TrustRadius: an HG Insights company

Datadog

Score8.8 out of 10

357 Reviews and Ratings

Try It For Free

What is Datadog?

Datadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.

Read more details.

Categories & Use Cases

Media

Screenshot of the out-of-the-box and customizable monitoring dashboards.
Screenshot of Datadog's collaboration features, where users can discuss issues in-context with production data, annotate changes and notify their teams, see who responded to that alert before, and discover what was done to fix it.
Screenshot of where Datadog unifies traces, metrics, and logs—the three pillars of observability.
Screenshot of some of Datadog's 400+ built-in integrations.
Screenshot of Datadog's Service Map, which decomposes an application into all its component services and draws the observed dependencies between these services in real time
Screenshot of centralized log data, pulled from any source.
Screenshot of Datadog's Host Map, which lets users see all hosts together on one screen, grouped and filtered as desired, with metrics made instantly comprehensible via color and shape.

1 / 7

Screenshot of the out-of-the-box and customizable monitoring dashboards.

Who Buys & Uses Datadog

Datadog Reviews

What users are saying about Datadog.
View all Datadog reviews

Pros

  • Powerful data visualization and customizable dashboards for comprehensive insights
  • Effective log management and search capabilities for efficient analysis
  • Intelligent and easily configurable alerting mechanisms

Cons

  • Opaque and potentially high pricing model, especially with increased data ingestion
  • Steep learning curve due to complex query syntax and extensive features
  • Challenges with dashboard customization and navigating the user interface

Excellent Tool in the Modern Observability Toolbox

Incentivized
Verified User
Strategist in Product Management (51-200 employees employees)

Use Cases and Deployment Scope

We use Datadog to monitor so many different tools across our whole tech stack. For my use case we have integrations with so many different platforms and Datadog helps us to gain visibility into failures to help with troubleshooting and keep a pulse on overall performance. I use the bits tool at times to use natural language to get a summary of data trends. Often we will look at the payloads, export the logs to csv for further analysis. But lately I've found that bits does a good job of summarizing so I don't have to export as much.

Pros

  • Surfaces failures in my DMS/ERP integrations
  • Summarizes issues with the bits AI tool
  • Helps me to see exactly what payload values are problematic

Cons

  • I know there are ways to set up alerts when something passes a threshold but I can't seem to figure it out

Return on Investment

  • It has had a huge impact in helping increase CSAT by surfacing issues before they become problems. We can ofter get ahead of issues before things get heated.

Usability

Other Software Used

Atlassian Jira, Looker Studio, Amazon Athena

Datadog user experience supporting a banking solution

Incentivized
Verified User
Engineer in Engineering (10,001+ employees employees)

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

Return on Investment

  • Incident response time drastically decreased
  • Reporting is made easy with the dashboards and single places for all important widgets
  • Though with some false positives anomaly detection monitoring was helpful

Usability

Alternatives Considered

Splunk AppDynamics, Prometheus and Grafana

Other Software Used

Finacle Core Banking, Red Hat JBoss Enterprise Application Platform, Red Hat Ansible Automation Platform

Powerful multi-cloud observability with real cost and usability tradeoffs

Incentivized
Verified User
Engineer in Research & Development (51-200 employees employees)

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.

Return on Investment

  • Shorter MTTD
  • Increased % of support queue fulfilled, given the access to historical data via rehydration
  • Increased systems performance after surfacing pipeline-wide profiling data

Usability

Alternatives Considered

Amazon CloudWatch

Other Software Used

AWS CloudFormation, CDK, Amazon Elastic Compute Cloud (EC2), Amazon S3 (Simple Storage Service)

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

Incentivized
Jeffrey van SantenView profile
Sr SWE in Engineering at Unnatural Products Inc (11-50 employees employees)

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

Return on Investment

  • Highly improved distributed compute debugging
  • Still waiting for Bits AI access - negative experience on new product rollouts
  • Spreadsheets have made reporting to external teams significantly easier

Usability

Alternatives Considered

ClickHouse, Grafana and Sentry

Other Software Used

AWS CloudTrail, Amazon CloudWatch, Google Workspace

Steep learning curve but totally worth it

Incentivized
Matt SteenView profile
Staff Software Engineer in Engineering at Aurora Solar (201-500 employees employees)

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.

Return on Investment

  • By using monitors for new errors, we've reduced the errors to near zero for a growing consumer-facing application. A meaningful percentage of visits were previously ending in failure, but we've been able to work through the errors with the increased visibility
  • Custom metrics have allowed us to provide excellent customer service to our business customers by analyzing usage patterns over time and helping them use our application more effectively. They really appreciate when we proactively reach out to them about ways to improve their success rate

Usability

Datadog Alternatives

Products similar to Datadog that may also meet your needs.
View all Datadog alternatives
New Relic
CompareLearn more
Splunk Cloud Platform
CompareLearn more
Dynatrace
CompareLearn more