View source for Datadog LLM-based System Observability Framework

A [[Datadog LLM-based System Observability Framework]] is an [[LLM observability framework]] by [[Datadog Inc.]] that can be used to create [[datadog LLM monitoring solution]]s (that support [[production generative AI deployment]]s and [[enterprise AI governance requirement]]s).
* <B>AKA:</B> [[Datadog LLM Monitoring Framework]], [[Datadog GenAI Observability Platform]], [[Datadog AI Application Monitoring Solution]].
* <B>Context:</B>
** It can typically provide [[End-to-End Tracing Capability]] through [[datadog LLM chain tracing]], [[datadog prompt-response correlation]], and [[datadog AI component visualization]].
** It can typically enable [[Root Cause Analysis]] through [[datadog LLM trace visualization]], [[datadog AI latency breakdown]], and [[datadog AI error identification]].
** It can typically monitor [[LLM Operational Metric]]s through [[datadog token consumption tracking]], [[datadog AI cost analysis]], and [[datadog LLM response time measurement]].
** It can typically evaluate [[LLM Response Quality]] through [[datadog AI toxicity detection]], [[datadog AI relevance scoring]], and [[datadog AI factual consistency check]]s.
** It can typically enforce [[LLM Security Control]]s through [[datadog AI prompt injection detection]], [[datadog sensitive data scanning]], and [[datadog AI content filtering]].
** ...
** It can often identify [[LLM Performance Issue]]s through [[datadog AI semantic clustering]] of [[similar datadog LLM request]]s and [[datadog AI error pattern]]s.
** It can often support [[Custom Evaluation]] through [[datadog user-defined metric]]s, [[datadog AI domain-specific criteria]], and [[datadog AI evaluation pipeline]]s.
** It can often correlate [[LLM Performance]] with [[datadog infrastructure health]] through [[datadog unified dashboard]]s and [[datadog cross-system metric]]s.
** It can often enable [[A/B Testing]] through [[datadog AI model performance comparison]]s, [[datadog AI output quality measurement]], and [[datadog AI version control]].
** It can often detect [[Hallucination Pattern]]s through [[datadog AI semantic drift analysis]], [[datadog retrieval-response alignment check]]s, and [[datadog AI ground truth comparison]].
** It can often provide [[Real-time Alert]]s for [[datadog AI anomaly detection]], [[datadog AI security breach]], and [[datadog AI cost threshold violation]].
** It can often visualize [[LLM Usage Pattern]]s through [[datadog AI heatmap]]s, [[datadog AI traffic analysis]], and [[datadog AI user segmentation]].
** ...
** It can range from being a [[Basic Datadog LLM Monitoring Solution]] to being an [[Enterprise-Grade Datadog LLM Governance System]], depending on its [[datadog LLM integration scope]] with [[existing datadog tool]]s.
** It can range from being a [[Development Environment Datadog LLM Debugging Tool]] to being a [[Production Datadog LLM Monitoring Platform]], depending on its [[datadog LLM deployment context]].
** It can range from being a [[Single-Provider Datadog LLM Monitor]] to being a [[Multi-Provider Datadog LLM Observability System]], depending on its [[datadog configured LLM service]]s.
** It can range from being a [[Datadog LLM Technical Monitoring System]] to being a [[Datadog LLM Business Impact Analysis Platform]], depending on its [[datadog AI business metric]] integration.
** ...
** It can integrate with [[Datadog APM]] for [[datadog full-stack trace correlation]] between [[datadog LLM component]]s and [[datadog traditional service]]s.
** It can connect to [[Datadog Infrastructure Monitoring]] for [[datadog host-level performance context]] of [[datadog LLM-serving system]]s.
** It can leverage [[Datadog Security Platform]] for [[datadog comprehensive threat detection]] across [[datadog LLM application]]s.
** It can support [[Multiple LLM Provider]]s including [[datadog openai integration]], [[datadog anthropic integration]], [[datadog cohere integration]], and [[datadog amazon bedrock integration]].
** It can enable [[Compliance Reporting]] for [[datadog AI regulatory requirement]]s, [[datadog AI audit purpose]]s, and [[datadog AI governance documentation]].
** It can utilize [[Datadog Bits AI]] for [[datadog AI-assisted troubleshooting]], [[datadog AI root cause suggestion]]s, and [[datadog AI optimization recommendation]]s.
** ...
* <B>Examples:</B>
** [[Datadog LLM Observability Implementation Pattern]]s, such as:
*** [[Datadog LLM Monitoring Architecture]]s, such as:
**** [[Centralized Datadog LLM Observability]] for [[datadog enterprise-wide oversight]] and [[datadog cross-application comparison]].
**** [[Service-Specific Datadog LLM Monitoring]] for [[datadog targeted optimization]] of [[datadog individual AI application]]s.
**** [[Hybrid Datadog LLM Monitoring]] combining [[datadog centralized governance]] with [[datadog service-level monitoring]].
*** [[Datadog LLM Integration Approach]]es, such as:
**** [[Datadog LLM Python SDK Integration]] for [[datadog deep instrumentation]] of [[datadog LLM application code]].
**** [[Datadog LLM API-Based Monitoring]] for [[datadog lightweight observation]] without [[datadog code modification]].
**** [[Datadog LLM Chain Framework Integration]] with [[datadog LangChain monitoring]], [[datadog LlamaIndex tracking]], and [[datadog custom chain observation]].
** [[Datadog LLM Observability Release]]s, such as:
*** [[Datadog LLM Observability (June 2024)]], with [[datadog LLM general availability]], [[datadog enhanced AI security feature]]s, and [[datadog expanded integration ecosystem]].
*** [[Datadog LLM Observability (August 2023)]], with [[datadog LLM beta capability]], [[datadog initial chain tracing]], and [[datadog basic AI evaluation]].
** [[Datadog LLM Observability Use Case]]s, such as:
*** [[Datadog LLM Industry Implementation]]s, such as:
**** [[WHOOP Datadog LLM AI Coaching Service]] for [[datadog 24/7 availability monitoring]] and [[datadog AI model performance evaluation]].
**** [[AppFolio Datadog LLM Real Estate AI]] for [[datadog sensitive data protection]] and [[datadog response quality assurance]].
**** [[Cash App Datadog LLM Financial Assistant]] for [[datadog AI compliance monitoring]] and [[datadog AI transaction security]].
*** [[Datadog LLM Workflow Type]]s, such as:
**** [[Datadog LLM RAG Pipeline Monitoring]] for [[datadog retrieval quality assessment]], [[datadog context relevance verification]], and [[datadog knowledge base coverage analysis]].
**** [[Datadog LLM Multi-Agent System Observability]] for [[datadog agent interaction tracing]], [[datadog task completion analysis]], and [[datadog agent collaboration efficiency]].
**** [[Datadog LLM Customer Service AI Monitoring]] for [[datadog sentiment analysis]], [[datadog conversation quality tracking]], and [[datadog resolution time optimization]].
** [[Datadog LLM Technical Integration]]s, such as:
*** [[Datadog LLM Hardware Monitoring]]s, such as:
**** [[Datadog LLM NVIDIA GPU Integration]] for [[datadog AI hardware utilization tracking]], [[datadog GPU memory monitoring]], and [[datadog thermal performance analysis]].
**** [[Datadog LLM AWS ML Chip Integration]] for [[datadog Trainium monitoring]], [[datadog Inferentia performance tracking]], and [[datadog ML accelerator optimization]].
*** [[Datadog LLM Vector Database Integration]]s, such as:
**** [[Datadog LLM Pinecone Integration]] for [[datadog vector storage monitoring]], [[datadog embedding quality assessment]], and [[datadog vector search performance]].
**** [[Datadog LLM Weaviate Integration]] for [[datadog semantic index monitoring]], [[datadog vector database health]], and [[datadog query performance tracking]].
** ...
* <B>Counter-Examples:</B>
** [[Standalone LLM Evaluation Tool]]s, which focus on [[model quality testing]] but lack [[datadog LLM operational monitoring capability]] and [[datadog platform integration]].
** [[Traditional Datadog APM Framework]], which provides [[application performance visibility]] but lacks [[datadog LLM-specific evaluation metric]]s and [[datadog AI semantic understanding]].
** [[Open-Source LLM Observability Stack]]s, which offer [[greater customization]] but require [[significant integration effort]] and lack [[datadog enterprise security feature]]s and [[datadog managed service reliability]].
** [[LLM Development Framework]]s, which support [[model building]] and [[fine-tuning]] but not [[datadog LLM production monitoring]] or [[datadog AI operational analytics]].
** [[Generic AI Monitoring Tool]]s, which provide [[basic AI performance tracking]] but lack [[datadog LLM-specific capability]]s such as [[datadog hallucination detection]] and [[datadog prompt injection protection]].
** [[Datadog Infrastructure Monitoring]] for [[LLM System]]s, which tracks [[hardware utilization]] but doesn't provide [[datadog semantic AI monitoring]] or [[datadog AI quality evaluation]].
* <B>See:</B> [[LLM Observability Framework]], [[Datadog Online Platform]], [[Enterprise AI Monitoring]], [[Generative AI Operations]], [[AI Observability Stack]], [[LLM Chain Tracing]], [[AI Security Assessment]], [[Token Usage Optimization]], [[Datadog AI Observability]], [[AI Governance Platform]].

----
----