Datadog LLM — Observability, Prompts & Evals Platform
Observability, Prompts & EvalsLayer 4EnterpriseWhat is Datadog LLM?
Datadog LLM Observability is a comprehensive monitoring platform designed to help teams deliver LLM applications to production faster with end-to-end tracing across AI agents, structured experiments, and robust quality and security evaluations. The platform provides complete visibility into inputs, outputs, latency, token usage, and errors across AI agent workflows. It features structured experiment management for testing prompt changes, model swaps, and parameter tuning, along with quality evaluations including hallucination detection and output clustering for drift identification. Security features include sensitive data scanning and prompt injection detection. As part of the broader Datadog platform, LLM Observability integrates seamlessly with APM and Real User Monitoring for unified full-stack visibility, allowing teams to correlate LLM workloads with backend services, infrastructure, and user sessions.
Key Features
- ✓LLM monitoring within Datadog platform
- ✓Unified APM + LLM observability
- ✓Automatic instrumentation
- ✓Cost and token tracking
- ✓Integration with existing Datadog dashboards
Pros & Cons
Pros
- +Seamless integration with Datadog's full observability suite for unified application monitoring
- +Built-in sensitive data scanning and prompt injection detection for enhanced security
- +Established enterprise platform with proven reliability and 9,700+ employees globally
- +Automatic cost estimation for LLM requests using public pricing models
- +Quick setup in seconds using their SDK
Cons
- -Premium automatically activated at $120/day when LLM spans detected with no opt-out
- -Can be expensive at scale compared to specialized LLM observability tools
- -Requires familiarity with Datadog ecosystem for optimal usage
- -Per-span pricing model may become costly for high-volume applications
Datadog LLM Pricing
Common Use Cases
Enterprise teams already using Datadog who want to add LLM monitoring
- •Unified monitoring for AI and traditional services
- •Enterprise LLM monitoring at scale
- •Correlating LLM performance with infrastructure
- •Compliance and audit logging
- •Large-scale production monitoring
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