Agentifact assessment — independently scored, not sponsored. Last verified Apr 13, 2026.
Datadog LLM Observability
Datadog's LLM Observability product monitors AI application performance, traces LLM calls end-to-end, and evaluates output quality in production. Integrates natively with OpenAI, Anthropic, and major frameworks like LangChain. Provides latency dashboards, token cost tracking, and automated quality evaluations.
Solid choice for most workflows
You need end-to-end visibility into LLM chains and agent workflows to debug latency, errors, and costs in production without manual instrumentation.
Excellent operational monitoring and alerting; traces production traffic reliably but evals are monitoring-focused, not CI-gated.
You want automated quality checks on LLM outputs like hallucinations or toxicity directly in your observability pipeline.
Runs automatically on prod traces with solid multi-stage hallucination detection; great for ops insights, less for deep prompt iteration.
Datadog excels at full-stack ops monitoring; Braintrust owns dedicated evals and CI integration.
Pick Datadog when you need LLM traces correlated to APM/infra with alerting in enterprise stacks.
Pick Braintrust for eval-first workflows, release gating, and prompt lifecycle management.
Evals not CI-native
Evaluation results integrate into dashboards but lack deep CI/CD pipeline support or release gating compared to eval platforms.
Trust Breakdown
What It Actually Does
Monitors your AI applications in production by tracking API calls to services like OpenAI and Anthropic, measuring response times and costs, and automatically checking output quality.
Datadog's LLM Observability product monitors AI application performance, traces LLM calls end-to-end, and evaluates output quality in production. Integrates natively with OpenAI, Anthropic, and major frameworks like LangChain. Provides latency dashboards, token cost tracking, and automated quality evaluations.
Fit Assessment
Best for
- ✓monitoring
- ✓observability
- ✓llm-tracing
- ✓cost-tracking
- ✓error-detection
Not ideal for
- ✗automatic activation of LLM observability charges without explicit opt-in when OpenTelemetry GenAI semantic conventions are detected
- ✗cost estimation displays as NA for non-OpenAI models like Gemini
- ✗token ingestion charges accumulate exponentially for high-traffic applications without visibility into billing triggers
Known Failure Modes
- automatic activation of LLM observability charges without explicit opt-in when OpenTelemetry GenAI semantic conventions are detected
- cost estimation displays as NA for non-OpenAI models like Gemini
- token ingestion charges accumulate exponentially for high-traffic applications without visibility into billing triggers
Score Breakdown
Protocol Support
Capabilities
Governance
- audit-log
- pii-masking
- rate-limiting