Datadog AI and LLM observability-
performance, cost, quality, and safety visible in four weeks.
Teams shipping LLM-powered applications often have no structured visibility into how those applications are actually performing, whether inference is slow, which prompts are expensive, where quality is degrading, and whether sensitive data is being exposed. Standard APM covers the infrastructure around the AI layer but not the AI layer itself. This accelerator changes that.
Datadog LLM Observability configured for your AI applications. APM connected to trace the full call chain. Sensitive Data Scanner applied. AI dashboard pack, alert pack, and issue taxonomy on delivery in four weeks.
From invisible AI operations to structured LLM observability
The four weeks instrument the AI application layer, connect it to the broader service context, and establish the visibility structures needed to operate LLM workloads confidently.
- LLM Observability configuration, Datadog LLM Observability integrated with your AI applications (OpenAI, Anthropic, or other providers) to capture inference metrics, token usage, latency, and error rates per model and prompt
- APM connection, AI application traces connected to downstream service APM so the full call chain is visible: from user request through LLM inference to backend data retrieval and response
- Sensitive Data Scanner, scanning configured to detect sensitive data patterns in prompts and completions; masking rules applied where required; audit trail established
- Cost visibility, inference spend tracked per model, per use case, and over time; cost monitors configured to alert on unexpected spend growth
- Quality and safety monitoring, quality degradation signals configured (error rates, latency percentiles, output length anomalies); safety signals from sensitive data scanning integrated into operational monitoring
- Issue taxonomy and ownership, categories of AI observability issues defined (performance, cost, quality, safety), ownership mapped to teams, routing established
Four deliverables at the end of week four
The right accelerator for these situations
- AI-powered features are in production but the team has no visibility into LLM inference performance, latency issues, quality degradation, and cost growth are discovered reactively
- LLM inference costs are growing but nobody can tell which models, prompts, or use cases are driving the increase, spend is unattributed and uncontrolled
- Compliance or data governance requires demonstrable controls over what data enters and exits LLM prompts and completions, sensitive data scanning is needed but not configured
- Datadog LLM Observability is available on the account but hasn't been set up, the team wants AI observability operational without a multi-sprint internal engineering project
Ready to get AI and LLM observability operational?
Four weeks, fixed scope, AI dashboard pack and cost visibility on delivery. Talk to Critical Cloud and we'll scope the accelerator against your AI application stack.