Datadog Reviews and Ratings

Rating: 8.6 out of 10

Score

8.6 out of 10

Community insights

TrustRadius Insights for Datadog are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Easy customization and understanding: Many users have found the dashboard in Datadog to be easy to customize and understand for their organization's needs, allowing them to effectively consume various site metrics.

Strong integration support: Several reviewers appreciate the strong integration support of Datadog, mentioning that it supports pretty much any service they can think of. This flexibility allows them to seamlessly integrate with other tools and services in their workflow.

Helpful customer support: Users have mentioned that Datadog's customer support is helpful and responsive. They appreciate the assistance provided by the support team in finding workarounds for any issues they encounter during their usage of the platform.

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

Incentivized

December 18, 2025

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>

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

<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>

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

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Reviews and Ratings

Powerful multi-cloud observability with real cost and usability tradeoffs

Rating: 8 out of 10

Incentivized

January 14, 2026

Use Cases and Deployment Scope

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.

Verified User

Engineer in Research & Development (51-200 employees)

Vetted Review

5 years of experience