The main use case for Datadog is to troubleshoot using application logs, and that is a great use case. Whenever we have integrated Datadog with all the application logs, we receive all the application logs there. Because CloudWatch logs are so expensive to use, we have integrated with Datadog, and it is very cost-friendly. The best use case is whenever we receive an error or suppose we have an issue going on, we can check it using Datadog logs and we can resolve it based on the logs.
Datadog Agent
DatadogExternal reviews
749 reviews
from
and
External reviews are not included in the AWS star rating for the product.
Datadog as a Single Source of Truth for Metrics, Traces, and Logs
What do you like best about the product?
What I like most about Datadog is that it can act as a single source of truth for our entire stack, helping break down the silos between infrastructure metrics, APM, and log management. During an incident, instead of jumping between three different tools, my team can quickly pivot from a spiked CPU metric to the relevant trace and the corresponding logs in just a couple of clicks.
What do you dislike about the product?
The learning curve is pretty steep. Since Datadog has expanded into so many areas (Security, CI Visibility, Real User Monitoring), the UI can feel cluttered and overwhelming—especially for new team members. On top of that, the cost of log indexing and retention is a major hurdle. I like the 'Logging without Limits' concept in theory, but the price gap between ingesting logs and actually being able to search them (indexing) forces us to make tough decisions about what data to keep.
What problems is the product solving and how is that benefiting you?
By combining APM with Quality Gates, we’ve been able to automate our safety checks. We can now clearly see the direct impact each deployment has on our core web vitals and error rates.
Essential for Accurate Logging and Issue Resolution
What do you like best about the product?
I use Datadog to check logs and audits, and I appreciate how it shows the timestamps of logs and events, which makes communication with the customer easy and allows our engineering team to fix issues faster. I like the RUM logs and replay sessions because the RUM logs provide an accurate log of success and failures, which helps escalate with the engineering team to fix issues, and the replay session allows us to see how the user interacted with the UI.
What do you dislike about the product?
The UI seems cluttered at times with too many elements. It might be better if there were organized sections to easily access information. For example, if there are device-specific details, they should be under a section labeled 'device' where all related details and geolocations can be found. Also, it took some time to get a hang of it initially.
What problems is the product solving and how is that benefiting you?
Datadog shows log timestamps and events, easing customer communication and speeding issue resolution. RUM logs provide accurate success and failure reports for the engineering team, while replay sessions reveal user interactions with the UI.
Log monitoring has reduced costs and enables faster troubleshooting for cloud applications
What is our primary use case?
What is most valuable?
The integration part is very smooth in terms of Datadog. The best features Datadog offers are first integration, second it is reliable, we can rely on it twenty-four seven, and third it is cost-effective.
Datadog integrates with any of the tools, and for now, we are working on the AWS cloud, so it is very easy to integrate with any of AWS services. We can push any of the logs for any of the applications. Datadog seems to be very reliable and stable, so we can check the logs and check anything there.
In terms of positivity, Datadog is cost-effective. We have used CloudWatch as well sometimes, but CloudWatch is expensive to use. If we want to search for any of the log streams, it is quite expensive. The cost is so much higher for that particular tool. But in terms of Datadog, it is very much cheaper than CloudWatch logs. It is similar to CloudWatch, but it is more cost-effective.
Datadog has helped our team save money, time, and resources, and I am certain that it has saved our time and our money.
What needs improvement?
Datadog can be improved if there is an AI functionality enabled. Let's suppose we are receiving a number of errors; an AI-integrated feature can happen there and it just gives us a root cause analysis based on the report, based on the error logs, and which service and what error codes we have received. That is how we can improve it.
I believe that is something which every organization wants, and I guess that is something really important because everyone wants the root cause after an incident has occurred.
For how long have I used the solution?
I have been using Datadog for the last five years.
What do I think about the stability of the solution?
I have never seen downtime with Datadog. It is pretty reliable.
What do I think about the scalability of the solution?
We can scale N number of things in Datadog. It is pretty scalable with no issues in the scaling part.
How are customer service and support?
I never reached out to customer support because I did not have to. Because we had no downtime for Datadog, we are good.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I have used Kibana before Datadog, but the problem with Kibana was there was so much downtime in Kibana. We used to have so many issues in Kibana and the troubleshooting was impacted due to that. So we switched to Datadog.
How was the initial setup?
I purchased Datadog through the AWS Marketplace.
What about the implementation team?
The implementation was amazing. The pricing is amazing and everything was so smooth with Datadog, so no issues at all.
What was our ROI?
I would say 100% return on investment. Let's suppose if I have to give an example of CloudWatch, it costs so much. If I have to search for two days of logs, it would cost me around three times more than what I search for from Datadog. Datadog is something which I can 100% rely on, and it is very cost-effective and totally worth it.
Which other solutions did I evaluate?
I have evaluated CloudWatch and Kibana, and hence I chose Datadog.
What other advice do I have?
I would recommend creating a data dashboard instead of searching for the logs. That feature is quite useful, so you can use the dashboards and you can monitor it from one place and also you can troubleshoot it from there as well using the logs. That is a very useful thing in Datadog. I give this review an overall rating of nine out of ten.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Unified monitoring has improved incident detection and reduced resolution time across our stack
What is our primary use case?
Datadog's main use case is end-to-end monitoring that helps check problems across infrastructure, application, database, security, and logs.
For example, when checking a problem with a mobile application such as an error from a user hitting a transaction, we check from the client-side mobile device and also from the back end for the API to see if there is latency or an error that triggers the problem. There may be an issue on the database, such as a locking query or high latency on query performance. For infrastructure, if the application is slow, it may be impacted on infrastructure monitoring by CPU and memory consumption.
Datadog is a powerful observability tool that allows us to correlate and see problems on the infrastructure or application side. In an incident war room, we can see the correlation and the detailed root cause of the problem across real user monitoring, application, database, and infrastructure.
How has it helped my organization?
Datadog has positively impacted our organization because our customers are very happy using it. With silo monitoring, where infrastructure has separate monitoring, application has another, and cloud has another, it becomes tricky and complex. We cannot correlate the silo monitoring, and pricing is complicated. With Datadog, we can centralize and use one observability tool for monitoring all components across all features or modules, unifying the monitoring process.
Regarding specific outcomes, I observe that tools with Datadog's capabilities enable us to quickly achieve mean time to detect problems. We can specifically check the root cause analysis of issues from the infrastructure, application, database, or security sides. Mean time to resolve is improved with Datadog since it provides many suggestions and actions to resolve problems, which heavily impacts the business for our application customers when issues arise.
What is most valuable?
Datadog's best feature is real user monitoring.
I prefer Datadog's real user monitoring most because of its analytics capabilities. First, Datadog is recognized in the Gartner Digital Experience for real user monitoring. Second, the analytics capability is very powerful, enabling us to check the experience of customers first. We can also correlate with the back-end side of the performance for real user monitoring and application monitoring. Finally, the capability of metrics within real user monitoring provides many helpful insights for mobile developers to improve their mobile application performance.
What needs improvement?
Datadog could improve its pricing because it is very tricky, and most of our customers notice many hidden costs. Additionally, if possible, Datadog should offer deployment options not only for SaaS but also for on-premises solutions, which would benefit banking transactions.
Regarding pricing, it remains tricky with many hidden costs. For technological enhancement, there could be an on-premises option alongside the SaaS version. I also find setting up and configuring Datadog to be very complex.
For how long have I used the solution?
I have been using Datadog for two years.
What do I think about the stability of the solution?
Datadog is very stable, and the features are quickly updated because the research and development process moves swiftly, making it reliable for fixes and updates.
What do I think about the scalability of the solution?
Datadog's scalability is very strong due to its cloud-native distributed architecture, massive data capability, extensive integration ecosystem, seamless expansion, and real-world scalability evidence.
How are customer service and support?
Customer support is very good because there is extensive support from Datadog, including live chat, ticketing, and a very high SLA of 99.98%.
Which solution did I use previously and why did I switch?
I was using Instana and Dynatrace as different solutions before Datadog.
What was our ROI?
I have seen a return on investment because Datadog helps save money and reduces the need for fewer employees while also saving time, which is very beneficial.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup costs, and licensing is that it is very tricky due to many hidden costs, so we need to check repeatedly for allotments and commitments regarding what we receive from the license.
Which other solutions did I evaluate?
I evaluated other options before choosing Datadog, specifically Dynatrace.
What other advice do I have?
My advice for others looking into using Datadog is to initially simplify the technical setup and configuration. Secondly, regarding pricing mechanisms, it would be wise to commit to clear allotments to avoid hidden costs for customers, as it significantly impacts pricing.
I believe Datadog is the largest single observability platform, with correlation as a differentiation factor, enterprise readiness as a measure, and cost management now being a key topic with a very clear roadmap and direction. I would rate this product nine out of ten.
Unified observability has improved incident response and now reduces downtime across environments
What is our primary use case?
My main use case for Datadog is unified observability, as I use it to correlate metrics, traces, and logs in a single pane of glass to ensure the health and security of our cloud infrastructure and application.
I correlate those metrics, traces, and logs using the Service Map to visualize dependencies between our microservices, and for example, during a latency spike, I can instantly see if there is a bottleneck in a specific database query or a downstream API, which allows me to route the issues to the right team immediately.
What is most valuable?
Datadog is an incredibly powerful daily driver for any engineer, and the recent addition of LLM observability for AI apps and Cloud Security Management makes it feel like a platform that is truly keeping up with modern tech trends. The dashboarding and alert integrations are great features offered by Datadog, giving us all the required information on a single screen, and the alert integration performs its job in a very good manner.
Datadog has positively impacted our organization, as it has eliminated many negative issues, which I call tool sprawl, by replacing four or five separate monitoring tools with one unified platform. This has improved our MTTR and broken down silos between Dev and Ops teams.
Since Datadog has been introduced, the response time when seeing an alert has increased, so alerts have been taken care of within less time and routed to the other teams who have been taking the required actions. This has given us a very positive approach towards the entire working culture.
What needs improvement?
Datadog is a platform that can be improved by making its pricing more predictable, as sometimes it is difficult to forecast exactly how much a new project will cost until after we have started ingesting the data.
When it comes to the documentation, we do not have much available right now, so if Datadog can improve the documentation part, it would really help the engineers to work on this.
Datadog is the most comprehensive observability tool on the market, and it only loses two points because the pricing for log ingestion can grow quickly if we do not carefully manage our filters.
For how long have I used the solution?
I have been using Datadog for about three years to monitor our cloud-native application and infrastructure across multiple environments.
What do I think about the stability of the solution?
Datadog is extremely stable, as it is built for high scalable environments and consistently maintains high availability, which is why I trust it as our primary monitoring tool.
What do I think about the scalability of the solution?
Datadog is built for hyperscale, as it automatically scales when we add new hosts or containers, and its Monitoring as Code approach via Terraform allows us to scale our monitoring setup instantly as our infrastructure grows.
How are customer service and support?
Their technical documentation is some of the best in the industry, and their support engineers are very proactive, helping us optimize the ingestion cost.
Which solution did I use previously and why did I switch?
I previously used a mix of open-source tools like Prometheus and Grafana, and I switched because manual upkeep was too high and I needed a platform that could handle logs and traces alongside metrics without having to manage the backend storage.
How was the initial setup?
Buying Datadog through the AWS Marketplace was seamless and helped me meet AWS spending commitments, and while Datadog's custom metric pricing can be complex, the setup cost is very low because the agent is easy to deploy.
What was our ROI?
I have seen a strong ROI through a thirty percent reduction in downtime and significant cost savings by identifying under-utilized cloud resources, for example, the ideal EC2 instances through their cloud cost management.
Which other solutions did I evaluate?
I evaluated New Relic, Dynatrace, and Amazon CloudWatch before choosing Datadog, and I chose Datadog because of its massive library of over seven hundred integrations and its superior user interface, which is easier for our developers to use daily.
What other advice do I have?
My biggest advice is to set up ingestion rules and filters early, as you should not send all your logs and metrics at once, and being selective about what you need to store can maximize your ROI from day one. I would rate this review as an eight.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Comprehensive Monitoring with Easy Setup
What do you like best about the product?
I really like how detailed the log traces can be in Datadog, and how I can search for specific logs based on labels and facets. Setting up Datadog agents was also very easy.
What do you dislike about the product?
Pricing can become really expensive at scale, especially when log ingestion and custom metrics are not carefully managed. It would really be nice to be able to view a cost dashboard, as I don't think Datadog has that feature.
What problems is the product solving and how is that benefiting you?
I use Datadog to gain insights into application metrics and monitor key metrics like memory and CPU usage. It also provides visibility into services deployed across clouds like GCP and AWS.
Unified Monitoring (APM) That Accelerates Issue Diagnosis and Incident Resolution
What do you like best about the product?
Datadog brings infrastructure, applications, logs, and security signals together in one place, which makes it much easier to understand what is really happening in an environment and to move quickly from detection to action. The correlation between metrics, traces, and logs is particularly valuable when diagnosing incidents, as it reduces guesswork and speeds up root cause analysis.
What do you dislike about the product?
While Datadog is extremely powerful, it can become difficult to control and predict costs in large or rapidly changing environments, particularly when ingesting high volumes of logs, metrics, and traces. Without strong governance and regular tuning, usage can grow quickly and lead to unexpected spend.
In addition, the breadth of features can sometimes feel overwhelming. Teams need time and clear ownership to configure dashboards, alerts, and monitors properly; otherwise, there is a risk of noise, alert fatigue, or under-utilisation of the platform’s capabilities.
In addition, the breadth of features can sometimes feel overwhelming. Teams need time and clear ownership to configure dashboards, alerts, and monitors properly; otherwise, there is a risk of noise, alert fatigue, or under-utilisation of the platform’s capabilities.
What problems is the product solving and how is that benefiting you?
Datadog helps us centralise logs and monitor our Java applications and APIs, and provides APM (Application Performance Monitoring) to quickly detect performance issues and troubleshoot incidents or bottlenecks.
Intuitive Interface That Makes Data Insights Effortless
What do you like best about the product?
The user interface is very intuitive, making it easy to gain insights from the data. getting data into datadog is quite simple due to the multiple integrations, so it get's ready to use in a few clicks, support is responsive, my team uses it every day.
What do you dislike about the product?
In terms of cost, this platform is not inexpensive. Additionally, making bulk changes across multiple widgets is not straightforward, which can be inconvenient.
What problems is the product solving and how is that benefiting you?
We leverage Datadog to generate alerts and reports for our services, which helps us maintain higher uptime and gain better visibility into any issues that arise.
Comprehensive Tracking Capabilities That Impress
What do you like best about the product?
It offers almost every possible way to track the application interactions.
What do you dislike about the product?
It's very expensive, and it's not easy to grasp for newbies
What problems is the product solving and how is that benefiting you?
It gives us insights into our application services and it allows us to catch issues. Also, it provides session replays that allow us to quickly spot issues with the way users interact with our application.
Empowers Confident Monitoring and Insightful System Analysis
What do you like best about the product?
Datadog is a powerful tool that gives us greater confidence in our company's systems and enhances our ability to detect outages. It offers a wide range of features, most of which we actively use. Datadog provides essential insights into our systems, which helps us investigate problems, identify issues, and monitor performance, these being just a few of the ways we rely on Datadog.
What do you dislike about the product?
The cost is one of Datadog's biggest drawbacks, there are some products that would be helpful to use but the cost makes them impractical. The cost of Mobile App Testing testing comes to mind as an example for this.
Additionally, we have experienced some frustration due to pricing changes. Our previous SKUs were grandfathered, but we were eventually required to switch to the newer, more expensive SKU pricing.
Additionally, we have experienced some frustration due to pricing changes. Our previous SKUs were grandfathered, but we were eventually required to switch to the newer, more expensive SKU pricing.
What problems is the product solving and how is that benefiting you?
Datadog helps gives our company confidence in knowing that our systems are being monitored and that issues can be detected and addressed by our observability team promptly. We also use the information collected in Datadog to assess the overall health of our systems and drive investigations for any issues detected.
showing 1 - 10