LLM Observability

LLM Observability extends KubeSense's monitoring capabilities to Large Language Model (LLM) applications, providing visibility into AI/ML inference pipelines, token usage, latency, and cost.

Overview

As organizations integrate LLMs into production applications, monitoring these AI systems becomes critical. LLM Observability helps you:

• Track inference performance — Monitor latency, throughput, and error rates for LLM API calls
• Monitor token usage — Track input and output token consumption across models and endpoints
• Cost visibility — Understand the cost of LLM operations across your application
• Quality monitoring — Track response quality metrics and detect regressions

Planned Capabilities

LLM Traces

End-to-end tracing of LLM request pipelines, including:

• Prompt construction and preprocessing
• Model inference time
• Post-processing and response delivery
• Embedding generation and vector store interactions

LLM Metrics

Key performance indicators for LLM workloads:

Model Comparison

Compare performance across different LLM providers and models:

• Latency comparison between models
• Cost efficiency analysis
• Error rate benchmarking

Prompt Analytics

Analyze prompt patterns and their impact on performance:

• Prompt length distribution
• Token efficiency
• Cache hit rates for repeated prompts

Supported Integrations

LLM Observability is designed to work with:

• OpenAI API
• Anthropic Claude API
• AWS Bedrock
• Google Vertex AI
• Self-hosted models (vLLM, Ollama, TGI)
• LangChain and LlamaIndex frameworks

Getting Started

LLM Observability will be accessible from the LLM Observability item in the sidebar once the feature is generally available. Stay tuned for updates.