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.

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.

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.

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 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.

We use Datadog for all of our observability needs and application performance monitoring. We recently transitioned our logs to Datadog. We also use it for incident management and on-call paging. We use Datadog for almost everything monitoring and observability related.

We use Datadog for figuring out the root cause of incidents. One of the more recent use cases was when we encountered a failure where one of our main microservices kept dying and couldn't give a response. Every request to it was getting a 500. We dug into some of the traces and logs, used the Kubernetes Explorer in Datadog, and found out that the application couldn't reach some metric due to its scaling. We were able to figure out the root cause because of the Kubernetes Event Explorer in Datadog. We pushed out a hotfix which restored the application to working condition.

Our incident response team leverages Datadog to page relevant on-calls for whatever service is down that's owned by that team, so they can get the appropriate SMEs and bring the service back up. That's the most common use case for our incident response. All of our teams appreciate using Datadog on-call for incident response because there are numerous notification settings to configure. The on-call schedules are very flexible with overrides and different paging rules, depending on urgency of the matter at stake.

As an administrator of Datadog, I really appreciate Fleet Automation. I also value the overall APM page for each service, including the default dashboards on the service page because they provide exactly what you need to see in terms of request errors and duration latency. These two are probably my favorite features because the service page gives a perfect look at everything you'd want to see for a service immediately, and then you can scroll down and see more infrastructure specific metrics. If it's a Java app, you can see JVM metrics. Fleet Automation really helps me as an administrator because I can see exactly what's going on with each of my agents.

My SRE team is responsible for upgrading and maintaining the agents, and with Fleet Automation, we've been able to leverage remote agent upgrades, which is fantastic because we no longer need to deploy to our servers individually, saving us considerable time. We can see all the integration errors on Fleet Automation, which is super helpful for our product teams to figure out why certain metrics aren't showing up when enabling certain integrations. On Fleet Automation, we can see each variant of the Datadog configuration we have on each host, which is very useful as we can try to synchronize all of them to the same version and configuration.

The Kubernetes Explorer in Datadog is particularly valuable. It gives us a look at each live pod YAML and we can see specific metrics related to each pod. I appreciate the ability to add custom Kubernetes objects to the Orchestration Explorer. It gives our team an easier time to see pods without having to kubectl because sometimes you have permission errors related to that. Sometimes it's just quicker than using kubectl.

Our teams use Datadog more than they used their old observability tool. They're more production-aware, conscious of how their changes are impacting customers, how the changes they make to their application speed up or slow down their app, and the overall request flow. It's a much more developer-friendly tool than other observability tools.

Datadog needs to introduce more hard limits to cost. If we see a huge log spike, administrators should have more control over what happens to save costs. If a service starts logging extensively, I want the ability to automatically direct that log into the cheapest log bucket. This should be the case with many offerings. If we're seeing too much APM, we need to be aware of it and able to stop it rather than having administrators reach out to specific teams.

Datadog has become significantly slower over the last year. They could improve performance at the risk of slowing down feature work. More resources need to go into Fleet Automation because we face many problems with things such as the Ansible role to install Datadog in non-containerized hosts.

We mainly want to see performance improvements, less time spent looking at costs, the ability to trust that costs will stay reasonable, and an easier way to manage our agents. It is such a powerful tool with much potential on the horizon, but cost control, performance, and agent management need improvement. The main issues are with the administrative side rather than the actual application.

I have been using Datadog for about a year and nine months.

We face a high amount of issues with niche-specific outages that appear to be quite common. AWS metrics being delayed is something that Datadog posts on their status page. We face a relatively high amount of Datadog issues, but they tend to be small and limited in scope.

We have not experienced any scalability issues.

I have interacted with support. Support quality varies significantly. Some support agents are fantastic, but some tickets take months to resolve.

We used Dynatrace previously, and I believe the switch was due to cost, but that decision was outside my scope as I'm not a decision-maker in that situation.

The initial setup in Kubernetes is not particularly difficult.

I cannot definitively say MTTR has improved as I don't have access to those numbers and don't want to make misleading statements. Developers use it significantly more than our old observability tool. We've seen some cost savings, but we have to be significantly more cost-aware with Datadog than with our previous observability tool because there's more fluctuation and variation in the cost.

One pain point is that it has caused us to spend too much time thinking about the bill. Understand that while it is an administrative hassle, it is very rewarding to developers.

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

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.

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.

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 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 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.

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 application monitoring.

Specifically for application monitoring, we monitor our production Laravel instances using APM spans and tracing.

In addition to application monitoring, I also use Datadog to monitor our log management for our applications that are both on-prem and in the cloud, as using the AWS integration.

In my experience, the best features that Datadog offers us include unprecedented visibility and the ability to dive deep on application debugging.

Datadog's visibility and debugging features help me day-to-day; specifically, we had an application that was throwing a bunch of errors causing an issue in our production database. Using Datadog, we were able to immediately isolate the error and plan around it.

Datadog has positively impacted my organization. I think it has given us not only the specific debug and error codes that we're looking for, but it has changed the entire company's mindset in how to extract value from data that's been lying around in our internal systems for years now and given everybody a new perspective on monitoring and debugging.

Since adopting Datadog, I've noticed specific outcomes. We've begun to handle our log management internally in a more efficient manner, so we've actually reduced our disk space as simplified our backup procedures and process chains using Datadog. Now that we have extracted the value from the logs and the traces and the debug logs, we no longer have to rely so much on traditional text-based logs or even digging into the code and the error files themselves.

The only improvement I would to see with Datadog is that the graphical user interface sometimes takes a little bit to load, especially when diving deep on a subject, and just a little bit more caching would help.

The largest pain point we've had with Datadog to this point was onboarding. This was partly our fault because our logs weren't really set up to be used in a modern observability platform Datadog, but I definitely would have liked to have seen more comprehensive onboarding. We had a few appointments, but the more help we get up front, the easier it is for us to get more familiar and do more things with Datadog.

At this time, I do not think there are any other improvements Datadog needs that would make my experience even better.

I have been using Datadog for approximately four months now.

Datadog is very stable.

We have not yet hit the use case to evaluate Datadog's scalability, but based off of everything else we've used with the infrastructure, I don't think there are going to be any issues with it. We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us. In fact, it was talking about costing and billing which we had not anticipated, but we were pleasantly surprised with.

Customer support is excellent; I have opened and closed probably five tickets in the past few days, specifically within the past seven days. Very responsive, and the support techs are knowledgeable and responsive.

I would rate customer support an eight out of ten. The only issues that we had were really needing more educational resources to begin with to truly understand the specifics of log management and APM tracing setup, simply because those are very complicated procedures. Walking through that a couple more times with the support engineer probably would have been helpful. It was not a deal breaker or a significant pain point, but the quicker we get up with Datadog, the happier, the quicker and deeper we get with Datadog, the happier people seem to be at our organization.

Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional. I've been in the industry over 20 years, and part of my roles has always been customer-facing. I find that Datadog's client support is very engaging, comprehensive, and thorough.

For on-prem infrastructure monitoring, we're currently using Nagios, but that's beginning to fade as we rely more on Datadog for our infrastructure monitoring. We had used New Relic for application performance monitoring, but because of the cost associated with that and not seeing the value from it, we stopped using that about two years ago.

We did not purchase Datadog through the AWS Marketplace; we were contacted independently by a Datadog sales agent.

My experience with pricing, setup cost, and licensing has been overall fairly positive. The on-demand/reserved pricing, we were not as cognizant as to how big the on-demand could get, especially when we were getting everything set up, but Datadog proactively took a strong hand in guiding us to getting our costs under control. I'm proud to say that we are within 1% of our projected cost budget, so that was very handy and that's happened in the last month. Very efficient and very effective working with Datadog to control cost.

In terms of time saved, I've noticed that when we're responding to potential errors or during our software deployments, it's saving us minutes at a time that quickly add up to hours, that quickly add up to days in terms of retrieving debug and application error information.

Before choosing Datadog, we evaluated other options including New Relic and SolarWinds.

I would advise others looking into using Datadog to evaluate it against other competing properties and applications in the space, and really dig in. You will find that Datadog does what it's supposed to do very quickly, very efficiently, as does it more cost competitively than some of the other offerings.

Datadog is deployed in my organization in both on-prem and in public cloud scenarios.

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