluated Splunk and ELK.

Be careful of the costs. Set up only the important things.