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AI Observability

AI Observability

End-to-end observability for your Generative AI, agentic, and LLM services.

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AI Observability overviewOverview of all of our integrationsCost and model performance debuggingEnd-to-end tracingCompliance dashboardOpenAI end-to-end ObservabilityAmazon Bedrock dashboard overview
  • Product information
  • Release notes

Overview

Businesses are adopting generative GenAI services for innovation, using client-side processing and semantic searches over domain-specific knowledge bases. Various tools use large language models (LLMs) to create AI agents that perform complex queries and integrate with various third-party services, and one of the success factors for businesses is efficient cost tracking and management.

Dynatrace provides a solution for tracking and revealing the full context of used technologies, service interaction topology, analyzing security vulnerabilities, and observing metrics, traces, logs and business events in real-time – all done automatically and applicable to all layers of AI Observability:

  • Model and Agent Layer: Amazon Bedrock, OpenAI, Azure AIFoundry, Google Vertex AI, Google Gemini, Mistral, NVIDIA NIM, DeepSeek, etc.
  • Orchestration Layer: Langchain, LlamaIndex, LLM, etc.
  • Vector Database/Semantic Search: LanceDB, Pinecone, Weaviate, Qdrant etc.
  • Infrastructure Layer: NVIDIA, Google TPU, Amazon EC2, TensorFlow Keras, etc.

AI and LLM Observability provide a comprehensive overview showing these AI services' consumption, latency, availability, response time, error count, and request quantity.

Prepare for seamless agent-to-agent observability with real-time insights and comprehensive AI layer monitoring.

You can access all these capabilities through customizable, ready-made dashboards tailored to your needs.

Use cases

Monitor service health and performance

  • Detect bottlenecks by tracking real-time metrics, including request counts, durations, and error rates.
  • Manage service costs with automated cost calculations for each request.
  • Stay on track with SLOs

Monitor service quality and cost

  • Set up error budgets for performance and cost controls
  • Validate consumption and response time by models
  • Prevent quality degradation by validating models and usage patterns in real-time.

End-to-end tracing and debugging

  • Achieve complete visibility of prompt flows, from initial request to final response, for faster root cause analysis.
  • Capture detailed debug data to troubleshoot issues in complex pipelines.
  • Streamline your workflows with granular tracing of LLM prompts, including response latency and model-level metrics.
  • Resolve issues quicker by pinpointing exact problem areas in prompts, tokens, or system integrations.

Build trust while reducing compliance and audit risks

  • Track every input and output for an audit trail
  • All data can be queried in real-time and stored for future reference.
  • Maintain full data lineage from initial prompt to response output

Get started

Setting up full stack observability for your GenAI applications is possible with Traceloop's OpenLLMetry, with OpenTelemetry under the hood, which can seamlessly provide comprehensive end-to-end insights into your production environments.

To set up OpenLLMetry with Dynatrace, see Dynatrace Documentation.

You can access all these capabilities through customizable, ready-made dashboards tailored to your needs.

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By Dynatrace
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More resources

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Full version history

ReleaseDate

Full version history

2.0.8

Patch Changes

  • Disable segments selection if the user doesn't have permissions
  • Improve integration with other Apps
  • Improve DQLs covering edge cases

2.0.7

Patch Changes

  • Fix the problem of missing Ready-made Dashboards

2.0.6

Patch Changes

  • Improve App performance
  • Improve support for OpenTelemetry

2.0.5

Patch Changes

  • Fixed onboarding URL handling and example values for OpenLLMetry
  • Fixed Overview page calculation inconsistencies

2.0.4

Patch Changes

  • Fixes an issue where the "Overall average cost per request" would be miscalculated when there are no requests.

2.0.3

Patch Changes

  • Fixed token generation error handling.
  • Fixed cost calculation on the overview section.
  • Fixed onboarding video rendering.

2.0.1

Major Changes

  • Observability for 20+ AI, Agent and LLM technologies
  • Overview of your multi-cloud and multi-ai workloads setup and visibility into performance, cost, errors, latency metrics.
  • Visibility into model providers, LLMs and Agent calls, prompts, metrics, guardrails and traces.
  • Intelligent alerting for AI workloads
  • Dedicated onboarding experience for OpenTelemetry and Cloud Monitoring
  • Ready-made dashboards for AWS, Azure, OpenAI, NVIDIA, Kong, Model versioning and A/B testing

Full version history

2.0.7

Patch Changes

  • Fix the problem of missing Ready-made Dashboards

Full version history

2.0.6

Patch Changes

  • Improve App performance
  • Improve support for OpenTelemetry

Full version history

2.0.4

Patch Changes

  • Fixes an issue where the "Overall average cost per request" would be miscalculated when there are no requests.

2.0.3

Patch Changes

  • Fixed token generation error handling.
  • Fixed cost calculation on the overview section.
  • Fixed onboarding video rendering.

2.0.1

Major Changes

  • Observability for 20+ AI, Agent and LLM technologies
  • Overview of your multi-cloud and multi-ai workloads setup and visibility into performance, cost, errors, latency metrics.
  • Visibility into model providers, LLMs and Agent calls, prompts, metrics, guardrails and traces.
  • Intelligent alerting for AI workloads
  • Dedicated onboarding experience for OpenTelemetry and Cloud Monitoring
  • Ready-made dashboards for AWS, Azure, OpenAI, NVIDIA, Kong, Model versioning and A/B testing

Full version history

2.0.3

Patch Changes

  • Fixed token generation error handling.
  • Fixed cost calculation on the overview section.
  • Fixed onboarding video rendering.

2.0.1

Major Changes

  • Observability for 20+ AI, Agent and LLM technologies
  • Overview of your multi-cloud and multi-ai workloads setup and visibility into performance, cost, errors, latency metrics.
  • Visibility into model providers, LLMs and Agent calls, prompts, metrics, guardrails and traces.
  • Intelligent alerting for AI workloads
  • Dedicated onboarding experience for OpenTelemetry and Cloud Monitoring
  • Ready-made dashboards for AWS, Azure, OpenAI, NVIDIA, Kong, Model versioning and A/B testing

Full version history

2.0.1

Major Changes

  • Observability for 20+ AI, Agent and LLM technologies
  • Overview of your multi-cloud and multi-ai workloads setup and visibility into performance, cost, errors, latency metrics.
  • Visibility into model providers, LLMs and Agent calls, prompts, metrics, guardrails and traces.
  • Intelligent alerting for AI workloads
  • Dedicated onboarding experience for OpenTelemetry and Cloud Monitoring
  • Ready-made dashboards for AWS, Azure, OpenAI, NVIDIA, Kong, Model versioning and A/B testing

Full version history

1.3.1

Patch Changes

  • Fix handling the case of no data available

1.3.0

Minor Changes

  • Ready-made dashboard for model versioning and A/B testing of prompts
  • Ready-made dashboards for AI service compliance and audit trails
  • Ready-made dashboard for Kong AI Gateway

Full version history

1.1.2

Patch Changes

  • a50eb79: Align naming

1.1.0

Minor Changes

  • 3288a76: Ready-Made dashboard for Google Gemini and Vertex AI Studio
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