Trace Sampling

Full definition

Two strategies: head-based sampling decides at request start (cheap but biased — misses rare errors); tail-based sampling buffers traces and keeps based on outcome (catches errors and slow tails, but more expensive). For LLM apps, the typical configuration is 100% sampling on errors and high-latency requests, plus 1–10% baseline. Tools: OpenTelemetry, Langfuse, Arize Phoenix, Helicone, LangSmith.

Why it matters

Naive 100% trace logging on a high-volume LLM product can cost more than the inference itself, especially when prompts and outputs are long. Smart sampling cuts observability cost 10–100x while keeping the traces that actually matter for debugging.

Example

A SaaS team logs every LLM call to Langfuse and burns $14k/mo on storage; switching to tail sampling (100% errors, 10% baseline) drops cost to $2k/mo with no loss of debugging capability.

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Related terms

• Prompt Observability
  Prompt observability is the practice of capturing, indexing, and analyzing every prompt, response, tool call, latency, token-count, and quality metric in an LLM application — for debugging, evaluation, audit, and continuous improvement.
• MLOps
  MLOps is the discipline of operating machine-learning and AI systems reliably in production — covering data pipelines, model training, deployment, monitoring, drift detection, governance, and incident response — analogous to DevOps for traditional software.
• Model Monitoring
  Model monitoring is the continuous measurement of a deployed model's inputs, outputs, and performance signals — including data drift, prediction drift, label drift, latency, and ground-truth accuracy — to detect degradation before it harms business outcomes.
• Tail Latency (P95/P99)
  Tail latency is the latency at high percentiles (P95, P99, P99.9) of the response-time distribution — the part of the experience that drives user frustration and SLA violations, even when median latency is fine.

More deployment & operations terms

• Retrieval-Augmented Generation (RAG)
  Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from an external knowledge base before generating a response, which reduces hallucination and enables citation of sources.
• Vector Database
  A vector database is a database optimized for storing and searching high-dimensional vectors (embeddings) by similarity rather than by exact match — the storage layer of every RAG and semantic-search system.