Datadog Reviews

My main use case for Datadog is performance monitoring, SLOs, and SLIs.

For performance monitoring, SLOs, and SLIs, we create objectives and indicators around user feedback and stakeholder feedback. We have weekly meetings to create backlog items to work on if things have elapsed and gone into the red based on our SLO definitions.

The best features Datadog offers are the analytics that are all associated with each other. RUM data associated with APM, trace data, and all of that, including information around inferred requests, has been super useful. Machine health data gives a complete picture of performance, which has been extremely useful for troubleshooting difficult problems.

Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time. I know that the quality of Datadog metrics gathered is enough to where I can rule things in and out. This basically goes for any web app; when asking why a web app is slow, first you look at the code. If the code looks good, then you look at the hardware or the database. Being able to rule all of those out with one tool with one set of requests is useful.

Datadog has positively impacted my organization by allowing us to gather complete data instead of looking all over the place at incomplete data and actually make pointed determinations for fixing issues. It has helped increase efficiency and saved time.

I don't know how Datadog can be improved as they are doing a pretty good job.

I have been using Datadog for three years.

Datadog is stable.

Datadog's scalability is good.

The customer support is good.

My experience with pricing, setup cost, and licensing is that it is really expensive.

I have not seen a return on investment.

My experience with pricing, setup cost, and licensing is that it is really expensive.

My advice to others looking into using Datadog is that it is good and they should use it.

I don't know if my company has a business relationship with this vendor other than being a customer.

On a scale of 1-10, I rate Datadog a 9.

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.

My team and I primarily rely on Datadog for logs to our application to identify issues in our cloud-based solution, so we can take the requests and information that's being presented as errors from our customers and use it to identify what the errors are within our back-end systems, allowing us to submit code fixes or configuration changes.

I had an error when I was trying to submit an API request this morning that just said unspecified error in the web interface. I took the request ID and filtered a facet of our logs to include that request ID, and it gave me the specific examples, allowing me to look at the code stack that we had logged to identify what specifically it was failing to convert in order to upload that data.

My team doesn't utilize Datadog logs very often, but we do have quite a few collections of dashboards and widgets that tell us the health of the various API requests that come through our application to identify any known issues with some of our product integrations. It's useful information, but it's not necessarily stuff that our team monitors directly as we're more of a reactionary team.

The best features Datadog offers, in my experience, are the ability to filter down by facets very quickly to identify the problems we're experiencing with our individual customers using our cloud application. I really enjoy the trace option so that I can see all of the various components and how they communicate with each other to see where the failures are occurring.

The trace option helps us spot issues by giving access to see if the problem is occurring within our Java components or if it's a result of the SQL queries, allowing us to look at the SQL queries themselves to identify what information it's trying to pull. We can also look at other integrations, whether that's serverless Lambda functions or different components from our outreach.

Datadog has impacted our organization positively because the general feeling is that it's superior to the ELK stack that we used to use, being significantly faster in searching and filtering the information down, as well as providing links to our search criteria that our development teams and cloud operations teams can use to look at the same problems without having to set up their own search and filter criteria.

For the most part, the issues that we come across with Datadog are related to training for our organization. Our development and operations teams have done a really good job of getting our software components into Datadog, allowing us to identify them. However, we do have reduced logging in our Datadog environment due to the amount of information that's going through.

The hardest thing we experience is just training people on what to search for when identifying a problem in Datadog, and having some additional training that might be easily accessible would probably be a benefit.

At this point, I do not know what I don't know, so while there may be options for improvements, Datadog works very well for the things that we currently use it for. Additionally, the extra training that would be more easily accessible would be extremely helpful, perhaps something within the user interface itself that could guide us on useful information or how to tie different components or build a good dashboard.

I have worked for Calabrio for 13 years.

Datadog is very stable.

Datadog's scalability is strong; we've continued to significantly grow our software, and there are processes in place to ensure that as new servers, realms, and environments are introduced, we're able to include them all in Datadog without noticing any performance issues. The reporting and search functionality remain just as good as when we had a much smaller implementation.

Previously, we used the ELK stack—Elasticsearch, Logstash, and Kibana—to capture data. Our cloud operations team set that up because they were familiar with it from previous experiences. We stopped using it because as our environment continued to grow, the response times and the amount of data being kept reached a point where we couldn't effectively utilize it, and it lacked the capability to help us proactively identify issues.

A general impression is that Datadog saves time because the ability to search, even over the vast amount of AWS realms and time spans that we have, is significantly faster compared to other solutions that I've used that have served similar purposes.

I would advise others looking into using Datadog to identify various components within their organization that could benefit from pulling that information in and how to effectively parse and process all of it before getting involved in a task, so they know what to look for. Specifically, when searching for data, if a metric can be pulled out into an individual facet and used, the amount of filtering that can be done is significantly improved compared to a general text search.

I would love to figure out how to use Datadog more effectively in the organization work that I do, but that is a discussion I need to have with our operations and research and development teams to determine if it can benefit the customer or the specific implementation software that I work with.

On a scale of one to ten, I rate Datadog a ten 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.