Datadog Enterprise

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.

What do you like best about the product?

It brings metrics, logs, traces, and alerts into a single, intuitive platform.
Real-time dashboards and powerful visualizations make it easy to identify issues quickly.

What do you dislike about the product?

Fine-tuning alerts and dashboards often takes time to avoid noise and false positives.

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

It centralizes metrics, logs, traces, and alerts across applications, infrastructure, and cloud services in one platform.

What do you like best about the product?

Firstly, Its integration capabilities to Hosts (Windows/Linux/mac), Platforms (AWS, Azure which I use, plus GCP etc) and container platforms (Docker, Kubernetes) etc benefits alot of usecases as DD server as a one stop shop. We have support for multiple programming languages to easily publish logs to Datadog directly right form your application code, eliminating any fancy stuff.

We have very reliable feature slike smart health checks and automated test suites so we catch problems before they hit. On-call teams get instant alerts, incident triage, and even automated workflows for triage etc enhance teams to focus fixing issues quickly and stress-free with some readily available first hand information.

Dashboards with visualizations like line, bar, pie, and timeseries charts cater to different use cases—such as applications, infrastructure, and databases—making it easier to monitor performance. DD become an integral part of our daily operations, helping outquickly spot anomalies and simplifying the overall and managing workflows.

Its easy to setup/install/implement agent configuration (Pre designed Installation URL with installation script) doesnt take more than 5mins. Users can readily build dashboards in under 15 mins for prod grade setup. [ In general its just 5mins as publicised by Datadog].

DD do has great customer support but we rarely need that as most of the stuff has documentation and easy to setup or configure.

What do you dislike about the product?

In our current context,
As our infrastructure or application footprint grows, storage costs increase proportionally and can become a major expense. If we need to retain data for extended periods, expect those costs to rise even further (so storage necessity is the key).

Just like other platforms, Datadog also offers numerous integrations with third-party platforms like Slack, Microsoft Teams, and Jira. We leveraged on all these channels initially that lead to increased costs, as each integration added complexity and resource usage along with increase complexity implementing them. We had to strip someof them to manage cost and purpose of applications at different environment levels.

There are so many options for same purpose but without proper guidance or complete understanding of that usecase, we may en dup implement more than what is required. So purpose is key here.

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

1. Datadog is helping us providing the complete picture of problem with some initial details and by giving us a single platform to monitor 50+ microservices across 40+ AWS accounts, so nothing slips through the cracks. we have some first hand information based on automation or test suits logs, we know where to check, leading to less turn around time.

2. It tackles incident management/response challenges with real-time alerts, on-call integration, and automated triage, identifying similar patterns, notes around the service and resolution documents helping us fix issues before they impact customers at large extend. Its integration to different platforms we manage (almost all) is really a value add.

3. Built-in health checks and test suites keep our systems in shape, while integrations with AWS, PagerDuty, Slack, and more make the whole workflow smooth and connected. Datadog eliminates tool silos and creates a smooth workflow for monitoring and incident resolution.

4. From service-level segregation to rich dashboards, Datadog turns most of our log data into simple insights for engineers and execs alike. Different dashboards at low level and higher level made our life easy from monitoring to presenting the data to higher-ups.

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.