Datadog Reviews & Product Details

Datadog is the monitoring, security and analytics platform for developers, IT operations teams, security engineers and business users in the cloud age. The SaaS platform integrates and automates infrastructure monitoring, application performance monitoring and log management to provide unified, real-time observability of our customers' entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics.

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Datadog

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English, French, Portuguese, Spanish

Solution Type

All-in-One

Overview by

Camille Chang

Used by DataArt and 2 others

Beta

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Pricing provided by Datadog.

Free

Per host, per month

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Datadog Integrations

(31)

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What's included in Datadog

CI VisibilityDatadog CI Visibility is a comprehensive observability and monitoring product purpose-built for continuous integration (CI) pipelines and test suites. It enables platform engineering, DevOps, and development teams to gain deep, end-to-end visibility into the health and performance of their CI/CD workflows—spanning every pipeline, stage, job, and test execution—all from a single unified platform. At its core, CI Visibility is composed of two complementary capabilities: Pipeline Visibility and Testing Visibility. Pipeline Visibility allows teams to monitor all CI pipelines across multiple providers in a single pane of glass, tracking key performance indicators such as execution time, failure rate, and queue time. Teams can visualize pipeline executions using flame graphs that break down each stage and job, making it straightforward to identify bottlenecks, slow jobs, and recurring failures. Out-of-the-box dashboards surface top failed pipelines, duration trends, and error rates, while granular filtering by branch, status, commit, and duration helps teams zero in on the root cause of issues quickly. Testing Visibility provides deep insight into test suite performance across all builds. CI Visibility automatically instruments tests end-to-end, enabling teams to trace individual test runs, surface common errors, and visualize performance trends over time. A key capability is automatic flaky test detection: Datadog identifies tests that variably pass and fail for the same commit, tracks when new flaky tests are introduced, and surfaces the specific commit responsible—enabling developers to debug and resolve test reliability issues before they impact more developers or degrade the default branch. CI Visibility also includes Test Impact Analysis (formerly Intelligent Test Runner), which intelligently selects only the tests relevant to each code change, reducing unnecessary test execution, shortening feedback cycles, and minimizing CI resource consumption. Teams can visualize time savings across services, repositories, commits, and authors in a dedicated dashboard. The product integrates with Datadog's broader observability platform, allowing teams to correlate pipeline and test data with infrastructure metrics, logs, network performance data, and Real User Monitoring (RUM). For example, Cypress end-to-end test results in CI Visibility can be linked directly to RUM browser sessions and Session Replay, enabling teams to investigate flaky or slow tests with full browser-level context. CI Visibility supports proactive alerting through CI Monitors, which notify teams when pipelines break, failure rates spike, or performance degrades—routing alerts with full pipeline context to the relevant teams. Teams can also scope data by repository, branch, or commit to identify trends and troubleshoot issues efficiently. By shifting observability left into the pre-production development lifecycle, Datadog CI Visibility helps engineering organizations accelerate software delivery, improve developer experience, reduce CI/CD costs, and maintain more efficient, reliable pipelines—all without leaving the Datadog platform.

Database MonitoringDatadog Database Monitoring (DBM) is a dedicated observability solution designed to help application developers, database administrators, and platform teams troubleshoot and optimize slow, inefficient queries across database environments — without requiring deep database expertise. DBM unifies database, application, and infrastructure telemetry in a single platform, giving teams full visibility into database load, long-running queries, and blocking queries. With AI-powered root cause analysis and automated query optimization recommendations, DBM surfaces the exact cause of performance issues and recommends actionable fixes, enabling teams to resolve problems faster and prevent spiraling database costs. **Troubleshoot Database Host and Query Inefficiencies** DBM enables teams to identify what resources queries are waiting on and which users, queries, services, and applications are driving load on database hosts. Teams can triage database issues using wait event load analysis and host insights to quickly identify areas of underperformance. Database table locks can be resolved by visually identifying root blocking queries and impacted waiting queries. An AI-powered root cause analysis agent automatically diagnoses query latency regressions, pinpoints what broke, why it happened, and the steps to remediate. **Proactively Optimize Query Performance** DBM provides automated recommendations with in-context remediation steps for underperforming queries — including those with long-running transactions, high-impact blockers, or missing index recommendations. Teams can track performance trends by monitoring key query metrics such as average latency, total execution time, and number of rows queried over time. Optimization suggestions include query rewrites, schema updates, and index changes. **Unified Database and Application Performance** DBM accelerates root cause analysis of high latency by correlating distributed traces with query metrics and node-level explain plans in a single view. Teams can understand how database bottlenecks impact calling services and vice versa. Cross-stack optimization recommendations are delivered directly through APM Recommendations, driven by DBM, Continuous Profiler, and application insights. **Strengthen Database Observability Across Teams** DBM includes out-of-the-box monitors and SLO tracking to maintain high database reliability and quickly catch and respond to issues. Customizable dashboards allow teams to track database trends alongside metrics, traces, and logs from across the stack. Shared database host and query visibility breaks organizational silos between development, operations, and database teams. **Historical Query Performance and Explain Plans** DBM stores historical query performance metrics at full granularity for up to three months, enabling long-term trend analysis and regression prevention. Detailed explain plans are available for each normalized query, listing the exact steps taken by the query planner along with estimated costs, helping surface inefficient operations like sequential scans. **Broad Database Support** DBM supports the five most popular database types — PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB — across self-hosted and fully managed deployments, including Amazon RDS, Amazon Aurora, Google Cloud SQL, Azure SQL, and Supabase.

Datadog APMDatadog APM (Application Performance Monitoring) is a cloud-scale observability solution that provides AI-powered, code-level distributed tracing from browser and mobile applications all the way through to backend services and databases. It is designed to help engineering teams detect, diagnose, and resolve performance issues faster by unifying traces, metrics, logs, real user monitoring (RUM) data, and security signals into a single, correlated view. At its core, Datadog APM delivers end-to-end distributed tracing across every service, host, container, and database involved in a request — even in the most complex microservice architectures. Traces are visualized as flame graphs that show which services, endpoints, calls, and queries were involved in serving a request, along with the latency associated with each operation. This thread-level visibility enables teams to pinpoint the exact method, code line, or thread responsible for a performance degradation or error. Datadog APM's Service Catalog (formerly Software Catalog) provides a centralized view of all services, including health metrics, dependencies, team ownership, on-call schedules, and runbooks — making it easy to remediate incidents efficiently. The Service Map automatically decomposes applications into their component microservices and draws observed dependencies in real time, helping teams identify bottlenecks and understand how requests flow through their architecture. Deployment Tracking allows teams to correlate application performance issues with code deployments, feature flags, configuration changes, and database modifications. Teams can compare error rates, latency, infrastructure metrics, and code profiling data across versions — before, during, and after a release — and leverage Watchdog AI for automatic detection of faulty deployments. Watchdog, Datadog's embedded AI/ML engine, automatically detects anomalies, outliers, and future performance degradations without requiring manual alert configuration. It provides automated root cause analysis to improve Mean Time to Resolution (MTTR) and surfaces error and latency outliers at query time to help teams find the signal in the noise. Datadog APM supports flexible, fine-grained ingestion controls, allowing teams to balance visibility and cost with sampling controls at the host, service, and endpoint level — all managed remotely from the Datadog UI. It supports both native and hybrid OpenTelemetry setups, enabling teams to adopt open standards without trade-offs. Distributed tracing can be set up directly via the Datadog Agent without code changes or restarts. For serverless environments, Datadog APM auto-instruments AWS Lambda functions, Azure App Service, and more via next-generation extensions. Lambda functions and AWS managed services are automatically connected in a single trace, providing end-to-end visibility into event-driven architectures. APM also integrates with Datadog's security capabilities through APM Security View, which surfaces security signals, vulnerability data, and attack information directly within the Service Catalog and distributed tracing views — powered by Datadog App and API Protection (AAP). Datadog APM supports a wide range of languages including Java, Python, Ruby, Go, Node.js, .NET, and PHP, with auto-instrumentation for popular web frameworks and data stores. It is available in three tiers — APM, APM Pro (which adds Data Streams Monitoring), and APM Enterprise (which adds Continuous Profiler for code-level CPU, memory, lock, and I/O profiling).

Infrastructure MonitoringDatadog Infrastructure Monitoring is a SaaS-based monitoring solution that provides comprehensive metrics, visualizations, and alerting to help engineering teams maintain, optimize, and secure cloud, hybrid, and on-premises environments. Designed for organizations of all sizes, it delivers full-stack visibility into the health and performance of servers, virtual machines, containers, databases, and other backend infrastructure components — all from a single, unified platform. At its core, Datadog Infrastructure Monitoring deploys a lightweight, open-source Datadog Agent that collects metrics from hosts and containers at 15-second granularity. This agent-based approach requires no professional services or extensive training to deploy, making it accessible to teams without deep monitoring expertise. The intuitive user interface requires no query language, enabling adoption across engineering, operations, and security teams alike. The product offers complete coverage of any technology stack. Teams can deploy it across on-premises, hybrid, edge, and multi-cloud environments, and benefit from vendor-backed integrations for Kubernetes, serverless platforms, and over 900 other popular technologies. Tag-based search and analytics allow teams to slice and dice complex infrastructure data to create fine-grained alerts or focus investigations on specific subsets of their environment. Datadog Infrastructure Monitoring tracks tens of thousands of infrastructure metrics out-of-the-box, including CPU utilization, memory usage, network throughput, and disk performance. It maintains continuous historical records — even for infrastructure that no longer exists — enabling retrospective analysis and capacity planning. One-click correlation of related metrics, traces, logs, and security signals from across the stack accelerates troubleshooting and reduces mean time to resolution (MTTR). AIOps capabilities are built into the platform to reduce alert fatigue. Machine learning-based monitors evaluate historical trends and automatically surface issues from the noise, ensuring engineers are only alerted when systems are genuinely in trouble. Anomaly detection, forecasting, and automated event correlation help teams proactively address problems before they impact users. The product also includes robust cloud security features. Teams can assess and enhance their security posture with continuous configuration checks across cloud accounts, hosts, and containers. Out-of-the-box support for over 15 compliance frameworks — including PCI DSS, SOC 2, HIPAA, GDPR, and CIS — helps organizations track and fulfill audit requirements. Vulnerabilities are automatically detected and prioritized based on potential business impact, deep observability context, and Datadog's curated security research. A comprehensive resource inventory visualizes cloud resources and their associated security risks at a glance. Custom metrics support allows teams to supplement out-of-the-box infrastructure metrics with business-level data such as revenue or customer behavior. Distribution metrics enable globally accurate percentile calculations across the entire infrastructure. The Host Map feature provides a visual overview of the health and resource utilization of all hosts, making it easy to identify outliers and performance bottlenecks at a glance. Datadog Infrastructure Monitoring integrates natively with Datadog's broader observability platform — including Application Performance Monitoring (APM), Log Management, Network Monitoring, Container Monitoring, and Cloud Security — giving teams a single source of truth for understanding and resolving performance issues across any layer of their stack.

Log ManagementDatadog Log Management is an AI-powered, cloud-scale log management and analytics platform that enables organizations to collect, monitor, manage, and analyze large volumes of logs while unifying metrics and traces in a single platform. Designed for teams of all sizes and technology stacks, it provides complete visibility across the entire application and infrastructure stack to accelerate troubleshooting, optimize performance, and defend against security threats. At its core, Datadog Log Management is built around the "Logging without Limits™" philosophy, which decouples log ingestion from indexing. This allows organizations to cost-effectively ingest and process 100% of their logs, while selectively retaining and indexing only the logs they need — eliminating the trade-off between visibility and cost. Logs can be enriched, parsed, and archived automatically, with on-demand rehydration available for historical analysis. The platform offers a centralized processing and routing control panel where teams can build structured datasets from raw log data sourced from any system. Logs can be enriched with custom reference data for added context, and forwarded securely to external destinations such as data lakes, Splunk, Elasticsearch, or SIEM vendors using Log Forwarding. For storage and retention, Datadog Log Management provides multiple tiered options: Standard Indexing for frequently queried short-term logs, Flex Logs for high-volume long-term retention (up to 15 months) with independently scalable compute and storage, Flex Frozen for extended retention of up to seven years for compliance and audit-heavy environments, and Archive Search for querying logs directly from customer-owned cold storage without re-indexing. A CloudPrem (BYOC) option also allows enterprises to deploy Datadog's indexing and search capabilities within their own infrastructure to meet data residency requirements. Log analysis is made accessible through the Log Explorer — a powerful yet intuitive interface that supports faceted search, full-text search, drag-and-drop dashboards, and log visualization as tables and charts. Log Patterns automatically groups similar log messages to surface trends, while Log Transaction Queries aggregate logs across services to provide end-to-end process visibility. Watchdog Insights and Bits AI provide automated anomaly detection, root-cause analysis, and AI-powered recommendations without manual configuration. Security is a first-class capability within Datadog Log Management. With 800+ out-of-the-box detection rules aligned to the MITRE ATT&CK® framework, teams can automate threat detection and response through a built-in AI-powered SIEM. Granular role-based access controls (RBAC) allow organizations to manage log access by team structure and role, while automatic compliance support for PCI and HIPAA standards reduces audit overhead. Datadog Log Management integrates with 1,000+ technologies and services, supports log collection via the Datadog Agent, log shippers, or API endpoints, and scales seamlessly to handle millions of logs per minute or petabytes per month. It is recognized by G2 for both Log Analysis and Log Monitoring.

Network Performance MonitoringDatadog Network Performance Monitoring (NPM) — now also referred to as Cloud Network Monitoring (CNM) — is a comprehensive network observability solution designed to give engineering and operations teams deep, real-time visibility into network traffic, dependencies, and performance across modern cloud-native, hybrid, and on-premises environments. At its core, NPM automatically analyzes network traffic between application components and provides rich visualization of interdependencies and application topology. It gives engineering teams critical visibility into network connections and data flows across hosts, containers, and services in complex, cloud-scale environments — eliminating the need for manual effort to understand service interdependencies. NPM delivers multi-cloud visibility into network flows in granular detail, enabling teams to aggregate and monitor data using any tag available in Datadog. Users can query and aggregate connection metrics between any two objects — from services to availability zones, or from Kubernetes pods to security groups — providing immediate insight into performance and dependencies. The product automatically collects relevant tags from more than 1,000 integrations, in addition to custom tags provided by developer instrumentation. Built on eBPF (Extended Berkeley Packet Filter), Datadog NPM provides detailed visibility into network flows with extremely low overhead, making it possible to achieve unprecedented network connection visibility in any environment without significant performance trade-offs. For Windows hosts, a custom driver is used to deliver the same network-level visibility with minimal overhead. Key capabilities include: - **Traffic Flow Analysis & Network Map**: Visualize network topology and traffic flows on a directed graph, scoped by service, Kubernetes deployment, Docker image, Chef role, AWS security group, or any other tag-based lens. - **DNS Monitoring**: Assess DNS server health with request volume, response time, and error code metrics, including SERVFAIL and NXDOMAIN error tracking. - **TCP Performance Metrics**: Track TCP retransmits, latency, established/closed connections, and connection churn to pinpoint sources of latency and outages. - **Network Path (Hop-by-Hop Tracing)**: Visualize the journey of packets hop by hop from source to destination using traceroutes (TCP, UDP, or ICMP), highlighting latency spikes and packet loss at each step. - **Cloud Service Autodetection**: Automatically detect AWS services (e.g., S3, RDS, Route 53) within the network and observe communication between internal and cloud-managed services. - **Cross-AZ & Cross-Region Traffic Visibility**: Track data flows between availability zones and data centers to optimize performance and reduce transport costs. - **Full-Stack Correlation**: Correlate network data with logs, distributed traces (APM), infrastructure metrics, and process-level data — all within a single pane of glass. - **Out-of-the-Box Dashboards**: Access pre-built CNM Overview dashboards covering network load, TCP traffic performance, DNS health, application overview, and cross-regional traffic. - **Saved Views**: Quickly access preset and custom queries to scope monitoring to specific services, pods, or cloud resources without rewriting queries. - **Network Device Monitoring (NDM) Integration**: Combine flow-level data with SNMP-based device metrics for end-to-end visibility from physical hardware to cloud services. - **Machine Learning–Based Alerting**: Proactively monitor network devices with ML-powered alerts, forecast key metrics, and get alerted before problems arise. NPM is fully integrated with the broader Datadog platform, enabling seamless pivoting between network data, APM traces, logs, Live Processes, Synthetic Monitoring, and Real User Monitoring (RUM). This unified approach eliminates tool sprawl and consolidates all network monitoring data in one centralized place, making it easier to identify root causes and remediate incidents faster across any environment.

Real User Monitoring (RUM)Datadog Real User Monitoring (RUM) provides full visibility into every user session across web and mobile applications, helping engineering and SRE teams detect, investigate, and troubleshoot frontend performance issues. By correlating real user behavior with logs, traces, and backend metrics in a single platform, RUM enables teams to resolve errors faster, optimize application performance, and deliver better digital experiences. **Error Detection & Troubleshooting** RUM automatically surfaces critical issues by grouping high-volume errors into actionable insights. It pinpoints the root cause of JavaScript errors down to the exact line of code, identifies affected user segments, and prioritizes fixes based on business impact. Error Tracking automatically groups related errors into issues with full stack traces and session context, enabling faster triage and resolution. **Unified Full-Stack Telemetry** RUM unifies frontend and backend monitoring to eliminate silos between teams. It seamlessly pivots from RUM data to backend traces and logs to accelerate root cause analysis, and integrates with Datadog APM so that distributed traces are automatically linked to real-user sessions. Teams can also simulate user journeys with Synthetic Monitoring and correlate results with real user sessions for a complete picture of application health. **Browser & Mobile Performance** For web applications, RUM measures key performance metrics including Core Web Vitals (Largest Contentful Paint, Interaction to Next Paint, Cumulative Layout Shift), First Contentful Paint, and overall loading time. For mobile apps on Android, iOS, React Native, Flutter, Roku, and more, RUM tracks crash-free sessions, fatal crashes, Time to Initial Display (TTID), Time to Full Display (TTFD), ANR rates, and other Mobile Vitals. Mobile application launch monitoring captures cold, warm, and prewarmed startup performance with industry-standard metrics. **Session Replay & Heatmaps** RUM recreates real user sessions with pixel-perfect accuracy using Session Replay, enabling teams to diagnose issues and identify hidden friction points such as rage clicks. Heatmaps — including Click Maps and Scroll Maps — organize sessions into visualizations that reveal where users focus their attention and which areas they may be avoiding due to performance issues. **Browser Profiling** RUM connects degraded performance signals to the exact JavaScript functions responsible for slowdowns using production profiling data captured from real user sessions. Aggregated profiling views surface the most CPU-consuming functions by page, route, or performance metric, and profiling snapshots can be compared side-by-side across application versions or deployment stages. **RUM Without Limits™** RUM Without Limits™ decouples sampling from ingestion, enabling teams to capture 100% of user sessions while selectively retaining the most valuable data — prioritizing errors, key user segments, and critical workflows. Over 30 out-of-the-box performance metrics are retained for 15 months to support long-term trend analysis and historical investigations. **Custom Data & Metrics** Teams can enrich session data with custom user IDs, capture business-critical user actions (such as checkout clicks), and ingest custom metrics correlated with real-time performance data. RUM-based metrics can be used to build dashboards, monitors, and SLOs for tracking historical trends in user experience and application health. **Platform & Framework Support** Datadog RUM supports browser applications as well as mobile platforms including Android, iOS, React Native, Flutter, Kotlin Multiplatform, .NET MAUI, Unity, watchOS, and visionOS, making it suitable for teams building across diverse technology stacks.

Security PlatformDatadog Security Platform is a unified, cloud-native security solution built directly on top of Datadog's industry-leading observability platform. It is designed to help DevOps, security, and engineering teams prevent exposure, detect threats, and respond to incidents—all from a single pane of glass—without the need to deploy or maintain multiple disconnected point solutions. At its core, the Security Platform correlates real-time observability telemetry (metrics, logs, traces, APM data, and service dependencies) with security signals across an organization's entire technology stack, including infrastructure, applications, cloud accounts, identities, APIs, and AI workloads. This shared data model enables security and engineering teams to access a common source of truth, dramatically reducing investigation time—by up to 93%—and accelerating mean time to remediation. The platform is organized into four primary layers of protection: **Code Security** helps development and security teams identify and fix vulnerabilities from the earliest stages of the software development lifecycle through to production. It includes Static Code Analysis (SAST), Software Composition Analysis (SCA) for open-source library risks, Runtime Code Analysis (IAST) with 100% OWASP Benchmark accuracy, Infrastructure-as-Code (IaC) Security, and Secret Scanning. Bits Code, an AI-native capability, generates code fixes directly and supports bulk remediation campaigns. **Cloud Security** uses agentless technology to scan an organization's entire cloud infrastructure in minutes for vulnerabilities, misconfigurations, identity risks, and compliance violations. Key sub-capabilities include Cloud Security Posture Management (CSPM) for continuous compliance against frameworks such as CIS, PCI DSS, SOC 2, and HIPAA; Cloud Infrastructure Entitlement Management (CIEM) to reduce excessive IAM permissions; Vulnerability Management for containers and hosts; App and API Protection (AAP) to detect and block OWASP Top 10 threats across APIs and microservices; and Cloud Workload Protection for real-time, in-kernel monitoring of host and container activity. **Cloud SIEM** is an AI-driven threat detection and incident response platform built on Datadog's advanced log management infrastructure. It provides 900+ out-of-the-box detection rules mapped to the MITRE ATT&CK® framework, real-time log ingestion and analysis at any volume, risk-based entity analytics, and built-in SOAR workflow automation with case management. The Bits AI Security Analyst autonomously triages SIEM signals, performs in-depth investigations, and delivers actionable recommendations to reduce alert fatigue. **Sensitive Data Scanner (SDS)** prevents sensitive data such as personally identifiable information (PII) from leaking into logs, LLM training datasets, or cloud storage resources like Amazon S3 and RDS. The platform also includes AI Guard for runtime protection of AI agents and LLM workloads, a Runtime Prioritization Engine that uses live APM traces to assess whether vulnerabilities are truly business-critical, and a Supply Chain Firewall (SCFW) that blocks malicious open-source packages before installation. Dynamic routing for security notifications, bidirectional Jira ticketing, and FedRAMP High-certified observability for regulated industries round out the governance and compliance capabilities. With 1,000+ vendor-backed integrations, Datadog Security Platform provides immediate time-to-value for organizations of all sizes seeking to unify their security and observability practices under a modern DevSecOps model.

SyntheticsDatadog Synthetics is a proactive monitoring solution built on the Datadog unified observability platform that enables engineering and reliability teams to simulate user traffic and requests across applications, APIs, and network layers — without waiting for real users to encounter issues first. At its core, Datadog Synthetics allows teams to create code-free API tests, browser tests, mobile app tests, and network path tests that continuously run from globally managed locations or self-hosted private locations. By monitoring applications, API endpoints, and key business flows via simulated user requests, Synthetics helps organizations ensure uptime, identify regional issues, track application performance, and manage SLAs and SLOs. **API Tests** validate all layers of a system — HTTP, gRPC, SSL, DNS, WebSocket, TCP, UDP, and ICMP — from both global managed and private locations. Single-step API tests monitor response latency, HTTP status codes, and response headers, while multistep API tests chain together multiple HTTP requests to verify critical service workflows end to end. Network timing breakdowns accelerate root cause analysis. **Browser Tests** provide end-to-end visibility across every step of key application workflows, such as user sign-up, login, or checkout flows. Tests are recorded using a fully hosted, codeless web recorder — no specialized framework knowledge or coding skills required. AI-powered self-maintaining browser tests automatically detect UI changes and update test definitions accordingly, reducing false alarms and minimizing maintenance overhead. Each test run generates step-by-step screenshots and waterfall visualizations so teams can see exactly what users experience. **Mobile App Tests** allow teams to proactively monitor key business flows on real Android and iOS devices, using a codeless recorder and an Element Inspector for precise UI element selection. **Network Path Tests** provide hop-by-hop visibility into network degradations impacting application performance, with alerts on packet loss and latency from global or private locations. **Private Locations** enable Synthetics to monitor internal, firewalled, or geographically specific applications that are not publicly accessible. Private location workers can be deployed via Docker containers, Windows MSI packages, Kubernetes, Amazon ECS, AWS Fargate, or Amazon EKS, and support HTTP/S, DNS, TCP, UDP, ICMP, gRPC, and WebSocket protocols. **CI/CD Testing** integrates Synthetic tests directly into CI/CD pipelines for shift-left testing. Tests run simultaneously rather than sequentially to avoid slowing down development, and can automatically block deployments when critical tests fail. Integrations are available for GitHub Actions, GitLab, Jenkins, CircleCI, and Azure DevOps. Synthetics is tightly integrated with the broader Datadog platform. When a test fails, correlated metrics, distributed traces, logs, and session replays from Datadog APM and RUM are automatically surfaced — enabling full-stack troubleshooting without switching tools. Teams can visualize uptime and performance data alongside key business metrics on Datadog dashboards, track SLAs and SLOs with drag-and-drop widgets, and route alerts to collaboration tools like Slack, PagerDuty, and ServiceNow. A unified Synthetic Monitoring landing page consolidates test health, coverage gaps, and system signals — including Core Web Vitals, SSL certificate expiration, DNS response times, and backend service latency — into a single actionable view. AI analysis of failed test runs helps teams understand issues faster and act before they reach end users.

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Pavan kumar S.

Cloud Engineer

Small-Business (50 or fewer emp.)

11/26/2025

"Effortless Monitoring Across Platforms with Datadog"

4.5/5

What do you like best about Datadog?

A lot to consider about Datadog: mainly, it is used to monitor a wide range of applications and infrastructure. Application monitoring for container-based, server-based, and web-based applications. Also, it reduces the human effort for checking the exact error.

It is easy to use and supports different kinds of platforms like AWS, Azure, and GCP. Installation is very easy. Datadog provides all the required documents for integrating the agent. We can use the predefined dashboard templates many times, which is a lifesaver if we have repeated monitoring services.

Large-scale monitoring services and tracking are very easy, and the UI is clean, and we can find every service and access the running process via UI as well.! Review collected by and hosted on G2.com.

What do you dislike about Datadog?

There are only a few things that I was not satisfied with Datadog, those are. Cost is somewhat high; we have to pay even we utilize the resources or not.

And the main con is the customer support; they only deal via email, and we have to dump the log data into their storage. They first analyse the logs and come back to us; it will typically take 24-36 hours, which has an impact on the clients. Also, they only come on a call on production issues. Review collected by and hosted on G2.com.

Aditya M.

Information Technology Operations Manager

Information Technology and Services

Mid-Market (51-1000 emp.)

10/5/2026

"Helpful monitoring without the hassle"

4.5/5

What do you like best about Datadog?

It’s really helpful when we have a slow application or an issue that’s hard to track down. I can quickly see which service or request is causing the problem instead of going through different logs manually. It makes troubleshooting a lot quicker, especially when we’re dealing with issues across multiple services. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

One limitation I’ve noticed with Datadog is the pricing around log ingestion. If you have a lot of logs coming in, the costs can add up pretty quickly, especially in a larger environment. We have to be selective about what we send to Datadog and keep an eye on usage so the bill doesn’t grow unexpectedly. Review collected by and hosted on G2.com.

What problems is Datadog solving and how is that benefiting you?

Datadog mainly helps us avoid spending too much time trying to figure out where an issue is coming from. We can monitor our applications, infrastructure, and logs in one place, which makes it easier to spot problems and troubleshoot them. The alerts are also useful because we can know about an issue before users start reporting it. For me, the biggest benefit is that the team can respond faster and spend less time jumping between different monitoring tools. Review collected by and hosted on G2.com.

Aditya J.

Security Delivery Analyst - Identity and Access Management

Enterprise (> 1000 emp.)

7/17/2026

"Enterprise Observability and Monitoring at Scale with Datadog"

3.5/5

What do you like best about Datadog?

What I like best about Datadog is its ability to provide deep observability across infrastructure, applications, logs, cloud services, and identity platforms from a single pane of glass. Having used Datadog for an extended period, I've found its dashboards, alerting capabilities, and correlation between metrics, logs, and traces to be extremely valuable for both proactive monitoring and incident response.

As an administrator, I appreciate the flexibility in creating custom dashboards, configuring monitors, and tuning alerts to reduce noise while ensuring critical issues are detected early. The platform's extensive integration ecosystem makes it easy to onboard new services, and features such as anomaly detection, log analytics, and service mapping significantly improve troubleshooting efficiency.

Overall, Datadog has helped streamline operations, reduce mean time to resolution (MTTR), and provide actionable insights that support both day-to-day monitoring and long-term platform optimization. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

While Datadog is a mature and feature-rich platform, one area that can be challenging is cost management at scale. As environments grow and more teams onboard services, log ingestion, custom metrics, and data retention costs require ongoing optimization and governance. I've also found that in large enterprise deployments, alert tuning and monitor management need regular review to avoid alert fatigue and maintain signal-to-noise quality.

Another area for improvement is that some advanced configurations and cross-product features can have a learning curve for newer administrators. While the platform offers tremendous flexibility, fully leveraging its capabilities often requires experience and a well-defined monitoring strategy.

That said, these challenges are common for enterprise observability platforms, and the operational benefits Datadog provides generally outweigh the drawbacks. Review collected by and hosted on G2.com.

What problems is Datadog solving and how is that benefiting you?

Datadog solves the challenge of maintaining visibility across complex, distributed environments by bringing infrastructure metrics, application performance, logs, traces, security signals, and cloud services into a single observability platform. Prior to adopting Datadog, troubleshooting often required switching between multiple tools and manually correlating data from different sources.

As a long-time user and administrator, I've found that Datadog significantly improves operational efficiency by enabling teams to quickly identify performance bottlenecks, detect anomalies, and investigate incidents from a centralized interface. The ability to correlate metrics, logs, and traces has greatly reduced troubleshooting time and improved root cause analysis.

Datadog has also helped us implement a more proactive monitoring approach through intelligent alerting, anomaly detection, and service health visibility. This has contributed to faster incident response, reduced downtime, improved system reliability, and better overall user experience. From an operations perspective, it has become a critical platform for maintaining service health and supporting informed decision-making across teams. Review collected by and hosted on G2.com.

Anshul S.

SDET

Mid-Market (51-1000 emp.)

6/30/2026

"Datadog: Unified Logs, Metrics & Traces for Real-Time Visibility and Faster Debugging"

4.5/5

What do you like best about Datadog?

One of the biggest strengths of Datadog is how it brings logs, metrics, traces, and alerts into a single platform. Instead of switching between multiple monitoring tools, I can quickly identify what's happening across the entire application stack. Comprehensive dashboards that provide real-time visibility into application health. Powerful log search and filtering for faster root cause analysis. APM (Application Performance Monitoring) that helps identify performance bottlenecks. Intelligent alerting that notifies the team before issues significantly impact users. Seamless integrations with cloud services, databases, CI/CD pipelines, and infrastructure tools. In my QA and automation workflow, Datadog significantly reduces the time required to investigate production issues. Rather than relying solely on application logs, I can correlate metrics, traces, and logs to pinpoint the exact cause of a problem. This makes debugging much faster and improves collaboration between QA, developers, and DevOps teams. Overall, Datadog provides the visibility needed to proactively monitor systems, troubleshoot issues efficiently, and maintain application reliability. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

Although Datadog is one of the most comprehensive monitoring tools I've used, there are a few areas where it could improve. Pricing can become expensive as the number of hosts, logs, and monitored services increases. The large number of features can make the platform overwhelming for new users. Building advanced dashboards and queries sometimes requires a learning curve. High log volumes need careful management to avoid unnecessary costs. Some alerts require fine-tuning to reduce noise and avoid alert fatigue. For me, the biggest challenge is cost management. As monitoring requirements grow, it's important to optimize log retention, dashboards, and alert configurations to keep expenses under control. Review collected by and hosted on G2.com.

What problems is Datadog solving and how is that benefiting you?

Datadog solves the problem of limited visibility into application performance and infrastructure health. Instead of checking multiple tools for logs, metrics, traces, and alerts, Datadog centralizes everything into a single platform, making monitoring and troubleshooting much more efficient. Detects application and infrastructure issues in real time. Centralizes logs, metrics, traces, and performance data. Speeds up root cause analysis during production incidents. Provides proactive alerts before issues impact end users. Helps monitor APIs, servers, databases, and cloud services from one dashboard. I use Datadog to investigate production issues, validate deployments, monitor API health, and analyze application performance. Having all the relevant telemetry in one place helps me identify problems much faster and collaborate effectively with developers and DevOps teams. The biggest benefit is reduced incident resolution time. By quickly correlating logs, metrics, and traces, Datadog helps the team diagnose issues faster, minimize downtime, and deliver a more reliable experience for users. Review collected by and hosted on G2.com.

Anshuman S.

Advance Software Engineer

Mid-Market (51-1000 emp.)

9/10/2026

"All-in-One Observability with Great Cloud Integrations and a User-Friendly UI"

4.5/5

What do you like best about Datadog?

It is an all-in-one Observability tool which is a combination of infrastructure metrics, application logs, application performance monitoring, real user monitoring, and security monitoring. It has very good integrations with almost all cloud providers. The User Interface is very user-friendly. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

It's very costly. Sending application logs to Datadog can lead to unpredictable costs. Providing add-on features is a very complex task. It's difficult to learn for new users. Review collected by and hosted on G2.com.

Bertrand P.

Senior Product & Digital Transformation Leader

Retail

Enterprise (> 1000 emp.)

6/26/2026

"Faster incident detection and root-cause analysis, leading to better customer experience."

4.5/5

What do you like best about Datadog?

As a product manager what I like most about Datadog is how it centralizes observability for complex systems in a single platform. It brings metrics, logs, traces, and alerts together in one place, making it much easier to understand overall system health and troubleshoot issues quickly for runners and support teams. Onboarding tech teams is easy.

Its real-time monitoring and alerting are especially valuable because they help detect incidents early and improve response times. I also appreciate the breadth and depth of integrations with cloud providers, infrastructure tools, and application services, which makes Datadog adaptable across different architectures.

Depending the implementation the price can evolve but you are fully mastering the cost.

Overall, it delivers strong visibility and control over system performance, which feels essential in modern distributed environments at scale. Perspectives to use it coupling with AI support agent is a plus to prepare the future. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

The tool provide insights and data but teams have to spend significant time interpreting what action should be taken. Review collected by and hosted on G2.com.

Zaid K.

Senior devops engineer

Enterprise (> 1000 emp.)

7/21/2026

"Comprehensive Monitoring with Easy Integration"

4.5/5

What do you like best about Datadog?

I use Datadog for monitoring, and it helps me in a lot of ways. It shows in-depth logs of the systems and provides a comprehensive view of metrics. There are lots of metrics, and the integration is very simple, especially with Terraform. It offers a good view of metrics, which is really valuable for me. I appreciate being able to create my own dashboards and metrics. The setup was quite easy, and we received support from the data ops team to integrate it with the systems. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I think Datadog's anomaly detection is quite basic in alerting me. I would like it to be enhanced to provide more detailed warning alerts. Review collected by and hosted on G2.com.

Gunther C.

Software Engineer

Mid-Market (51-1000 emp.)

6/5/2026

"Easy Datadog Integration with Powerful, Insightful Dashboards"

5/5

What do you like best about Datadog?

I like how easy datadog is to integrate with existing systems. Once set up it provides an incredibly useful view into the status and state of application health. It's dashboards are very easy to create and are a valuable method for gathering key information all in one place. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I have few complaints about Datadog, I think it becomes more valuable the more an organization invests in configuring it, and my only complaint might simply be a company not using it enough (or taking a long time to get fully set up) Review collected by and hosted on G2.com.

Stephan L.

Small-Business (50 or fewer emp.)

10/10/2026

"Seamless Observability with Datadog"

4.5/5

What do you like best about Datadog?

I find the seamless observability engine of Datadog to be a standout feature. It allows us to automate out of the box data correlation, which is incredibly helpful. When an alert triggers or there's a performance anomaly, I can pick a metric data point instantly to the corresponding log line with distributed ATM traces without losing context. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

None. Review collected by and hosted on G2.com.

Rose E.

Small-Business (50 or fewer emp.)

10/16/2026

"Granular Observability, Fast Setup—But a Crowded UI"

4.5/5

What do you like best about Datadog?

I like Datadog’s granularity across the platform, along with its interactive filters and streamlined searches. They make searching user-friendly and efficient, and help me find errors and performance bottlenecks across log lines and microservices. Datadog turns a flood of logs and telemetry into actionable diagnostic insights. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I find Datadog’s interface can feel a little overwhelming, with so much data, menus, and controls packed onto the screen, especially for a new user. Review collected by and hosted on G2.com.