Datadog Reviews & Insights

Score8.7 out of 10

349 Reviews and Ratings

Try It For Free

#1 most frequent

Retail Trade

14.3%

3,965 installations of 27,655

#2 most frequent

Professional, Scientific, and Technical Services

13.0%

3,596 installations of 27,655

“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.”
— Director of Software Engineering, WPP (10,001+ employees)

#3 most frequent

Manufacturing

12.0%

3,324 installations of 27,655

“Quicker debugging of issues and root causing”
— SRE, Sibros (51-200 employees)

Overview

Synthesised from 29 reviews

Datadog serves as a comprehensive platform for monitoring, observability, and application performance management, with 86% of reviewers highlighting its utility in gaining real-time insights into system health. Organizations widely adopt it to centralize data for effective log analysis and debugging, a capability mentioned by 55% of users, and to leverage robust Application Performance Monitoring (APM) tools to identify and resolve bottlenecks, noted by 52% of reviewers. The platform is instrumental in proactive incident management through its alerting features, which 45% of reviewers emphasize for reducing downtime and improving response times. The product excels in data visualization, with 79% of reviewers commending its dashboards for providing real-time insights and facilitating data combination. Beyond visualization, Datadog's strengths include comprehensive log management and search, noted by 48% of reviewers, and its effective alerting mechanisms, mentioned by 45%. These core functionalities are enhanced by extensive and seamless integrations, which 28% of reviewers find crucial for a unified monitoring experience. However, reviewers frequently point to the platform's cost and pricing model as a significant concern, with 55% citing it as opaque, unpredictable, and prone to escalation. A steep learning curve and challenges with documentation are also noted by 31% of reviewers, indicating complexity in adoption. Furthermore, 24% of reviewers mention difficulties with dashboard usability, navigation, and customization, while 17% express dissatisfaction with alerting issues such as alert fatigue and false alarms. Despite these challenges, Datadog significantly contributes to business objectives by enhancing operational efficiency; 45% of reviewers report faster incident resolution, leading to reduced downtime and improved customer experience. The platform also fosters improved developer productivity, as 28% of users find it aids in correlating data and debugging. While 21% of reviewers acknowledge its potential for cloud cost savings through resource right-sizing, managing overall expenses remains a complex aspect.

Pros

Robust data visualization and customizable dashboards
Comprehensive log management and search capabilities
Effective and proactive alerting mechanisms
Powerful Application Performance Monitoring (APM) and traceability
Extensive and seamless third-party integrations

Cons

High and unpredictable cost with opaque pricing models
Steep learning curve and complex documentation
Usability challenges with dashboard creation and navigation
Issues with alerting fatigue and false positives
Needs enhancement in log management for very high volumes

Why it matters:

Datadog serves as a foundational tool for organizations seeking comprehensive visibility into their infrastructure and applications. Reviewers frequently leverage it to monitor key services, architecture, and overall system health, enabling early problem identification and performance optimization. This broad scope of observability helps teams maintain reliability and scalability across their digital assets, as noted by 86% of reviewers.

“We use Datadog to monitor our application using monitors and alerts, create dashboards to report system performance, and general tracking of logs and traces to make debugging easier.”

Why it matters:

Reviewers consistently praise Datadog's dashboards and visualization capabilities, with 79% highlighting them as a core strength. These features enable users to combine different data sources like logs and metrics into meaningful, real-time displays, significantly aiding in performance tracking and debugging. The platform provides powerful tools for creating custom dashboards that present critical data in an easily understandable and visually appealing manner.

“Dashboards and monitors make it easy to visualize application performance and track important metrics”

Why it matters:

The platform significantly improves the process of log analysis and debugging by centralizing log ingestion and providing advanced discovery capabilities. Reviewers find it essential for quickly tracing requests, analyzing logs, and correlating data to troubleshoot issues efficiently. This functionality is crucial for identifying root causes and resolving problems before they impact users, a benefit cited by 55% of reviewers.

“Datadog covers many use cases which includes generating key custom metrics for top-level analytics of our data, APM traces that tracks fine-grained microservices communication with continuous log aggregation and multiple test suites for our product.”

What other products like Datadog have you used or evaluated?

From 29 reviews

Summary

Reviewers frequently cite Grafana as an alternative or complementary tool to Datadog, with 38% of reviewers mentioning it. This indicates a strong preference for open-source visualization and dashboarding solutions within the observability space. New Relic is another significant competitor, mentioned by 28% of reviewers, suggesting its continued relevance as a comprehensive application performance monitoring (APM) and observability platform. Prometheus, often paired with Grafana, is also a prominent alternative, noted by 24% of the sample, highlighting the adoption of open-source monitoring systems for metrics collection. Commercial cloud-native monitoring solutions also feature, with Amazon CloudWatch and Dynatrace each cited by 14% of reviewers, indicating that specialized cloud provider tools and enterprise-grade observability platforms are also considered. The overall landscape of alternatives is diverse, encompassing both open-source projects and proprietary commercial offerings, often used in conjunction to address various monitoring and observability needs.

Top Quotes

Grafana

“Grafana and Sentry”

New Relic

“New Relic, AWS CloudTrail and Sentry”

Prometheus

“Prometheus and Grafana”

What positive or negative impact (i.e. Return on Investment or ROI) has Datadog had on your overall business objectives?

From 29 reviews

Summary

Datadog significantly contributes to business objectives primarily through enhancing operational efficiency and accelerating problem-solving. A substantial 45% of reviewers reported faster incident resolution, attributing this to reduced downtime, improved customer experience, and more efficient team operations. This is closely linked to improved issue resolution speed, noted by 24% of users, who found the platform instrumental in quickly identifying and fixing production issues. Furthermore, 28% of reviewers highlighted improved developer productivity, as engineers could more efficiently correlate data, debug, and find root causes. Datadog also aids in data-driven decision making, a benefit cited by 17% of reviewers, who leverage customizable dashboards and live metrics for better strategic choices. While these operational benefits suggest a strong positive ROI, cost management remains a mixed area, with 21% of reviewers acknowledging its potential for cloud cost savings through right-sizing resources, yet also noting challenges in keeping costs in check and the potential for high expenses.

Top Quotes

Faster Incident Resolution

“reduced the time in debugging and the team is more productive as RCA is easy to find”

Improved Developer Productivity

“Increased systems performance after surfacing pipeline-wide profiling data”

Issue Resolution Speed

“Positive: Been able to debug issues a lot easier”

Besides Datadog, what other software do you regularly use? How likely would you be to recommend it to a friend or colleague?

From 29 reviews

Summary

Reviewers frequently utilize a range of software tools in conjunction with Datadog, with observability and monitoring platforms being the most commonly cited. Grafana stands out as a prominent choice, mentioned by 31% of reviewers, often used for data visualization and dashboarding. Following closely, Prometheus was noted by 24% of respondents, typically serving as a monitoring and alerting system. Beyond specialized monitoring, communication platforms also play a significant role, with Slack being mentioned by 17% of reviewers for team collaboration. Additionally, cloud-native monitoring solutions like Amazon CloudWatch, cited by 10% of reviewers, and comprehensive application performance management (APM) tools such as New Relic, also mentioned by 10%, indicate a diverse ecosystem of tools supporting various operational needs. The integration of these tools suggests a preference for specialized solutions that complement each other to provide a holistic view of system health and team communication.

Top Quotes

Grafana

“Grafana, Retool, Slack”

Prometheus

“Elastic Observability, Grafana, Prometheus, Google Cloud Platform, Amazon Web Services”

Slack

“Cursor, GitHub, Slack”

Describe how you use Datadog in your organization. What are the business problems the product addresses and what is the scope of your use case?

From 29 reviews

Summary

Datadog is widely adopted by organizations primarily as a comprehensive platform for monitoring, observability, and application performance management. Reviewers frequently highlight its utility in gaining real-time insights into system health and performance, with 86% of reviewers citing its role in monitoring and observability. The platform addresses critical business problems by centralizing data for effective log analysis and debugging, a capability mentioned by 55% of users, and by providing robust Application Performance Monitoring (APM) tools to identify and resolve bottlenecks, noted by 52% of reviewers. Furthermore, Datadog is instrumental in proactive incident management through its alerting features, which 45% of reviewers emphasize for reducing downtime and improving response times. A significant advantage cited by 34% of users is the platform's ability to offer a centralized view and unified platform, consolidating various monitoring and analysis tools into a single interface, thereby streamlining operations and enhancing troubleshooting efficiency across diverse environments.

Top Quotes

Monitoring and Observability

“We use Datadog to monitor our application using monitors and alerts, create dashboards to report system performance, and general tracking of logs and traces to make debugging easier.”

Log Analysis and Debugging

APM and Performance Monitoring

“We use it for everything from monitoring system CPU and memory usage on our cloud instances to application-level error custom alerting.”

Please provide some detailed examples of areas where Datadog has room for improvement.

From 29 reviews

Summary

Reviewers frequently highlight several areas where Datadog could improve, primarily focusing on cost management, ease of use, and specific feature refinements. A significant concern, cited by 55% of reviewers, revolves around the platform's cost and pricing model, which many find opaque, unpredictable, and prone to rapid escalation. Relatedly, 31% of reviewers point to a steep learning curve and challenges with documentation, suggesting that the platform's complexity hinders efficient adoption and usage. Dashboard and visualization usability also emerged as an area for improvement, with 24% of reviewers noting difficulties in navigation, customization, and building complex visualizations. Furthermore, 17% of reviewers expressed dissatisfaction with alerting and notification issues, citing problems with alert fatigue, false alarms, and notification delays. Finally, log management and the log explorer were mentioned by 14% of reviewers as needing enhancements, particularly regarding handling high log volumes and improving search capabilities.

Top Quotes

Cost and Pricing Opacity

“Cost Transparency and Pricing”

Learning Curve and Documentation

“steep learning curve and complex UI”

Dashboard and Visualization Usability

“Dashboards can sometimes be disorganized if not maintained properly”

Please provide some detailed examples of things that Datadog does particularly well.

From 29 reviews

Summary

Datadog is widely recognized for its robust capabilities in monitoring and observability, with reviewers frequently highlighting its strengths in data visualization and management. A significant majority, 79% of reviewers, commend its dashboards and visualization features for providing real-time insights and facilitating the combination of various data types. Beyond visualization, Datadog excels in comprehensive data handling, particularly in log management and search, noted by 48% of reviewers, and its effective alerting mechanisms, mentioned by 45%. The platform's ability to offer powerful application performance monitoring (APM) and traceability is also a key strength, cited by 38% of the review sample. These core functionalities are further enhanced by Datadog's extensive and seamless integrations, which 28% of reviewers noted as crucial for a unified monitoring experience across diverse environments.

Top Quotes

Dashboards and Visualization

“Dashboards and monitors make it easy to visualize application performance and track important metrics”

Log Management and Search

“log management”

Alerting

“Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.”

Reviews

63 Reviews

Datadog user experience supporting a banking solution

Rating: 8 out of 10

Incentivized

January 28, 2026

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.

Pros

APM - particularly with Dynamic instrumentation helpful for trace analysis
Infrastructure Monitoring- for both on-premise - host map and OCP deployments from Kubernetes explorer
BIT AI SRE Agent - for Incident investigations, finding RCA

Cons

Support for traceability of mainframes application, applications/solutions on C/C++
Avoiding duplicating/non useful monitors and allow AI monitoring to decide, which should be mandatory monitors
Custom metrics usability to have similar kind of visualizations of various metrics

Likelihood to Recommend

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

Verified User

Engineer in Engineering (10,001+ employees)

Vetted Review

18 years of experience

Powerful multi-cloud observability with real cost and usability tradeoffs

Rating: 8 out of 10

Incentivized

January 14, 2026

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.

Pros

Free-form search across logs and fields (`*:query`)
Heavy-duty dashboard capabilities, handling a lot of data with relative speed
Thorough, solid integration via datadog-agent and the various plugins
Exceptionally quick/nice support

Cons

Alarms leave a lot to be desired. My team currently struggles with alarm fatigue because we couldn't find way to represent complex/sophisticated failure modes that would auto-recover, so we incur in noise. The composite alarm functionality is not enough.
Costs are frequently opaque, and deciding to adopt new features requires talking to our representative to estimate real impact
Some recurring, but fundamental issues have to be solved via workarounds, such as tags being delayed by cloud-providers and requiring workarounds at the risk of having gaps on dashboards. The agent should handle that automatically.

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.

Verified User

Engineer in Research & Development (51-200 employees)

Vetted Review

5 years of experience

Datadog is a Very Powerful and Comprehensive Performance Monitoring Platform.

Rating: 10 out of 10

December 18, 2025

Use Cases and Deployment Scope

We use Datadog as the main Observability and Appication Monitoring Tool in our organization.

It excel at our :

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.

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.

Pros

Monitoring apps and server health
Alerting
Built-in dashboard gives great visualization
Visibility into traces ,metrics ,logs all in one place
Observability needs and performance monitoring

Cons

For custom metrics it get costly
There is a learning curve when building complex queries or nested monitor ,this also require a training or expert help.

Likelihood to Recommend

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.

Has a wide integration with cloud services which makes it very flexible for monitoring.

Gilan Pharmacy

System Engineer in Engineering at ELSODENT (11-50 employees)

Vetted Review

6 years of experience

Robust Monitoring Tool with Powerful Insights.

Rating: 10 out of 10

Incentivized

December 18, 2025

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.

Pros

Applications ,log and portal monitoring
Powerful dashboards and visualization
Collecting logs and metrics
Smart alerts
Proactive monitoring
Incident management

Cons

I don't like how the pricing model seems to escalate fast with increasing metrics ingestion and monitoring across clouds which make it hard to predict the cost.
Dashboards are abit limited ,building highly customized visualization is not always seamless.

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.

Vipul Singh

Senior Software Engineer in Engineering at Adstello (11-50 employees)

Vetted Review

4 years of experience

An honest Datadog review

Rating: 9 out of 10

Incentivized

December 18, 2025

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.

Pros

Cloud (Infrastructure) Monitoring
Synthetic tests (Multi Location Tests)
APM (Application Performance Monitoring)
SLIs/SLOs track

Cons

Log Management (cost related)
APM (cost related)
Security (SIEM)

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!

Verified User

Director in Information Technology (10,001+ employees)

Vetted Review

3 years of experience

Datadog has got it all

Rating: 9 out of 10

Incentivized

December 18, 2025

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.

Pros

Outlines and details monitored metric alerts
Outlines and details recovered alerts to provide detailed visuals to numerical data
Scans for vulnerabilities, misconfigurations, and compliance issues within the company

Cons

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.

Verified User

Analyst in Corporate (5001-10,000 employees)

Vetted Review

6 years of experience

Datadog Review

Rating: 8 out of 10

Incentivized

December 18, 2025

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.

Pros

Datadog does a good job of organizing logs and has many filters to search through them. I use it most often to investigate issues.
Datadog has alert "monitors" that can be setup to automatically notify teams of certain issues
Datadog has dashboards that can be setup to monitor resource usage

Cons

The log explorer could be improved by loading larger amounts of logs at a time without requiring scrolling. This would also make it easier to quickly Ctrl+F find certain keywords over the existing results.
The log explorer could be improved by handling even larger logs. There are some logs that get broken into different logs due to the size.
The Watchdog AI feature can still be improved since it mostly raises anomalous logs that are not interesting issues.

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.

Verified User

Engineer in Engineering (11-50 employees)

Vetted Review

5 years of experience

Datadog seemed to be everything we hoped for but ultimately let us down

Rating: 3 out of 10

Incentivized

December 18, 2025

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)

Pros

Datadog agent was really good at analyzing performance
UI allowed you to cross reference traces, logs, errors, metrics, etc
Customizable

Cons

In my experience, .NET Tracing Agent caused severe and untraceable performance issues
In my opinion, usage and billing structures were opaque and surprising
In my experience, documentation was incomplete, contradicting or sometimes completely wrong, even for common infrastructure (AWS Fargate)
I feel support was unhelpful at times, and bounced us back and forth to other teams
In my opinion, multiple methods of sample rate control were ineffective, adding to excessive usage and cost

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.

Verified User

Engineer in Engineering (11-50 employees)

Vetted Review

1 year of experience

Steep learning curve but totally worth it

Rating: 7 out of 10

Incentivized

December 6, 2025

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.

Pros

Setting up tracing is incredibly easy and powerful
Log search, especially with subqueries, makes it possible to find a needle in a haystack
Dashboards make it easy to compare data across dimensions

Cons

Building dashboards is often painful - the query syntax, especially for APM, is challenging to navigate. This feels like somewhere where an LLM integration would be incredibly helpful
Specifically, the lack of wildcard search for APM resources makes it hard to gather or view data across a group of related endpoints
The query helper is often too eager to help, opening dropdowns when I don't want them and inserting extra query filters where they aren't wanted or needed.

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.

Matt Steen

Staff Software Engineer in Engineering at Aurora Solar (201-500 employees)

Vetted Review

4 years of experience

View profile

Datadog is a fundamentally useful platform for centralized app observabilty and beyond

Rating: 8 out of 10

Incentivized

December 4, 2025

Use Cases and Deployment Scope

Datadog is our first point of access for developers to review logs and monitoring of key services and architecture. Primarily, we were drowning in trying to find useful logs in AWS Cloudwatch and Datadog's log discovery capabilities are far and away better. We have setup up several key alarms for bad log patterns but have yet to find full utility in monitoring other metrics - largely because we do not have a core platform development team.

Pros

Log indexing
Log Searching
Dashboard building (combining logs and metrics)
Traceability
User Monitoring

Cons

More recipes for fundamental monitoring tooling
Targetting different scales of application (beyond enterprise SaaS)
Multiple workspaces in an account to separate users

Likelihood to Recommend

Datadog can be pricey for larger scale businesses, so it really depends on your use case. For us, we have a small single deployment application and a small developer team, so our costs are mostly reasonable. There are more features than we can explore which can be somewhat overwhelming. It is mostly easy and intuitive to use but for larger scale you may consider rolling your own solutions.

Jeffrey van Santen

Sr SWE in Engineering at Unnatural Products Inc (11-50 employees)

Vetted Review

2 years of experience

View profile