Datadog Reviews

My main use case for Datadog is to analyze data in regards to instant funding.

A specific example of how I use Datadog for instant funding data is understanding how long it takes for an application to be processed, approved, and then instantly funded, how many applications there are, and if there's any holdups on the applications as well.

We are identifying the reason behind a hold-up for instant funding and possibly why some applications do not get instantly funded. Datadog helps us identify those weak areas.

Datadog has significantly improved our organization’s visibility into system performance and application health. The real-time dashboards and alerting capabilities have helped our teams detect issues faster, reduce downtime, and improve response times. It’s also made collaboration between engineering and operations smoother by providing a shared view of metrics and logs in one place.

In my experience, the best features Datadog offers include the layout of the reporting, which is user-friendly, and for those who are not familiar with data, this helps the visual impact.

The layout and reporting are user-friendly because there is a dashboard that I use the most.

Datadog has positively impacted my organization by allowing cross-functional teams who do not necessarily work directly with data to understand, simplify, and take in the data points.

Those cross-functional teams are using the data now by reviewing these reports and they are able to identify weak spots as well to improve cross-functionally the application process.

Areas for improvement:
Datadog could improve in dashboard usability and data correlation across products. While it’s powerful, the interface can feel cluttered and overwhelming for new users. Streamlining navigation and offering simpler default dashboards would help teams ramp up faster.

Additional features for next release:
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance. Improved cost management insights or forecasting tools would also help teams monitor usage and control expenses more effectively.

I have been using Datadog for roughly six months.

Datadog is stable.

Regarding Datadog's scalability, we have not scaled yet, but we are in the process of continuously scaling up, so we will find out in the near future.

The customer support of Datadog is amazing.

I would rate the customer support a definite 10, as friendliness is top tier.

I previously used a different solution, and we switched due to inconsistencies. The previous solution was also inaccurate and unreliable.

I have seen a return on investment in terms of time saved. I don't have metrics on hand for that answer, but there has been time saved due to the Datadog output.

My experience with pricing, setup cost, and licensing has been that all were fair.

Before choosing Datadog, I evaluated other options, but I don't want to identify other ones.

I don't have anything else to mention about the features, including integrations, alerts, or ease of setup.

I am unsure what advice I would give to others looking into using Datadog.

I found this interview impressive for AI, and I do not think there is anything I would change for the future.

On a scale of one to ten, I rate Datadog a 10.

My main use case for Datadog is monitoring our servers.

A specific example of how I'm using Datadog to monitor my server is that we are maintaining request and latency and looking for errors.

I really enjoy the user interface of Datadog, and it makes it easy to find what I need. In my opinion, the best features Datadog offers are the customizable dashboards and the Watchdog.

The customizable dashboards and Watchdog help me in my daily work because they're easy to find and easy to look at to get the information I need. Datadog has positively impacted my organization by making finding and resolving issues a lot easier and efficient.

I think Datadog can be improved by continually finding errors and making things easy to see and customize.

I have been using Datadog for one month.

Datadog is stable.

Datadog's scalability has been easy to put on each server that we want to monitor.

I have not had to contact customer support yet, but I've heard they are great.

Neutral

We previously used our own custom solution, but Datadog is a lot easier.

I'm not sure if I've seen a return on investment.

My experience with pricing, setup cost, and licensing is that it was easy to find and easy to purchase and easy to estimate.

I did not make the decision to evaluate other options before choosing Datadog.

I would rate Datadog a nine out of ten.

I give it this rating because I think just catching some of the data delays and latency live could be a little bit better, but overall, I think it's been great.

I would recommend Datadog and say that it's easy to customize and find what you're looking for.

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 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.

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

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.

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.

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 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.