{"name":"Datadog","slug":"datadog","category":"observability","type":"cloud","website":"https://www.datadoghq.com","pricing":"paid","pricing_tiers":["Free tier (5 hosts)","$15/host/mo Infrastructure","$31/host/mo APM","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","go","java","ruby","csharp","php"],"frameworks":["langchain","openai-agents"],"agent_features":{"llm_tracing":true,"cost_tracking":true,"evaluation":false,"prompt_management":false,"real_time_monitoring":true},"compliance":["soc2","hipaa","gdpr","pci-dss","iso27001"],"best_for":"Full-stack observability at scale — infrastructure, APM, logs, and LLM tracing in one platform","limitations":"Expensive at scale; LLM observability is newer and less mature than dedicated tools like Langfuse; vendor lock-in on proprietary data format","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://docs.datadoghq.com","pricing":"https://www.datadoghq.com/pricing"},"feature_labels":{"llm_tracing":"Trace LLM calls, tool invocations, and agent reasoning steps end-to-end","cost_tracking":"Track token usage and cost per request, per agent run, and per model","evaluation":"Score agent outputs against test datasets with automated evaluators","prompt_management":"Version, manage, and A/B test prompts in production","real_time_monitoring":"Live dashboards and alerting for agent performance metrics"},"comparisons":[{"slug":"datadog-vs-grafana","title":"Datadog vs Grafana","vs":"grafana"},{"slug":"datadog-vs-helicone","title":"Datadog vs Helicone","vs":"helicone"},{"slug":"datadog-vs-langfuse","title":"Datadog vs Langfuse","vs":"langfuse"},{"slug":"datadog-vs-langsmith","title":"Datadog vs LangSmith","vs":"langsmith"},{"slug":"langfuse-vs-datadog","title":"Langfuse vs Datadog","vs":"langfuse"}],"body":"# Datadog\n\nDatadog is a comprehensive cloud monitoring and observability platform. For AI agent developers, it offers LLM Observability as an extension of its existing APM product — tracing LLM calls, token usage, latency, and error rates alongside traditional infrastructure metrics.\n\nThe main advantage is consolidation: if your team already uses Datadog for infra and APM, adding LLM tracing means one fewer vendor. The tradeoff is that its LLM-specific features are less deep than purpose-built tools like Langfuse or Langsmith."}