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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Datadog Community

Languages Supported

English, French, Portuguese, Spanish

Solution Type

All-in-One

Overview by

Camille Chang

Used by DataArt and 2 others

Beta

Pricing

Pricing provided by Datadog.

Free

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Datadog Integrations

(35)

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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.

Datadog Reviews FAQs

Generated using AI

Last updated: Nov 04, 2026

What is Datadog, and what problem is it built to solve?

Datadog is a monitoring and analytics platform for cloud-scale applications. It is built to help organizations monitor infrastructure, application performance, and logs in real time.

What languages does Datadog support?

Datadog supports 4 languages: English, French, Portuguese, and Spanish.

What company sizes and industries is Datadog the best fit for?

Datadog has positive feedback from small-business, mid-market, and enterprise users, with particularly visible representation in software, IT services, and financial services. The strongest fit signals span these company-size and industry segments:

Company sizes: Small business, mid-market, and enterprise teams.
Industries: Computer software, information technology and services, and financial services.

How long does it take to implement Datadog across a team?

Implementation timelines vary with the environment and scope, though 1–3 months is the most commonly reported range. Other reported timelines include setups completed in under a month and deployments taking several months or longer.

What customer support options does Datadog provide, and how do users find the overall support experience?

Support is available through self-service resources and direct channels, including phone, chat, and email/help desk, with 24/7 live-rep support. The experience is mixed: users describe responsive, knowledgeable help and useful guidance, while others report slow replies or support that does not resolve the issue efficiently.

What is the experience like for teams switching to Datadog from another tool?

Teams have moved to Datadog from monitoring and observability tools such as New Relic, Splunk, Grafana, Dynatrace, and Prometheus. The move is often driven by a desire to bring infrastructure monitoring, logs, metrics, traces, and alerting together, with integrations and data correlation that can make investigation more direct. Some transitions feel straightforward, while instrumentation, configuration, and learning the broader platform can take work.

What tools and platforms does Datadog integrate with, and how do these integrations help users get more value from the product?

Datadog offers 31 integrations. The integrations users describe span cloud and infrastructure, team communication and incident response, and service-management and developer tools:

Cloud and infrastructure: AWS, Azure, Google Cloud, and Kubernetes.
Team communication and incident response: Slack, Microsoft Teams, and PagerDuty.
Service management and development: ServiceNow, Jira, and GitHub.

These connections bring monitoring data and alerts into existing cloud environments and team workflows, helping teams investigate issues without switching between as many separate tools.

What features or capabilities could make Datadog even more useful?

Datadog would be more useful with clearer paths for new users and more straightforward ways to tailor dashboards and investigations. Recurring improvement areas include:

Simpler onboarding and navigation: Make common workflows, documentation, and setup guidance easier to find and follow.
Clearer cost controls: Improve cost visibility and forecasting, especially as log volumes, hosts, and custom metrics grow.
More flexible dashboards and alerting: Expand visualization and customization options while making alerts easier to tune and less noisy.

How useful do Datadog users find its AI features in everyday use?

Datadog’s AI is most useful as an aid to anomaly detection and incident investigation: it can surface unusual patterns and help narrow the search for a cause. Its suggestions are not consistently specific enough to act on without review, so users still rely on their own context and expertise to validate findings.

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Lora D.

Mid-Market (51-1000 emp.)

10/3/2026

"Centralized Observability with Real-Time Alerting"

5/5

What do you like best about Datadog?

I really value Datadog's real-time alerting and its extensive cloud integration ecosystem. It's impressive how easy it is to onboard new engineering teams and connect cloud providers, infrastructure components, and microservices. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

One thing I find challenging with Datadog is that even though it excels at generating deep diagnostic data and telemetry, it still requires significant human effort to interpret that data and determine concrete actions. Review collected by and hosted on G2.com.

Brant H.

Small-Business (50 or fewer emp.)

10/7/2026

"Effortless Monitoring with Room for Dashboard Improvement"

4/5

What do you like best about Datadog?

I rely on Datadog every single day in our engineering and technical operations team to maintain clear visibility across all our services, active endpoints, and background applications. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

What I dislike most about Datadog is that the default dashboards for services and specific API endpoints could be much better structured. Additionally, the user interface feels a bit confusing and cluttered. Review collected by and hosted on G2.com.

Isiah C.

Small-Business (50 or fewer emp.)

11/12/2026

"Rock-Solid APM for Incidents, Despite Fragmented Navigation"

4.5/5

What do you like best about Datadog?

I use Datadog as my go-to platform for investigating incidents and debugging back-end service issues. I value its reliability and rock-solid performance, especially in APM. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I find Datadog’s user experience can feel fragmented because too many features, tools, and views are spread across separate navigation windows. Review collected by and hosted on G2.com.

Rubene R.

Small-Business (50 or fewer emp.)

9/29/2026

"Unmatched Observability with Pricing Caveats"

4.5/5

What do you like best about Datadog?

I rely on Datadog as my primary all-in-one observability platform every single day. Out of all the modules built into the platform, I find the APM distributed tracing combined with the log explorer to be the most invaluable feature set for my daily workflow. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

My biggest frustration with Datadog comes down to its pricing model and how fast ingestion costs can spiral completely out of control if you aren't hypervigilant. Review collected by and hosted on G2.com.

Ila R.

Small-Business (50 or fewer emp.)

10/8/2026

"Streamlined Monitoring with Enhanced Log Management"

4.5/5

What do you like best about Datadog?

I use Datadog as our primary platform for log management, infrastructure monitoring, and application performance monitoring. I like its overall user interface, smooth navigation, and how seamlessly it handles central log management. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I find it annoying that Datadog automatically converts all metric and span tags into lowercase. This can be a hassle when we have existing tags or custom metadata from other sources using camel case or uppercase. Review collected by and hosted on G2.com.

Rogelio H.

Small-Business (50 or fewer emp.)

10/17/2026

"Unifies Fragmented Observability with Powerful Integrations"

5/5

What do you like best about Datadog?

I like having Datadog as a central place to bring together heterogeneous data and make it instantly queryable under a single schema. The wide range of integrations lets me connect database clusters, cloud provider APIs, and container orchestrators without writing complex scrapers. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

My biggest complaint with Datadog is the time involved. Review collected by and hosted on G2.com.

Dena M.

Small-Business (50 or fewer emp.)

10/1/2026

"Essential Infrastructure Monitoring with Seamless Integration"

4.5/5

What do you like best about Datadog?

I really like how simple and clean the integration process with Datadog is. Managing our monitoring configuration through infrastructure as code using Terraform is a standout capability for me. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

My main criticism lies in Datadog's built-in anomaly detection and warning alert capabilities. While the overall experience is positive, this aspect seems less effective and could be improved. Review collected by and hosted on G2.com.

Guy W.

Small-Business (50 or fewer emp.)

10/18/2026

"Clean, Intuitive Monitoring for Better Postmortems"

5/5

What do you like best about Datadog?

I like Datadog’s professional layout and clean design, and it’s easy to use for everyday monitoring. The visual design makes dense system graphs, log timelines, and metric widgets feel intuitive rather than chaotic. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I find some parts of the UI complex, and I run into feature limitations when I try to customize things more deeply. Review collected by and hosted on G2.com.

Terrie P.

Small-Business (50 or fewer emp.)

10/14/2026

"Effortless Integrations, Clear Dashboards for DevOps"

4.5/5

What do you like best about Datadog?

I like how easy Datadog is to integrate with existing systems, and how little effort it takes to set up data collection across our servers and cloud integrations. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

I have very few complaints about Datadog itself, but it becomes more valuable the more time and effort I put into configuring it. Review collected by and hosted on G2.com.

Spencer B.

Small-Business (50 or fewer emp.)

10/20/2026

"Unified Global Visibility and AI, Despite Cost Management Complexity"

4.5/5

What do you like best about Datadog?

I like how Datadog seamlessly consolidates global environments and regions in a single interface. The multi-region visibility lets us scale our footprint without adding operational friction. Review collected by and hosted on G2.com.

What do you dislike about Datadog?

My biggest frustration with Datadog is how complex cost management can get to monitor across an environment, especially with log ingestion and custom metrics. Review collected by and hosted on G2.com.