Datadog Reviews and Ratings

Use Cases and Deployment Scope

With the Platform Reliability Engineering team supporting legacy core banking applications that has multiple services was always a challenge. With Datadog APM distributed tracing, anomaly detection algorithms for the transactions break, supporting the infrastructure monitoring for both onprem and kubernetes cluster is made easy from our org. Monitoring the core banking application is our usecase.

Likelihood to Recommend

Well suited for cloud applications and less appropriate for applications still running on legacy programming languages.

Use Cases and Deployment Scope

Datadog is used in our organization to monitor applications, infrastructure, logs, and user experience on a unified platform. Datadog consolidates all tools into one platform. Moreover, it provides end-to-end visibility across servers, containers, databases, APIs, and third-party services. Datadog helps us by enabling faster troubleshooting and root cause analysis.

Likelihood to Recommend

As per my experience, Datadog is best suited for complex, cloud-native environments where unified observability is critical, as it integrates seamlessly with AWS and Azure. Moreover, it provides deep visibility into latency and error rates. Datadog pricing is less appropriate for Startups with a tight budget and for organizations needing advanced incident management.

Use Cases and Deployment Scope

We're a global ad-platform company with hosts across multiple regions on both AWS and GCP. Datadog helps us manage multi-cloud complexity, monitor latency and ad-delivery performance, and ensure high availability at scale.It lets us correlate logs, metrics, and sometimes traces to quickly diagnose issues, reduce mean time to resolution, and prioritize capacity and cost optimizations. Datadog provides a unified view for engineering and SRE teams, drives alerting and incident management, and supports post-incident analysis—covering infrastructure, services, and application performance across the entire pipeline.

Likelihood to Recommend

Datadog shines when correlating logs, metrics, and traces across systems/cloud providers.

The various integrations (either native or via the agent) make it straightforward to get unified views over complex systems.

That said, it can be costly and the pricing is often opaque, which complicates forecasting and optimization. Non-trivial amounts of time are spent seeing what we should emit/index, and many features are outright forbidden given our data volumes. Its breadth is also a double-edged sword: without careful tuning you can very quickly hit alert fatigue.

Use Cases and Deployment Scope

<div>We use Datadog as the main Observability and Appication Monitoring Tool in our organization.</div><div>It excel at our :</div><div>1 Incident Detection and Response -Wr are able to have real time monitoring of apps and service health ,with alerting configured via integrations like PagerDuty.This help us to detect Incidents quickly ,reduces downtime and ensures we meet our SLA/SLO targets.</div><div>2.End -to- End Observability - we are able to trace requests,analyse logs and monitor metrics to spot issues particulary in our Envoy mesh architecture and coroutine-driven workloads.</div>

Likelihood to Recommend

<div>Datadog is the best for looking out the root cause of incidents ,early last month we encountered an issue with one of our main microservices kept failing with many errors we used Datadog's traces ,logs and Kubernetes Explorer to identify the root cause .This helped us to deploy a quick fix which restored the application back to service ,till today we have experienced any failure.</div><div>Has a wide integration with cloud services which makes it very flexible for monitoring.</div>

Use Cases and Deployment Scope

We use Datadog for centralized monitoring and observability ,tracking app performance metrics, logs and root cause analysis.We use it to collect logs ,metrics and traces all in one place which make it much effortless to detect and troubleshoot issues before they cause any harm.Using APM and monitors ,we are able to identify performance bottlenecks and track down root causes.In addition to app monitoring we use it to monitor our log management for our apps that are both on premises and in the cloud via Datadog's AWS integration.

Likelihood to Recommend

Datadog is the best , helps us in monitoring and logs from all our applications for example we send logs into Datadog for troubleshooting and alerting purposes.Also we have multiple monitors set up that provide us a heads up early whenever there's a problem with end users getting to the applications that are available to them .Also it allow shared shareboards ,alerts and insights which helps our teams stay on same page on problems and priorities.

Use Cases and Deployment Scope

I use Datadog for different reasons, and they are: (1) to monitor my cloud (infrastructure) resources (as I have multiple platforms deploy on different cloud providers); (2) to test my SaaS application (mostly API) from different regions (a multi-location test check); (3) to check APM (Application Performance Monitoring) to identify bottlenecks and which API region is slow (in order to investigate why it is slow) and finally (4) I am moving my Grafana SLIs/SLOs into Datadog to have all observability in one tool.

Likelihood to Recommend

Datadog is well suited for cloud infrastructure monitoring, where you can create monitors to track the health of cloud resources and if the monitor is triggered you can then notify different teams like your NOC/SOC team. Another case were Datadog is well suited is for APM (Application Performance Monitor) where you can create SaaS platform checks and use Datadog edge servers to execute them for you, testing latency, performance, and finding problems on different regions.

Now, the use cases were Datadog is less appropriate is for Log Management, as the retention of logs and the creation of log indexes can consume a lot of money in a short period of time. So if you are not aware of it, consider this as food for thought!

Use Cases and Deployment Scope

Datadog is used daily to alert our company, and respective departments of any potential issues that may be occuring. We receive a daily report that list and details metric events that can indicate a bigger issue and allows us to pinpoint the problem in order to resolve it or determine if it is a high alert issue or not. Our analytical department used the Datadog software to detail and monitor specific metrics migrated from multiple in-house applications.

Likelihood to Recommend

Easy to monitor metrics like HTTP/Ping/CPU. They have really good graphs and dashboards while maintaining ease of use. Also easy to install collection agents and an overall very polished product for small and large companies. Sometimes data may be a little overwhelming depending on your departments uses but overall great product.

Use Cases and Deployment Scope

I use Datadog to record logs, investigate issues, monitor resource usage, and setup alarms to notify teams about certain issues. I also occasionally use the AI Watchdog to see if it notices new issues in the logs.

Likelihood to Recommend

Datadog does a sufficient job to monitor logs, resource usage, and setup alerts. I use it everyday as a tool to help investigate issues.

The AI watchdog can be improved since it generally raises anomalies that are not interesting.

Use Cases and Deployment Scope

We assessed Datadog as a provider for telemetry and user monitoring to aid in our optimization and troubleshooting efforts of our web application. We were looking to measure overall performance of HTTP endpoints and background jobs, surface errors and inefficient areas, and drill down into problem areas and dependencies (DB, AWS services, external web services, inter-service communication, etc)

Likelihood to Recommend

Datadog may be better suited for teams that have a more out-of-the-box infrastructure, on the primary platforms Datadog supports. You may also have better results if you have a bigger team dedicated to devops and/or a bigger budget. We found that trying to adapt it to our use case (small team, .NET on AWS Fargate) wasn't feasible. We continually ran into roadblocks that required us to dig through documentation (and at times, having to figure out some documentation was wrong), go back and forth with support, and in my opinion, waste money on excessive and unintended usages due to opaque pricing models and inaccurate usage reports, as well as broken/non-functional rate sampling controls.

Use Cases and Deployment Scope

We use Datadog for monitoring and observability across our org. It gives us visibility into what's really happening when something goes wrong via tracing, and its monitors have alerted us to issues countless times before a customer could complain. We monitor performance via metrics and can dig into specific calls using traces and flame graphs to see where bottlenecks are.

Likelihood to Recommend

Datadog does well at its key use cases of providing visibility into an application across multiple services, via logs, traces, metrics (including custom metrics), and powerful monitoring. The main reason my rating isn't higher is due to some of the UX concerns around APM queries. While it's usually possible to get the data I want, it seems harder than necessary.