Datadog
Unified observability for metrics, traces, and logs
Datadog is profiled here as a Observability tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Datadog is an observability platform, founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, that brings infrastructure metrics, distributed traces, and logs together in one place for engineering teams. Agents and integrations collect signals from servers, containers, databases, and cloud services, and Datadog correlates them so an on-call engineer can move from an alert to the root cause across layers. Its LLM Observability product extends this coverage to AI applications, tracking prompts, latency, token cost, and quality alongside the rest of the stack.
Key Capabilities:
Infrastructure metrics across hosts, containers, and cloud services
Distributed tracing that follows requests across microservices
Log management with search, parsing, and correlation to traces
LLM Observability for tracing prompts, cost, and quality
Hundreds of integrations for databases, queues, and platforms
Dashboards, alerting, and anomaly detection across all signals
Alternative tools
- HoneyHive
Evaluation and observability platform for AI agents
- Sentry
Error tracking and performance monitoring for developers
- SigNoz
Open-source, OpenTelemetry-native observability platform
- Arize AX
Enterprise platform for AI observability and evaluation
- OpenTelemetry
Vendor-neutral standard for traces, metrics, and logs
- Prometheus
Open-source metrics monitoring and time-series database