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 Pro
DatadogExternal reviews
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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.
DataDog Delivers Deep, Reliable Visibility Across AWS and GCP
What do you like best about the product?
We use DataDog primarily for infrastructure monitoring across EC2 instances, EKS clusters, and more. It gives us full visibility into the critical systems we run, mainly on AWS and GCP. “Very functional” is the best way I can describe it, and it consistently provides deep insights into the systems and resources we operate across both services.
What do you dislike about the product?
I think the setup can be a bit complex, and you may need an understanding of things like agents. I also feel it would be better if there were an easier way to cover more of the resources, because setting up the agents wasn’t very straightforward. On top of that, there are quite a lot of monitoring services, so it can get overwhelming pretty quickly.
What problems is the product solving and how is that benefiting you?
Monitoring, visibility, and observability have been a big focus for us. The core AWS and GCP alerting services aren’t really easy to get up and running, and with so many different services we needed eyes on, we wanted everything in one central system. DataDog really solved that for us.
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)
Insightful Monitoring, Quick Integration
What do you like best about the product?
I really like how quickly data shows up in Datadog. It's really quick and easy to integrate webhooks with it, and we can search through the results quickly and easily to find examples of integrations working or not working. Being able to dig into API payloads and understand what's causing issues by looking at API responses in Datadog makes troubleshooting a lot easier for me. The ability to build dashboards and metrics to gain insights on our integrations also stands out.
What do you dislike about the product?
Sometimes, once you have searched for something and it has filtered down to a specific context, it can be difficult to know how to expand the context to include other sources.
What problems is the product solving and how is that benefiting you?
I use Datadog to track errors in integrations, troubleshoot export issues, and check API error rates. It provides insights into system performance and helps me build dashboards to monitor API response times and integration status.
Very Easy to Use and Incredibly Useful for My Job
What do you like best about the product?
It’s very easy to use and has been really useful for my job.
What do you dislike about the product?
Honestly, there’s nothing I really dislike about it. It’s a very good product overall.
What problems is the product solving and how is that benefiting you?
It’s useful for tracking logs and monitoring custom metrics, and it makes it easier to follow what’s happening over time.
All-in-One Observability That Speeds Up Root Cause Analysis
What do you like best about the product?
I like the concept overall, the system that tracks every data point your applications provide and you can collect and analyse it in a single space.
It basically allows to find the root cause of issues much faster as you are able to correlate data from different sources (server load, logs, network performance etc.)
And because of all those data agregated in one place you can setup notifications based on multiple metrics together, not just one. Or even do something with webhook.
It basically allows to find the root cause of issues much faster as you are able to correlate data from different sources (server load, logs, network performance etc.)
And because of all those data agregated in one place you can setup notifications based on multiple metrics together, not just one. Or even do something with webhook.
What do you dislike about the product?
I personally don't really enjoy Datadog's interface, it does look modern and UI elements are small, but I don't have any other complaints so far.
What problems is the product solving and how is that benefiting you?
Datadog allows to identify what caused some problem fast, closely monitor systems with useful notifications that based on multiple metrics, and improve and analyse performace of applications I manage.
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.
Fast, Unified Observability That Speeds Up Production Root-Cause Analysis
What do you like best about the product?
What I like best about Datadog is how fast it helps teams understand what’s actually happening in production. The platform brings logs, metrics, traces, and real-time alerts into a single, intuitive view, so you’re not jumping between tools when something goes wrong. That unified observability makes it much easier to identify root causes quickly, especially in complex, distributed systems.
What do you dislike about the product?
Cost can escalate quickly. Pricing is usage-based, so as log volume, metrics, or hosts scale up, it’s easy for costs to grow faster than expected if usage isn’t closely monitored.
What problems is the product solving and how is that benefiting you?
Datadog is solving the core problem of not knowing what’s happening inside your systems when it matters most, and it benefits you by saving time, reducing stress, and helping you make better decisions.
Comprehensive APM with Installation Challenges
What do you like best about the product?
I like Datadog for its real-time traces and the ability to connect real user monitoring (RUM) with application performance monitoring (APM). This is very useful for me because it helps trace errors and map them with the real user experience. I also find it easy to reproduce cases with the detailed summary available in the dashboard.
What do you dislike about the product?
I find the installation process a bit complex, especially when setting it up on a Docker-based setup. It's very much difficult to set up compared to a normal VM-based server.
What problems is the product solving and how is that benefiting you?
I use Datadog to check latency, pinpoint errors, and trace errors effectively. The real-time traces and APM-RUM connection are useful for mapping errors with user experience and reproducing cases with detailed dashboards.
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