What is Datadog?
Datadog is an observability platform that gives engineering teams full visibility into their infrastructure, applications, and security across any cloud environment. It collects metrics, traces, logs, and events from every layer of a tech stack and brings them together in one place. The platform covers everything from server health and container performance to database queries, network traffic, and real user behavior. Teams working across cloud, on-premises, or hybrid setups can use Datadog to monitor, troubleshoot, and secure their systems at any scale. A mobile app extends access beyond the desktop.
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Features & Benefits
- Infrastructure Monitoring: monitor hosts, containers, serverless functions, and Kubernetes clusters across cloud and on-premises environments in real time.
- Application Performance Monitoring (APM): trace requests end-to-end across services to find latency, errors, and bottlenecks in application code.
- Log Management: ingest, search, and analyze logs at scale; includes sensitive data scanning, audit trails, and observability pipeline controls.
- Real User Monitoring (RUM): capture browser and mobile user sessions to track frontend performance, errors, and user journeys.
- Synthetic Monitoring: run AI-driven proactive tests on critical application paths and APIs before real users are affected.
- Network Monitoring: analyze traffic patterns and dependencies across cloud environments to detect performance issues at the network layer.
- Database Monitoring: track query performance, connection health, and resource usage across database instances.
- Data Streams Monitoring: observe and troubleshoot data pipelines end-to-end.
- Cloud Security: detect misconfigurations, manage entitlements, identify vulnerabilities, and monitor compliance across cloud environments.
- Cloud SIEM: correlate security signals across logs and infrastructure to identify and respond to threats.
- Code Security: find vulnerabilities in application code, open-source dependencies, and infrastructure-as-code before and during runtime; includes SAST and IAST analysis.
- Workload Protection: monitor and protect running workloads against active threats.
- App and API Protection: detect and block attacks targeting applications and APIs at runtime.
- Secret Scanning: identify exposed credentials and secrets in code and infrastructure.
- CI Visibility: monitor CI/CD pipeline performance and track build and test results over time.
- Test Optimization: reduce test run times and surface flaky tests across the software delivery pipeline.
- Continuous Testing: run automated tests across browsers, mobile, and APIs as part of delivery workflows.
- Session Replay: replay recorded user sessions to reproduce frontend issues and understand real user behavior.
- Product Analytics: analyze how users interact with product features to identify engagement and friction patterns.
- Experiments: run feature flag-based experiments to test changes against real user segments.
- Error Tracking: group, prioritize, and investigate errors across frontend and backend services.
- Incident Response: manage the full incident lifecycle from detection through resolution with integrated workflows.
- Case Management: track issues and investigations tied directly to monitoring data.
- Service Level Objectives (SLOs): define, track, and report on reliability targets across services.
- Workflow Automation: build automated remediation and operational workflows triggered by monitoring events.
- App Builder: create internal tools and dashboards connected to live observability data.
- Watchdog: apply AI-powered anomaly detection across metrics, traces, and logs to surface issues without manual threshold setting.
- Bits AI Agents: deploy AI agents that investigate incidents, respond to questions in natural language, and take remediation actions autonomously.
- Bits Chat: interact with observability data through a conversational AI interface.
- Bits Code: get AI-assisted code suggestions and fixes within the platform.
- Bits Security Analyst: run AI-driven security investigations tied to live threat and observability data.
- Agent Observability: monitor the health and behavior of AI agents running in production environments.
- GPU Monitoring: track GPU resource usage and performance for AI and compute-intensive workloads.
- Fleet Automation: manage and update Datadog agent deployments across large infrastructure fleets.
- Dashboards and Notebooks: build custom visualizations and collaborative analysis documents from live data.
- Alerts: configure threshold, anomaly, and forecast-based alerts across any data type in the platform.
- Cloud Cost Management: track and analyze cloud spending alongside infrastructure performance data.
- Internal Developer Portal: give engineering teams a centralized view of services, ownership, and delivery health.
- DORA Metrics: measure deployment frequency, lead time, change failure rate, and recovery time across engineering teams.
- Mobile App: access dashboards, alerts, and incident data from iOS and Android devices.
- Integrations: connect to a broad range of cloud providers, services, and tools through native integrations and OpenTelemetry support.
- Access Control: manage permissions across teams and resources using role-based access controls.
What can Datadog do?
- Monitor cloud infrastructure performance in real time
- Trace application requests across distributed services
- Analyze and search logs from across a tech stack
- Detect anomalies in metrics without manual thresholds
- Monitor Kubernetes clusters and container health
- Track real user sessions on web and mobile apps
- Run synthetic tests on APIs and browser flows
- Monitor network traffic across cloud environments
- Detect security threats across cloud workloads
- Scan code for vulnerabilities before and during runtime
- Manage and resolve incidents from detection to close
- Track CI/CD pipeline performance and test results
- Monitor database query performance and health
- Analyze cloud infrastructure spend alongside performance data
Real-World Applications
Engineering teams running distributed systems across multiple cloud providers may find cloud monitoring fragmented across separate tools. Datadog’s observability platform pulls infrastructure metrics, application traces, and logs into a unified view. This makes it possible to correlate a spike in error rates with a specific deployment or infrastructure change without switching between systems.
Product and reliability teams responsible for user-facing applications can use Datadog to connect backend performance data with real user experience. Session replay, RUM, and synthetic monitoring work together to show where users encounter friction, which makes it easier to prioritize fixes based on actual impact rather than assumptions.
Security teams working alongside DevOps and engineering may use the observability platform to close the gap between security signals and operational context. Cloud SIEM, workload protection, and code security tools share the same data layer as infrastructure and application monitoring. This lets teams investigate a threat in the context of what the affected system was actually doing at the time.
Organizations managing large-scale software delivery pipelines can track build performance, test reliability, and deployment frequency through CI Visibility and DORA Metrics. Teams responsible for maintaining service reliability can define SLOs and receive alerts before those targets are at risk, with automated workflows available to trigger remediation actions.
