LLM observability

Large language model (LLM) observability made simple — track costs, latency, errors, and dependencies while ensuring safety and reliability.

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Monitor and optimize AI performance, cost, safety, and reliability

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Get the full picture with at-a-glance dashboards

Prebuilt dashboards for OpenAI, AWS Bedrock, Azure OpenAI, and Google Vertex AI offer comprehensive insights into invocation counts, error rates, latency, utilization metrics, and token usage, enabling SREs to identify and address performance bottlenecks, optimize resource utilization, and maintain system reliability.

Slow performance? Pinpoint root causes

Get full visibility into each step of the LLM execution path for applications integrating generative AI capabilities. Enable deeper debugging with end-to-end tracing, service dependency mapping, and visibility into LangChain requests, failed LLM calls, and external service interactions. Quickly troubleshoot failures and latency spikes to ensure optimal performance.

AI safety concerns? Get visibility into prompts and responses

Gain transparency into LLM prompts and responses to protect against data leaks of sensitive information, harmful or undesirable content, and ethical issues, as well as address factual errors, biases, and hallucinations. Support for Amazon Bedrock Guardrails and content filtering for Azure OpenAI enable policy-based interventions and provide contextual grounding to enhance model accuracy.

Trouble tracking costs? See usage breakdowns per model

Organizations need visibility into token usage, high cost queries and API calls, inefficient prompt structures, and other cost anomalies to optimize LLM spend. Elastic provides insights for multi-modal models, including text, video, and images, enabling teams to track and manage LLM costs effectively.

Get started with LLM observability

STEP 1: Ingest — we know what you need, no cleanup required
STEP 2: Instant dashboards — available out of the box in seconds
STEP 3: Create alerts — turn data into actions

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Visibility for GenAI apps

Use Elastic to gain end-to-end insight into AI applications through third-party tracing libraries as well as out-of-the-box visibility into models hosted by all major LLM services.