The solution is basically used for servers and applications.
Datadog OverviewUNIXBusinessApplicationPrice:
Datadog Buyer's Guide
Download the Datadog Buyer's Guide including reviews and more. Updated: June 2023
What is Datadog?
Datadog is a cloud monitoring solution that is designed to assist administrators, IT teams, and other members of an organization who are charged with keeping a close eye on their networks. Administrators can use Datadog to set real-time alerts and schedule automated report generation. They can deal with issues as they arise and keep up to date with the overall health of their network while still being able to focus on other tasks. Users can also track the historical performance of their networks and ensure that they operate at the highest possible level.
Datadog Benefits
Some of the ways that organizations can benefit by deploying Datadog include:
- Gain an integrated view of the services and programs that IT teams are employing across their networks. Users can view and monitor all of the disparate programs that they have running across their networks with this one solution. They can track these programs across the entirety of the data’s life cycle.
- Analyze and utilize massive amounts of data in real time. Datadog’s dashboards gather data in real time. Administrators can utilize their network’s data the minute that it becomes relevant to them. Decisions can be made based on the most current information available.
- Keep your cloud network secured against digital threats. Datadog enables users to create alerts that will notify the minute that threats arise. IT teams and administrators can rapidly address any issue that comes up and prevent any existing problem from growing worse.
- Easily get it up and running. Users can set up Datadog, configure it, and employ API integrations to connect it to external solutions with ease.
Datadog Features
- Customizable and prefabricated monitoring dashboards. Administrators are supplied with two different types of dashboards that they can choose from when they are setting up Datadog. They can customize the dashboards to fit any specialized monitoring need. Additionally, users can choose to use prefabricated dashboards that come with the solution.
- Disaster recovery feature. Datadog has a built-in feature that enables organizations to continue functioning if some disaster strikes their network. If the network suffers damage, Datadog can restore lost data and infrastructure. Should a digital threat do damage to the network, Datadog ensures that the damage is not irreparable.
- Vulnerability scanning tool. Users can keep ahead of threats to their networks by employing Datadog’s vulnerability scanning feature. This tool scans the entirety of a user’s network and warns them if a vulnerability is detected. Users can then move to patch these holes in their security before the threat to their network can escalate.
Reviews from Real Users
Datadog is a solution that stands out when compared to many of its competitors. It can offer organizations many advantages. Two major advantages are the dashboards that users can create and the monitoring capability that it gives system administrators.
A senior manager in charge of site reliability engineering at Extra Space Storage writes, “The dashboards we created are core indicators of the health of our system, and it is one of the most reliable sources we have turned to, especially as we have seen APM metrics impacted several times lately. We can usually rely on logs to tell us what the apps are doing.”
Housecall Pro’s senior director of DevOps writes, “We value the monitoring capability since it allows us to be pushed alerts, rather than having to observe graphs continually.”
Datadog Customers
Adobe, Samsung, facebook, HP Cloud Services, Electronic Arts, salesforce, Stanford University, CiTRIX, Chef, zendesk, Hearst Magazines, Spotify, mercardo libre, Slashdot, Ziff Davis, PBS, MLS, The Motley Fool, Politico, Barneby's
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Easy to set up and good UI but needs better customization capabilities
Pros and Cons
- "The many dozens of integrations that the solution brings out of the box are excellent."
- "Deploying the agents is still very manual."
What is our primary use case?
What is most valuable?
The UI, basically, is the most valuable aspect of the solution. I really like the look and feel of the solution. It's not very distinctive now since other players have caught up, however, they were the first in the market to present such an effective UI.
The many dozens of integrations that the solution brings out of the box are excellent.
It's easy to set up.
What needs improvement?
Deploying the agents is still very manual.
Network monitoring could be better or rolled into this solution so that you do not have to buy a different product.
Customization of the tool itself should be taken into account. At the moment, although what they provide out of the box is good, they don't offer many customization possibilities. I know it's difficult, however, it's something that they would need to look at. When the customer gets some customization, they want customized requirements. We cannot do it.
For how long have I used the solution?
I've been dealing with the solution for five years.
Buyer's Guide
Datadog
June 2023

Learn what your peers think about Datadog. Get advice and tips from experienced pros sharing their opinions. Updated: June 2023.
708,830 professionals have used our research since 2012.
What do I think about the stability of the solution?
It's quite stable. I have never had an issue in regard to reliability, so it's very stable.
What do I think about the scalability of the solution?
It's very scalable. I have not reached the limits at any time, never in the solution. I've never seen any performance degradation in large environments. I would say it's very scalable.
Each client has its own instance. We do not share instances with multiple customers. There's usually between 20 and 30, depending on the customer.
How are customer service and support?
I never use technical support, to be honest.
How was the initial setup?
The initial setup for the solution itself is quite straightforward. You just set it up and that's it. However, when it comes to, for instance, deploying the agents to the servers, or at least the target machines, it's still a manual task. They still do not have centralized management of the FD agents, which basically delays the deployment of the solution. It's very manual still.
How long it takes to deploy is difficult to pin down. It will vary based on the environment size. Obviously, if it's ten servers, it will basically take half an hour or one hour. If it's 5,000, obviously, besides the number of notes, other considerations will need to be taken into account. If t's a large environment, it will take much longer. We would need to basically develop a solution, or an effective process to deploy the agent and configure them in a standardized manner. This is something that the tool itself or the tool provider does not offer out of the box. You need to build it. That's a drawback.
How many people you need for the deployment and maintenance processes depends on the environment's size and geographical area. On average, I would usually require for every 500 notes, one resource for implementation. Then for overall support, I usually put one resource per 1500.
What was our ROI?
Before, the ROI was much higher as you would not have to compete with any kind of tool since they were very good in the space. However, with time, other companies have picked up the slack. Now, you have other tools which provide a higher ROI. I cannot give a specific ROI percentage since I don't use it for personal use with deployment. We deploy it on behalf of customers. Obviously, depending on the deal, depending on the size, and the ROI will vary. If people are looking for a global monitoring solution in the same tool as Datadog network monitoring, they are always hindered as Datadog does not provide an adequate solution for it. That kind of decreases the ROI since you still need to get another tool to do the network monitoring.
What's my experience with pricing, setup cost, and licensing?
The licensing is a bit complicated. When you pay for it on a note basis, that's perfectly fine. However, when you put log analytics on top of it, it's based on traffic. This is actually an issue. It gets complicated.
What other advice do I have?
I'm providing Datadog. I'm a retailer.
I would recommend the solution.
I would suggest if their environment is in the cloud, companies have their environments in the public cloud, such as GCP, Azure, or AWS. Datadog is a very good candidate to provide an overview of the monitoring. If you want to consider a hybrid solution where systems and servers and applications also provide a good solution and have a lot of APM capabilities, the only drawback will be network monitoring. When you grab a tool that you want to basically monitor the entire environment at a single point of contact, with Datadog, it's possible, however, there's not an effective tool to do network monitoring.
I'd rate the solution seven out of ten.
Which deployment model are you using for this solution?
Public Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer:

Customizable and helpful for isolating and filtering environments
Pros and Cons
- "We have way more observability than what we had before - on the application and the overall system."
- "Auto instrumentation on tracing has not been very easy to find in the documentation."
What is our primary use case?
We use Datadog for observability and system/application health, mainly for product support, triaging, debugging, and incident responses.
We use a lot of the logging and the Datadog agent to collect logs, metrics, and traces from our GKE workloads. We use APM and continuous profiling for latency and performance measurement. We use RUM to observe frontend user events, such as tracing on request and what actions they take before errors occur. We also use error tracking and source maps to debug production failures.
We are still relatively new to the product, and we are planning to use more of the notebook functionality and power packs to record run books and break knowledge silos. We also need to utilize dashboards and continuous profiling more for performance measurement and integrate Datadog alerts for incident response.
How has it helped my organization?
We have way more observability than what we had before - on the application and the overall system. That includes the GKE cluster, nodes, and pods. It's helped with our cloud-run instances, databases, and data storage.
We also started observability in the CI pipeline to measure our CI performance, as it was a pain point for us. We are aiming to do incremental deployments and releases, and the bottleneck so far has been our CI performance. The visibility on which actions or functions take the most time allows us to pinpoint and focus on improving configurations on these.
What is most valuable?
We use structure logging a lot to triage production issues. The querying, attributes and tags manipulation, and customization have been very helpful in isolating and filtering environments. The integration with Winston logger has also been a breeze.
First and foremost, was that structured logging, tags, and attributes have not only allowed us to narrow down to a problem quickly in production, they have also let us create dashboards from these logs to understand more user behaviors, such as how many users stop and leave our application before an upload has completed. That helps us understand how important processing time is to a user.
We also intend to use distributed tracing more to understand where the error has occurred in a particular request.
What needs improvement?
Definitely, documentation could use improvement. As I navigated and try to find instrumentation and implementation details, I discovered inconsistency among SDKs based on languages.
There are also places where highlighting can be improved. I once created an issue on GitHub, and it was resolved right away by an engineer. He pointed out that it was actually in the documentation. I looked again and found it was not very obvious. We were stuck on the problem for days.
Auto instrumentation on tracing has not been very easy to find in the documentation. We ended up using OpenTelemetry, yet the conversion between tracing contexts has been difficult.
For how long have I used the solution?
We've used the solution between six months and a year.
How are customer service and support?
Customer service and support are generally very fast. I did experience one ticket, which involved changing the log index retention period, not being responded to. Any support tickets related to technical issues were resolved pretty fast.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We used to use GCP Stackdriver for logging and monitoring since our infrastructure is all GCP based. It was lacking a lot, particularly on tracing and structured logging. We often had a lot of trouble triaging and diagnosing a production problem. Datadog's specialty is observability. Since we started using the product, we were able to create dashboards, and utilize APM, continuous profiling, RUM, and distributed tracing for production support and user trends.
Datadog also offers labs and workshops for its products, which is very helpful.
What about the implementation team?
We implemented the product ourselves.
What was our ROI?
I'm not sure what our ROI would be.
What's my experience with pricing, setup cost, and licensing?
We started with on-demand pricing as we were re-writing our product, and we weren't sure about the total usage. After we went into production and released the product, we experienced a price surge. Fortunately, our Datadog account manager reached out to us and suggested a monthly subscription, which is what we'll be switching to.
I'd advise keeping an eye on the usage and possibly setting up some monitoring on price. We didn't have much of a setup cost; we started with a free trial and continued with on-demand after the trial ended.
Which other solutions did I evaluate?
We didn't evaluate many of the other options. However, we do also use OpenTelemetry, which is vendor agnostic and integrates with Datadog.
What other advice do I have?
We always keep the Datadog agent to the latest version.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Google
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 31, 2022
Flag as inappropriateBuyer's Guide
Datadog
June 2023

Learn what your peers think about Datadog. Get advice and tips from experienced pros sharing their opinions. Updated: June 2023.
708,830 professionals have used our research since 2012.
CTO at a tech vendor with 1-10 employees
Increases delivery velocity with les manual testing and good integrations
Pros and Cons
- "Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity."
- "Since the Datadog platform has so many separate features, solving so many use cases, there are often inconsistencies in feature availability and interoperability between products."
What is our primary use case?
We use Datadog for three main use cases, including:
- Infrastructure and application monitoring. It is ensuring that our services are available and performant at all times. This allows us to proactively address incidents and outages without customers contacting us. This includes monitoring of cloud resources (databases, load balancers, CPU usage, etc.), high-level application monitoring (response times, failure rates, etc.), and low-level application monitoring (business-oriented metrics and functional exceptions to customer experience.
- Analyzing application behavior, especially around performance. We often use Datadog's application performance monitoring on non-production environments to evaluate the impact of newly introduced features and gain confidence in changes.
- End-to-end regression testing for APIs and browser-based experiences. Using Datadog's synthetic testing checks periodically that the system behaves in the exact correct way. This is often used as a canary to detect issues even before users reach them organically.
How has it helped my organization?
Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity.
We have seen time after time that the monitors we have carefully created based on all ingested data are detecting issues quickly and accurately.
This means we allow ourselves to manually test things less frequently. We have also had an easier time investigating application errors and slowness using Datadog's APM and log explorer products which allow us to introspect any part of the system, in its execution context.
What is most valuable?
The most valuable features include:
- Integrated observability data ingestions: All data that Datadog collects is connected. This allows easily connected logs with failed requests, and slow database questions with services and requests.
- Broad integrations allow us to monitor our entire production environment in a single place, not just cloud resources. Since all parts stream metrics, logs, and events to Datadog, we can have unified dashboards and manage monitors and incidents all from the same page.
- A high level of configuration. We can configure and modify many parts, from how data is collected from our applications to how Datadog parses and visualizes it. This means that we always get the best experience, and we don't need to find ten different products that do small things well or settle on one product that does everything badly.
What needs improvement?
Since the Datadog platform has so many separate features, solving so many use cases, there are often inconsistencies in feature availability and interoperability between products.
Older, more mature products tend to be complete (many features, customization, broad integrations, etc.), while newer products will often be at a "just above minimum viable product" phase for a long time, doing what's intended yet missing valuable customizations and integrations.
For how long have I used the solution?
We've used the solution for 12 months.
What do I think about the scalability of the solution?
The solution scales very well on technical aspects, being able to ingest large quantities of data from many services. However, the pricing often doesn't scale naturally, and effort has to be put in to keep ongoing costs at a reasonable amount.
How are customer service and support?
Customer service and support are generally very high-quality. In most cases, they reply very quickly and offer well-researched and relevant responses. This is contrasted with many vendors who take a long time to reply and send links to documentation instead of understanding the problem.
However, we had cases where support took several weeks to reply to a complicated request and sometimes eventually responded that the issue cannot be resolved. These are rare edge-case occurrences.
How would you rate customer service and support?
Positive
How was the initial setup?
A large part of the initial setup was straightforward. We were able to collect about 80% of the relevant and 90% of the meaningful insights from just a couple of hours of connecting the AWS integration and the Datadog APM agent.
Getting it to 100% and configuring and customizing things to our unique situation, took about two weeks. Datadog's documentation and support team were extremely helpful during both phases.
What about the implementation team?
We handled the setup in-house.
What was our ROI?
From the number of outages stopped or shortened (which lead to lost revenue from non-renewals) and the number of hours saved on investigations (which correlates to engineering salaries), I estimate that the ROI of the implementation time and monthly charges to be between 10x and 20x.
What other advice do I have?
We use the solution as a SaaS deployment.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 31, 2022
Flag as inappropriateArchitect at SEI Investments
Great support with a helpful APM and profiler
Pros and Cons
- "The most valuable aspects of the product include the APM and profiler."
- "I find the training great. That said, it is set for the LCD (lowest common denominator). Of course, this is very helpful to sell the product, yet, to really utilize the product, you need to get more detailed."
What is our primary use case?
We primarily use Datadog for:
- Native memory
- Logging
- APM
- Context switching
- RUM
- Synthetic
- Databases
- Java
- JVM settings
- File i/o
- Socket i/o
- Linux
- Kubernetes
- Kafka
- Pods
- Sizing
We are testing Datadog as a way to reduce our operational time to fix things (mean time to repair). This is step one. We hope to use Datadog as a way to be proactive instead of reactive (mean time to failure).
So far, Datadog has shown very good options to work on all of our operational and development issues. We are also trying to use Datadog to shift left, and fix things before they break (MTTF increase).
How has it helped my organization?
We are currently in a POC and do not own Datadog at the moment.
So far, there have been a few issues due to security. There are two main security issues.
The first is moving data off-prem. This has been resolved to a point (filtering logs, etc). However, there is still an issue with moving a JFR as a JFR potentially contains data that is not allowed off-prem.
The second security issue is more internal, however, the main installation requires root access or using an ACL. Our company does not use ACLs on our Linux platform. This is problematic since the install sets a no-login on the Datadog user.
What is most valuable?
The most valuable aspects of the product include the APM and profiler.
These two have given us insights into things that are very difficult to track down given the standard OS (Linux) tools.
The native memory tracking is super difficult to see exactly where it comes from. I attended a course (continuous profiling), and it showed me the potentially very important capabilities.
If you add these details to a standard dashboard, or a sub-dashboard for techy people, or even just a notebook, it would be easy to identify issues before they occur.
Combining these details with the basic tools (infra, logging, APM, and good rules), Datadog can easily show the details that a true engineer would need. It isn't just for monitoring, however, I see the value in it for engineers.
What needs improvement?
I have done every training offered (and in a short period of time: two days for 20 courses).
I find the training great. That said, it is set for the LCD (lowest common denominator). Of course, this is very helpful to sell the product, yet, to really utilize the product, you need to get more detailed.
If I did the training as it is written and I cut/paste a bunch of stuff and see the cut/paste work, I didn't really learn anything. Later sessions (I quit using the editor and switched to VI) stopped cutting and pasting, and learned much more.
For how long have I used the solution?
I've used the solution for one month.
What do I think about the stability of the solution?
I' give stability a thumbs up.
What do I think about the scalability of the solution?
We are not sure yet in terms of scalability. The off-prem solution seems to scale well (although had issues with the training slowing down).
How are customer service and support?
Technical support is great.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I previously used Dynatrace and Elastic. We didn't switch. We are in a POC.
How was the initial setup?
The initial setup is simple yet complex. There are too many teams are needed.
What about the implementation team?
We did the initial setup in-house.
What was our ROI?
In terms of ROI, the labor saving is probably the biggest. The NPR is probably second - although management would probably reverse these.
What's my experience with pricing, setup cost, and licensing?
Pricing and licensing is fairly complicated. A GB for .1 sounds great, however, once you put all 16 or so prices together, it adds up fast. A cost model sheet on the main site would be very helpful.
Which other solutions did I evaluate?
We are currently in a POC.
What other advice do I have?
We work with all product versions.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 31, 2022
Flag as inappropriateStaff Engineer at a tech services company with 1,001-5,000 employees
Great distributed tracing and flame graphs for debugging with a relatively painless setup
Pros and Cons
- "We like the distributed tracing and flame graphs for debugging. This has been invaluable for us during periods of high traffic or red alert conditions."
- "Once Datadog has gained wide adoption, it can often be overwhelming to both know and understand where to go to find answers to questions."
What is our primary use case?
We are using a mixture of on-prem and cloud solutions to bridge the gap with healthcare entities in the service of providing patients with the medication they need to live healthy lives.
Since we're a heavily regulated company, a lot of our solutions grew from on-premises monoliths. However, as we scaled out, it became harder and harder to move forward with that architecture. Today, we're investing heavily in transforming our systems from monoliths into distributed systems.
With this change in mind, the ability for us to connect the dots using Datadog has been invaluable.
How has it helped my organization?
We have an API that serves as a critical aspect of our system for generating new requests for us to process in service of a patient. This service has many tentacles, and it was always hard to track down how issues from this API are affecting things downstream. Since we've added more instrumentation in this API, Datadog has changed our status from a reactive posture to a proactive one.
It has also served as a prime example to other applications on what the benefit of a well-instrumented system is for that application and other applications around it. Due to this, more and more people are using Datadog.
What is most valuable?
We like the distributed tracing and flame graphs for debugging. This has been invaluable for us during periods of high traffic or red alert conditions. It has also informed our developers on how our various systems are interconnected and the downstream effects of the problems we might encounter for certain services.
We're still working on getting widespread adoption of these products. Still, we're already seeing a shift in the developer's perspective from application-specific and starting to look at things from a more holistic systems perspective.
While this is not part of the question, this is relevant: Now that I've learned more about RUM, this will be something that we will heavily leverage moving forward to give us a whole complete view of our system from the front and back end perspective.
What needs improvement?
Once Datadog has gained wide adoption, it can often be overwhelming to both know and understand where to go to find answers to questions. Currently, we use a combination of documentation and COPs to ensure that folks know how to leverage what we have in Datadog properly.
While the guides for Datadog go a long way, a way to customize the user experience from "advanced" to "novice" mode would go a long way.
For how long have I used the solution?
I've been using the solution for two years.
What do I think about the stability of the solution?
It has never failed us and therefore I consider it to be very stable.
What do I think about the scalability of the solution?
It's magic. For the most part, we just installed the product and a lot of it just worked out of the box.
How are customer service and support?
Technical support is excellent.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We have used Splunk, Sentry, and a suite of hand-made solutions. We switched since the Datadog solution was both comprehensive and cohesive. It was also easier to onboard people since the solution was well-documented and standardized.
How was the initial setup?
For the most part, it was really painless to set up.
What about the implementation team?
We implemented the solution in-house.
What was our ROI?
We're still early on in our transformation process. That said, we are gaining a lot of steam in terms of adoption. Both the engineering team and the product team are seeing tremendous value from this solution.
What's my experience with pricing, setup cost, and licensing?
Which other solutions did I evaluate?
What other advice do I have?
Adding more tooltips and links to documentation or how-tos within the application would really go a long way for those trying to get their feet wet with Datadog.
Which deployment model are you using for this solution?
Hybrid Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 30, 2022
Flag as inappropriateDevOps Engineer at a printing company with 51-200 employees
Great visibility, good logs, and a helpful dashboard
Pros and Cons
- "For us to have visibility into our app stack and the hardware we run has been highly beneficial."
- "I want to applaud the efforts in making the UI extremely usable and approachable. My suggestion would be to take another look at how the menu structure is put together, however. Even after using the platform mostly every day for months, I still find myself trying to find a service or feature in the menus."
What is our primary use case?
Log aggregation for us was a key component since we have a fairly old-school app running on VMs on bare metal. We previously didn't have much insight into our logs unless we manually tunneled them into each server.
The solution is reducing manual labor in troubleshooting problems in our environments server by server.
We also needed to monitor our Java app and MySQL database to understand their problems so that we could take action and resolve them.
Our use cases have since expanded to encompass all aspects of monitoring.
How has it helped my organization?
Before Datadog, all we had to go on was the gut reaction of the old guard on our team. While useful, the reactions and inherent knowledge only benefited a few folks.
Datadog has allowed us to create comprehensive dashboards and proactively send out alerts. We used the knowledge of people very versed with our products to help set up the platform and have since benefited from that.
The operative word here is visibility, and we've seen a huge improvement in that.
What is most valuable?
Seeing log trends and patterns and aggregate search was a huge first step for us. We then began using other features of the Datadog platform by enabling APM. After that, we did other integrations.
For us to have visibility into our app stack and the hardware we run has been highly beneficial.
We leverage APM, log management, and at least ten other integrations. Our DB, web servers, network, storage, and other areas are now monitored and hooked up to dashboards.
Dashboarding has also proven useful when information is going to be viewed by anyone in the organization.
What needs improvement?
Our experience has been overwhelmingly positive so far. That said, there is one area that could benefit from some polish. For example, I want to applaud the efforts in making the UI extremely usable and approachable. My suggestion would be to take another look at how the menu structure is put together, however. Even after using the platform mostly every day for months, I still find myself trying to find a service or feature in the menus.
For how long have I used the solution?
I've used the solution for around six or eight months. We've had the Datadog agents deployed on our various environments.
What do I think about the stability of the solution?
So far, we have not had any issues with stability. It should be very stable and easy to update.
What do I think about the scalability of the solution?
The solution is currently deployed on a limited scale. That said, we see the potential and benefits of deploying this in a cloud scenario.
How are customer service and support?
Customer service and the support teams have been very responsive when we need them. They are very professional.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
This was our first solution in this space.
How was the initial setup?
The initial setup steps with the agent are only confusing when using the config files for the first time. The main file includes a lot that you can specify elsewhere and it's not readily apparent which one to use until you dig in more.
What about the implementation team?
We did an in-house implementation.
What was our ROI?
Our ROI with Datadog has been very high. It's given us the ability to see how we're performing, which we didn't have before.
What's my experience with pricing, setup cost, and licensing?
Ensure you have your ingestion pipelines dialed in, or you'll likely spend more than you were expecting.
Which other solutions did I evaluate?
We evaluated free and open-source options, however, ultimately, we decided that we didn't have the manpower as a small company to maintain them.
What other advice do I have?
There is nothing that the documentation cannot help with; it's very good.
Which deployment model are you using for this solution?
Private Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 31, 2022
Flag as inappropriateSenior Engineer at a educational organization with 5,001-10,000 employees
I like the amount of tooling and the number of solutions they sold with their monitoring.
Pros and Cons
- "I like the amount of tooling and the number of solutions they sold with their monitoring. Datadog was highly intuitive to use."
- "Datadog needs more local Asia-Pacific support, and if they don't have a SaaS solution in Asia-Pacific, they should offer an on-prem version. I'm told that's not possible."
What is our primary use case?
Datadog is a SaaS solution we tried for URL and synthetic monitoring. You record a transaction going into a website and replay that transaction from various locations. Datadog is mainly used by the admin, but three or four other guys had access to the reports and notifications, so it's five altogether.
We probably tried no more than 8 percent of what Datadog can do. There are so many other bits and modules. I've only gone into about half of what APM can do in the Datadog stack.
How has it helped my organization?
We could detect outages on particular websites or problems in specific locations. If I had paid for the full solution, I'm sure I could get a lot of value out of Datadog.
What is most valuable?
I like the amount of tooling and the number of solutions they sold with their monitoring. Datadog was highly intuitive to use.
What needs improvement?
Datadog needs more local Asia-Pacific support, and if they don't have a SaaS solution in Asia-Pacific, they should offer an on-prem version. I'm told that's not possible.
For how long have I used the solution?
I have used Datadog for about two or three years.
What do I think about the scalability of the solution?
I was only using Datadog to monitor on a small scale.
How are customer service and support?
I'd rate Datadog support four out of 10. It was primarily an issue with support in the Asia-Pacific region. I sent them several emails, and they responded around three weeks later.
They said it went around the houses. Nobody knew who to respond to. That's not good enough. They should have at least told me they'd received the email. I used to work in support.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
We were just trying Datadog, and we've switched temporarily to Site24x7. We're looking for one of the bigger ones. They've all given us proposals, whereas Datadog hasn't come forward with a proposal for what they could do.
I used Datadog because I already had a relationship with them at a previous company. However, that guy's moved on now, and I wanted to see how good they were.
How was the initial setup?
Setting up Datadog is pretty straightforward. I have a lot of experience doing that sort of thing. It took maybe a day and a half to deploy because I was picking externally facing websites.
I deployed it by myself. One person is enough for the small system we had. However, if we were moving forward, I'd recommend at least two or three people to manage it.
What's my experience with pricing, setup cost, and licensing?
Datadog would've cost around $850 a month based on the loads we were doing, and you could estimate roughly what you would be paying monthly. I liked their pricing model. It was flexible, so you only paid for what you used. I rate Datadog pricing eight out of 10.
Which other solutions did I evaluate?
We looked at several URL and APM monitoring solutions like Site24x7 and Pingdom. They weren't big players like Dynatrace or any of the those that had already provided us a request for information.
What other advice do I have?
Even with our negative experiences, I'd still give Datadog an eight out of 10. Datadog is a complete solution with easy-to-use templates and excellent scalability. People should know exactly what they're going to configure before they try it out. The trial is brief. Don't start a trial until you know exactly what you're going to do.
You must be certain that you can meet any internal security requirements. If you're in the Asia-Pacific region, you might not be able to run something that's running abroad.
Which deployment model are you using for this solution?
Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Lead Software Engineer at a retailer with 51-200 employees
Great APM and interesting log management but the UI is daunting
Pros and Cons
- "The most useful feature is the APM."
- "As a new customer, the Datadog user interface is a bit daunting."
What is our primary use case?
We are trying to get a handle on observability. Currently, the overall health of the stack is very anecdotal. Users are reporting issues, and Kubernetes pods are going down. We need to be more scientific and be able to catch problems early and fix them faster.
Given the fact that we are a new company, our user base is relatively small, yet growing very fast. We need to predict usage growth better and identify problem implementations that could cause a bottleneck. Our relatively small size has allowed us to be somewhat complacent with performance monitoring. However, we need to have that visibility.
How has it helped my organization?
We are still taking baby steps with Datadog. Hence, it's hard to come up with quantifiable information. The most immediate benefit is aggregating performance metrics together with log information. Having a better understanding of observability will help my team focus on the business problems they are trying solve and write code that is conducive to being monitored, instead of reinventing the wheel and relying on their own logic to produce metrics that are out of context
What is most valuable?
The most useful feature is the APM. Being able to quickly view which requests are time-consuming, and which calls have failed is invaluable. Being able to click on a UI and be pointed to the exact source of the problem is like magic.
I'm also very intrigued by log management, although I haven't had quite a chance to use it very effectively. In particular, the trace and span IDs don't quite seem to work for me. However, I'm very keen on getting this to work. This will also help my developers to be more diligent and considerate when creating log data.
What needs improvement?
As a new customer, the Datadog user interface is a bit daunting. It gets easier once one has had a chance to get acquainted with it, yet at first, it is somewhat overwhelming. Maybe having a "lite" interface with basic features would make it easier to climb the learning curve.
Maybe the feature already exists. However, I'm not sure how to keep dashboard designs and synthetic tests in source control. For example, we may replace a UI feature, and rebuild a test accordingly in a pre-production environment, yet once the code is promoted to production, the updated test would also need to be promoted.
For how long have I used the solution?
We have just started using the solution and have only used it for about two months.
What do I think about the stability of the solution?
We're new at this. That said, so far, there haven't been any issues to report.
What do I think about the scalability of the solution?
I have not had the opportunity to evaluate the scalability.
How are customer service and support?
Customer support is full of great folks! We're beginning our Datadog journey, so I haven't had that much experience. The little I have had has been great.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
This is all new.
We used to work with New Relic. New Relic has an amazing APM solution. However, it also became cost-prohibitive
How was the initial setup?
Since we are relatively greenfield, it was relatively painless to set up the product.
What about the implementation team?
Our in-house DevOps team did the implementation.
What was our ROI?
I don't know what the ROI is at this stage.
What's my experience with pricing, setup cost, and licensing?
I'm not sure what the exact pricing is.
What other advice do I have?
So far, it's been great!
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 31, 2022
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