Datadog Enterprise

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.

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.

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.

I have been using Datadog for about three years to monitor our cloud-native application and infrastructure across multiple environments.

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.

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.

Their technical documentation is some of the best in the industry, and their support engineers are very proactive, helping us optimize the ingestion cost.

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.

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.

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.

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.

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.

Hybrid Cloud

Amazon Web Services  (AWS)

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.

What do you like best about the product?

What I appreciate most about DataDog are its reliability and observability. Even during major global outages, such as those affecting Cloudflare and AWS, DataDog continued to perform without any issues. The APM, RUM, and Synthetic Checks are exceptionally dependable, giving me the confidence to choose DataDog without hesitation.

What do you dislike about the product?

I wish the UI had a more modern look, as it currently feels like I'm using an old inventory management system from the 2000s.

What problems is the product solving and how is that benefiting you?

DataDog has been highly beneficial for us in many ways, from monitoring synthetic checks to tracking APM traces. The platform provides a map of interconnected, dependable services, allowing us to view latency and error rates all in one central location. The alerting mechanism is easy to use, supports numerous integrations, and consistently works smoothly.

What do you like best about the product?

Collecting all important monitoring information in one place so if a problem arises it is faster to find the cause as every metric is collected.

What do you dislike about the product?

I find its interface a bit outdated and unintuitive

What problems is the product solving and how is that benefiting you?

It provides a hub to collect a lot of data points from different applications in one place which makes it much easier to access as well as setup notifications based on multiple data sources.

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.

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.