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W&B Weave

by Weights & Biases

LLM tracing and evaluation framework from Weights and Biases — enabling APAC ML teams to trace LLM pipelines, evaluate outputs with custom scorers, and track LLM experiment quality within the same W&B workspace used for traditional APAC ML model experiments.

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AIMenta verdict
Decent fit
4/5

"LLM evaluation and tracing by Weights and Biases — APAC ML teams use W&B Weave to trace LLM calls, evaluate model outputs with scorers, and track APAC LLM experiment quality alongside existing ML model experiments in one platform."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Auto-tracing: @weave.op() APAC decorator for zero-instrumentation LLM call capture
  • W&B integration: APAC LLM metrics alongside traditional ML experiment tracking
  • Evaluation leaderboard: APAC model/prompt/retrieval version quality comparison
  • Custom scorers: APAC team-defined evaluation functions for domain criteria
  • Trace tree: APAC nested pipeline visualization from query to generation step
  • Free tier: APAC development and low-volume evaluation without subscription
When to reach for it

Best for

  • APAC ML engineering teams already using Weights & Biases for model training who are building LLM-powered applications — particularly APAC teams that want unified experiment tracking across both traditional ML and LLM development without adopting a separate APAC LLM observability platform.
Don't get burned

Limitations to know

  • ! W&B-centric — APAC teams not using W&B face adoption friction for Weave alone
  • ! Less opinionated than dedicated APAC LLM platforms (Langfuse, Humanloop) for LLM-specific workflows
  • ! APAC data residency: W&B Weave is cloud-only — on-premise not available
Context

About W&B Weave

W&B Weave is the LLM tracing and evaluation framework from Weights & Biases — providing APAC ML teams with LLM-native observability that integrates directly with existing W&B experiment tracking workflows. APAC teams already using W&B for traditional ML model experiments (training curves, hyperparameter sweeps, model comparison) use Weave to add LLM tracing and evaluation within the same platform, avoiding context switching between multiple APAC monitoring tools.

Weave's auto-tracing captures APAC LLM call inputs, outputs, latency, and token usage with a single decorator — `@weave.op()` on any APAC Python function automatically logs its inputs and outputs to Weave without manual instrumentation of every LLM call. Nested APAC function calls create trace trees showing the full APAC pipeline execution, enabling drill-down from high-level RAG query to individual retrieval and generation steps.

Weave's evaluation framework runs APAC custom scorer functions over logged traces — APAC teams define scoring functions (semantic similarity, keyword presence, LLM-as-judge) that Weave applies to evaluation datasets and displays in a comparison leaderboard. This leaderboard enables APAC teams to compare model versions, prompt variations, and retrieval strategies quantitatively within W&B's familiar experiment comparison UI.

Weave's W&B integration gives APAC ML teams a unified view of model quality across the ML development lifecycle — training metrics for APAC base models and fine-tuning runs appear alongside LLM application quality metrics from Weave, enabling APAC ML engineers to correlate upstream model changes with downstream APAC application quality impacts in a single dashboard.

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