Datadog Reviews

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.

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.

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 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.

Amazon Web Services (AWS)

My main use case for Datadog is monitoring performance of Grainger.com and all the components that are involved within it.

A specific example of how I use Datadog to monitor performance is finding out an issue with an internal bot that we use. We had some issues with some of the commands and we looked into the logs which showed the events from that Slack bot. This was quite useful.

I use Datadog day-to-day to monitor the performance of key services, endpoints, and resources. Currently, we have a migration project for which I created a dashboard to help visualize the performance of key services and endpoints being migrated. At a high level, it helps to capture the performance and health of the services and endpoints.

Datadog has impacted my organization positively as this is our main observability tool when it comes to monitoring services, traces, and all resources within key services. This is our go-to tool and it has helped us to pinpoint issues. One aspect that needs improvement about Datadog is the Watchdog. If there are any escalated conditions or errors happening, it does not indicate which service is causing the issue or which line of code is responsible unless we recreate Watchdog monitors and add the dependency of the GitHub repo to that service.

When pinpointing issues, it helps us focus on where the problem is. Sometimes it's finding a needle in a haystack, especially when it comes to network issues. This has been our key concern lately. During network outages, we don't know exactly which device has the issue, but network observability is an area we're working towards improving. For regular issues within services, we can see the errors, but we must configure the GitHub repo associated with that service to see the key issue. Overall, it helps us to pinpoint issues. While I'm not certain about the exact timing of resolution, it does help overall.

In my opinion, the best features Datadog offers are their APM traces and ability to create dashboards with many customizable metrics, from CPU to thread count to host errors by host and errors by service. Having customized dashboards is really useful, and exploring traces is one of my favorite parts.

We have a list of dashboards primarily showing the key services and APIs related to orders, generating orders, customer direct, and main customer services. Within that list, we have RUM dashboard as well, which shows us the customer impact and the performance of key services which can directly impact customers. During code red or major escalations, I refer to these dashboards for quick analysis of any issues for the services or endpoints.

To make Datadog better, it should be able to pick up error codes automatically. Currently, you have to programmatically configure every single step. In our previous tool, Dynatrace, it could pick up error codes without developers having to explicitly code that into the configuration. Sometimes the APMs are missing the exact error code and error message which is frustrating.

Some minor improvements could include adjusting unit display on dashboards. When request counts go from 900,000 to 1.5 million or 2.2 million for endpoints, the graph keeps all units in thousands rather than converting to millions, which would be more useful and visually appealing.

Datadog Watchdog hasn't been as effective as Dynatrace Davis, which pinpoints key errors or latency within a specific service and drills down to the specific endpoint. This is an area where Datadog could improve.

We fully migrated to Datadog last year.

In my experience, Datadog is stable, though there's typically at least one or two incidents per week. This amounts to approximately four incidents per month that cause disruption. These incidents are related to log service, indexes, and metric capturing issues, which occur in the Datadog platform more frequently compared to other tools we have.

Datadog's scalability for my organization is pretty straightforward. When it comes to installation, we just have to install it on the respective service hosts and configure it. There's a new way of installing these agents, though I haven't worked on it in a while, but the process is straightforward for installing.

The customer support rates eight out of ten. They require all information upfront and there's still back and forth communication happening. Overall, they provide good service.

Positive

We switched from Dynatrace to Datadog after conducting a survey amongst team members from various service teams. We found that developers preferred using Datadog over Dynatrace. The user interface was more intuitive, modern, and more cloud-focused. Since everybody was moving to cloud, we determined that Datadog would be a suitable tool for us.

When comparing the setup between Dynatrace and Datadog, Datadog required more time and effort. Dynatrace was more straightforward - you simply install the agent and it picks up all the traffic with minimal configuration needed for capturing specific things. Overall, the setup for Datadog was more challenging compared to Dynatrace setup.

I would rate Datadog overall as eight out of ten.

My advice for others looking into using Datadog is to be ready to spend a lot of time setting it up and make sure you have a good plan in terms of analyzing the finances because it can easily cost a lot of money to install agents on your service hosts.

My main use case for Datadog involves working on projects related to our sales reps in terms of registering new clients, and I've been using Datadog to pull up instances of them while they're beta testing our product that we're rolling out just to see where their errors are occurring and what their behavior was leading up to that.

I can't think of all of the specific details, but there was a sales rep who was running into a particular error message through their sales registration process, and they weren't giving us a lot of specific screenshots or other error information to help us troubleshoot. I went into Datadog and looked at the timestamp and was able to look at the actual steps they took in our platform during their registration and was able to determine what the cause of that error was. I believe if I remember correctly, it was user error; they were clicking something incorrectly.

One thing I've seen in my main use case for Datadog is an option that our team can add on, and it's the ability to track behavior based on the user ID. I'm not sure at this time if our team has turned that on, but I do think that's a really valuable feature to have, especially with the real-time user management where you can watch the replay. Because we have so many users that are using our platform, the ability to filter those replay videos based on the user ID would be so much more helpful. Especially in terms where we're testing a specific product that we're rolling out, we start with smaller beta tests, so being able to filter those users by the user IDs of those using the beta test would be much more helpful than just looking at every interaction in Datadog as a whole.

The best features Datadog offers are the replay videos, which I really find super helpful as someone who works in QA. So much of testing is looking at the UI, and being able to look back at the actual visual steps that a user is taking is really valuable.

Datadog has impacted our organization positively in a major way because not even just as a QA engineer having access to the real-time replay, but just as a team, all of us being able to access this data and see what parts of our system are causing the most errors or resulting in the most frustration with users. I can't speak for everybody else because I don't know how each other segment of the business is using it, but I can imagine just in terms of how it's been beneficial to me; I can imagine that it's being beneficial to everybody else and they're able to see those areas of the system that are causing more frustration versus less.

I think Datadog can be improved, but it's a question that I'm not totally sure what the answer is. Being that my use case for it is pretty specific, I'm not sure that I have used or even really explored all of the different features that Datadog offers. So I'm not sure that I know where there are gaps in terms of features that should be there or aren't there.

I will go back to just the ability to filter based on user ID as an option that has to be set up by an organization, but I would maybe recommend that being something part of an organization's onboarding to present that as a first step. I think as an organization gets bigger or even if the organization starts using Datadog and is large, it's going to be potentially more difficult to troubleshoot specific scenarios if you're sorting through such a large amount of data.

I have been working in this role for a little over a year now.

As far as I can tell, Datadog has been stable.

I believe we have about 500 or so employees in our organization using our platform, and Datadog seems to be able to handle that load sufficiently, as far as I can tell. So I think scalability is good.

I haven't had an instance where I've reached out to customer support for Datadog, so I do not know.

I do not believe we used a different solution previously for this.

I cannot answer if I have seen a return on investment; I'm not part of the leadership in terms of making that decision. Regarding time saved, in my specific use case as a QA engineer, I would say that Datadog probably didn't save me a ton of time because there are so many replay videos that I had to sort through in order to find the particular sales reps that I'm looking for for our beta test group. That's why I think the ability to filter videos by the user ID would be so much more helpful. I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for. But in regards to your specific question, I don't think that's an answer that I'm totally qualified to answer.

I was not part of the decision-making process before choosing Datadog, so I cannot speak to whether we evaluated other options.

Right now our users are in the middle of the beta test. At the beginning of rolling the test out, I probably used the replay videos more just as the users were getting more familiar with the tool. They were probably running into more errors than they would be at this point now that they're more used to the tool. So it kind of ebbs and flows; at the beginning of a test, I'm probably using it pretty frequently and then as it goes on, probably less often.

It does help resolve issues faster, especially because our sales reps are used to working really quickly in terms of the sales registration, as they're racing through it. They're more likely to accidentally click something or click something incorrectly and not fully pay attention to what they're doing because they're just used to their flow. Being able to go back and watch the replay and see that a person clicked this button when they intended to click another button, or identifying the action that caused an error versus going off of their memory.

I have not noticed any measurable outcomes in terms of reduction in support tickets or faster resolution times since I started using Datadog. For myself, looking at the users in our beta test group, none of those came as a result of any sort of support ticket. It came from messages in Microsoft Teams with all the people in the beta group. We have resulted in fewer messages in relation to the beta test because they are more familiar with the tool. Now that they know there might be differences in terms of what their usual flow is versus how their flow is during the beta test group, they are resulting in fewer messages because they are probably being more careful or they've figured out those inflection points that would result in an error.

My biggest piece of advice for others looking into using Datadog would be to use the filters based on user ID; it will save so much time in terms of troubleshooting specific error interactions or occurrences. I would also suggest having a UI that's more simple for people that are less technical. For example, logging into Datadog, the dashboard is pretty overwhelming in terms of all of the bar charts and options; I think having a more simplified toggle for people that are not looking for all of the options in terms of data, and then having a more technical toggle for people that are looking for more granular data, would be helpful.

I rate Datadog 10 out of 10.

Our main use case for Datadog includes monitoring and logs, custom metrics, as well as utilizing the APM feature and synthetic tests in our day-to-day operations.

A quick specific example of how Datadog helps with our monitoring and logs comes from all our applications sending logs into Datadog for troubleshooting purposes, with alerts built on top of the logs, and for custom metrics, we send our metrics from the applications via Prometheus to Datadog, building alerts on top of those as well, sometimes sending critical alerts directly to PagerDuty.

We generally have monitors and alerts set up for our applications and specifically rely on them to check our critical business units, such as databases; in GCP, we use Cloud SQL, in AWS, we use RDS, and we also monitor Scylla databases and EC2 instances running Kafka services, which we heavily depend upon. Recently, we migrated from US one to US five, which was a significant shift, requiring us to migrate all alerts and monitors to US five and validate their functionality in the new site.

The best feature Datadog offers is its user-intuitive interface, making it very easy to track logs and custom metrics. We also appreciate the APM feature, which has helped reduce our log volumes and custom metric volumes, allowing us to turn off some custom metrics.

We recently learned how tags contribute to custom metrics volume, which led us to exclude certain tags to further reduce that volume, and we implement log indexing and exclusion filters, leaving us with much to explore and optimize in our use of Datadog as our major observability platform.

Regarding metrics showing our improvements, the MTTR has been reduced by about 40% after integrating Datadog with PagerDuty, and we've seen our costs significantly drop in the most recent renewal after three years' contract.

Operationally, we spend about 30-40% less time correlating logs and metrics across services, while potential areas for improvement in Datadog include its integration depth and providing more flexible pricing models for large metric and log volumes.

I would suggest having an external Slack channel for urgent requests, which would enable quicker access to support or a dedicated support team for our needs.

I choose eight because, while we have used Datadog for three years and experienced growth in our business and services, the cost has also increased with the growth in metrics and log volumes, and proactive cost management feedback has not been provided to help manage or budget those rising costs. Thus, I'd like to see more proactive cost management in the future, as the pricing model seems to escalate quickly with increasing metrics ingestion and monitoring across clouds. Datadog is a powerful and reliable observability platform, but there is still room for improvement in cost efficiency and usability at scale.

Regarding pricing, setup costs, and licensing, I find Datadog's pricing model transparent but scaling quickly; the base licensing for host integration is straightforward, but costs can rapidly climb as we add custom metrics and log ingestion, especially in dynamic Kubernetes or multi-cloud environments, with the pricing being moderate to high, and while cost visibility is straightforward, it could become challenging with growing workloads. The upfront setup cost is minimal, mainly involving fine-tuning dashboards, tags, and alerts, making licensing very flexible to enable features as needed.

I have been working in my current field for roughly around 10 years, starting my AWS journey about 10 years ago, mainly focused on infrastructure and observability.

I believe Datadog is stable.

Datadog's scalability is impressive, as it has the necessary integrations, supports agent-based and cloud-native solutions, and accommodates multi-cloud, multi-region features, making overall performance very good.

Customer support has improved recently with online support available through a portal, allowing for quicker access to help.

Neutral

Previously, we used Splunk SignalFx for a couple of years, switching to Datadog because of Datadog's user-intuitive interface, which was lacking in SignalFx at the time.

Datadog has had a significant positive impact on our organization overall, particularly in visibility, reliability, and cost efficiency, allowing us to centralize metrics, logs, and traces across our cloud, moving from reactive to proactive monitoring, with improvements including faster incident detection and resolution, enhanced service reliability, better cost and resource optimization, and shared dashboards providing the engineering and product teams a single source of truth for system health and performance, thus enhancing our overall observability and operational efficiency.

I believe Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization; although we sometimes miss critical alerts, overall, it has improved our team's efficiency by maybe 30% less time spent troubleshooting logs and custom metrics while providing measurable ROI through enhanced system reliability, reduced incident costs, and infrastructure spending optimization.

We only evaluated SignalFx before choosing Datadog, as Datadog offered simpler scaling, better management, broader integrations, and dashboards, allowing for easier monitoring of our multi-cloud setup.

After reducing log and custom metric volumes, we notice a significant reduction in costs without any performance issues on our end, actually seeing a lot of cost reductions.

I strongly recommend using Datadog, but suggest being proactive about resource usage and tracking anomalies monthly.

I find the interview process okay, although it runs longer than I expected, exceeding the anticipated 10 minutes.

My rating for Datadog is 8 out of 10.

I think the most important feature for me in Datadog is the RUM features.

I check the efficiency of the applications that I'm supporting in Datadog and also use it to view the sessions of users.

I have some trouble doing troubleshooting in our app currently, but RUM is my main use case in Datadog.

The personalized dashboards and alerting in Datadog stand out to me, so that way you can gear your use of the product towards what's important to you.

Datadog has allowed us to ensure that we can look at how our beta testers are using our new UIs and seeing where their frustration points are, which has been important to us.

We've been using the heat map feature in Datadog to measure those frustration points.

Some templates for certain roles and things that users care about could be auto-suggested for a dashboard or alerting in Datadog.

We had limitations around RUM and our feature flag provider in Datadog because it's a back-end forward feature flag usage in our Next.js application. We had trouble hooking up our feature flags due to RUM being client-side only. This issue arose because Next.js is a front-end and back-end focused application, and it would be beneficial to send the feature flag resolution from the back-end if needed. Our feature flag provider is GrowthBook, and the way we would have to get those feature flags into Datadog was time-consuming with a lot of boilerplate. We would have to mimic feature flag resolution on the client side, so we decided to forego that.

We have been using Datadog for about two or three months.

Datadog seems stable in my experience without any downtime or reliability issues.

Datadog is scalable and I don't think we'll have problems with scalability in terms of our use case. We might face limitations with logs, but I feel we would not be reaching any of Datadog's limits.

The customer support has been one of the best parts of Datadog.

I would rate the customer support from Datadog a 10 on a scale of 1 to 10.

I would suggest staying in close contact with your customer support representative to get the most out of Datadog.

Positive

We did not have a different solution before Datadog.

Setup with Datadog was pretty easy.

It is too early to tell if we've seen a return on investment so far with Datadog.

I'm not clear on pricing, but it's not a huge concern for us at the moment in terms of RUM. For the other pieces, I know that there may be some pricing that they've been looking at for APM and logs.

I did not evaluate other options before choosing Datadog.

I personally don't use the personalized dashboards and alerting, but I've seen some nice use cases from others on my team. On a scale of 1-10, I rate Datadog an 8.

My main use case for Datadog is the security aspect of it, utilizing the SIEM and the cloud security features. I use it every day monitoring different types of logs and reports that come through, managing most of the alerts that populate from our different applications and software, and it's been a good ride.

Datadog has impacted my organization positively because it tracks all the logs and helps us utilize our features through security. We use Datadog in basically all of our other teams, including engineering, code, APIs, and many other features available, and my peers always say something good about it.

Datadog has helped my organization improve a lot of response time because we get alerts the minute it happens, which is our only means to reduce incident response time. I also appreciate how it provides remediation efforts, allowing us to implement different playbooks while constantly updating with new threats and vulnerabilities, keeping us safe.

One of the best features I appreciate is the Cloud SIEM, and I've used many SIEMs in my experience, but until I got to this company, I never had the chance to really see how Datadog works. With this organization, they were able to show me how easy it was, and Datadog has a really good UI that's easily navigable, helping us teach new team members quickly.

My experience with the Cloud SIEM specifically is that it works 24/7 and stands out due to the easy UI it provides, which helps onboard new members who enjoy it. They are able to pick it up quickly without any prior knowledge.

Datadog helped us out with cloud security features and alerts during situations where we get numerous account lockouts or accounts being jeopardized. Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.

Something I would appreciate seeing from Datadog is more events focused on the networking aspect, which allows me to see what others are using. I enjoy showing up to those events and exploring new features they are releasing as well.

I think Datadog has been performing excellently with no areas that need improvement, as they've been doing great and I want them to keep striving to do better.

I'm fairly new with Datadog, having used it for the past year and a half, almost two years now, and it's been going really well.

Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time. I would appreciate seeing it as an app or mobile app for quicker issue tracking.

Datadog has definitely kept up with our growth.

I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.

Positive

I was not here during the time they onboarded Datadog or looked for different solutions, so I'm not aware of which solution we used before.

I cannot share any metrics regarding return on investment.

Pricing is fairly affordable, and the setup cost has been good, while licensing has been well maintained, making it pretty great.

I'm certain they did their research and looked around at many different options, but I cannot speak on their behalf regarding which they chose or had competition with.

My advice for others looking into using Datadog is to honestly give yourself a week or two to explore all the features and software application, as there are quite a lot of amazing features to learn and utilize, making it not just a software to monitor threats but also a tool to enhance your knowledge in this industry. I rate Datadog 10 out of 10.

My main use case for Datadog is application monitoring and alerting.

A specific example of how I use Datadog for application monitoring and alerting is monitoring for storage filling up.

I also monitor services to ensure that they're running when they should be, and then I schedule downtimes for whenever they shouldn't be.

In my experience, the best features Datadog offers are integrations with ServiceNow and PagerDuty and the large variety of other third-party integrations.

The integrations with ServiceNow and PagerDuty have helped my workflow because whenever there's an issue, we can get notified quickly, and whoever is on call, if it's after hours, can be notified that there's an issue going on.

Dashboards are nice for quick and easy access to important and useful information, and logs are a great place to review information quickly and easily without connecting to the application directly.

Datadog has positively impacted my organization by allowing for a more proactive response to issues whenever they occur.

Being more proactive has helped by reducing downtime and improving our response to resolution. It has helped us limit business impact whenever there are issues that arise.

I believe Datadog could be improved because sometimes it's not the most user-friendly, and when monitors have a new metric or a service that no longer needs to be monitored, it remains in the system. It could be user error, but it would be nice to remove a specific service or part of a monitor from continuing to be monitored if there's no data being collected anymore.

Documentation sometimes is a little misleading or confusing, and there are multiple versions available, so having more up-to-date or clearer documentation regarding which version it applies to would be good.

I have been using Datadog for two, two and a half years.

Datadog is stable.

Datadog's scalability has been pretty scalable from what we've done in our organization.

The customer support is very good; it's easy to get support on pretty much any question that we have, including being able to chat in.

Positive

We previously used LogicMonitor, and I was not involved in the discussions on why we switched.

It's a pretty steep learning curve to start using Datadog; it takes time to really configure everything.

I would say we have seen a return on investment, but I don't have any relevant metrics.

My experience with pricing, setup cost, and licensing is that it was good; I wasn't too involved with it, but as far as I know, it was smooth.

Before choosing Datadog, we did evaluate other options, but I'm not sure what those options were.

On a scale of 1-10, I rate Datadog an 8.

My main use case for Datadog is monitoring and collecting metrics. I use it to collect metrics from Kubernetes pod CPU and memory usage, and also logging, basically all our middleware platforms.

The best features Datadog offers are the ability to collaborate between different metrics such as logging, metrics, and APM, which helps me to pinpoint when I'm troubleshooting issues. The dashboard is very useful; I can use it to get a glance on how the system performs, and alerting is what I'm using right now to send notifications to either email or PagerDuty.

Datadog has positively impacted my organization by shortening our time to resolve incidents because it's a central place for getting all the data that we need for troubleshooting.

I think Datadog can be improved by adding anomaly detection, that would be nice. The user interface is okay, but sometimes cost is the issue because for logging, I had to actually trim down my logs because the cost is too much.

I have been using Datadog for several years.

Datadog is stable.

Datadog's scalability is quite good since it's a SaaS solution, and there are no scalability issues for me. I simply install an agent for whatever new component, server, or host I want to monitor, and then I'm good.

The customer support is hit and miss. Sometimes they respond fairly quickly, but it depends on the person, and it may take a couple of communications for them to actually understand what I need.

Positive

I previously used some open-source solutions from other vendors before Datadog. The switch was made to get a better observability stack.

My experience with pricing, setup cost, and licensing indicates that the pricing is based on usage. When we adopt more, we get more, so everything is based on our desire to improve adoptability for the entire studio, then cost becomes a main issue.

Before choosing Datadog, I evaluated other options, including Dynatrace, which was approximately 10 years ago.

My advice to others looking into using Datadog is that if cost is not a concern, I would recommend them to use it. However, if they are sensitive or concerned about how much money they want to spend, then maybe Datadog is not the solution for them.

I would rate Datadog overall as eight out of ten, though I find it too costly.

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.