Agentifact assessment — independently scored, not sponsored. Last verified Mar 8, 2026.
W&B Weave
Evaluation and tracing toolkit by Weights & Biases for GenAI applications. Automatically captures LLM call inputs, outputs, costs, and latency via the @weave.op decorator, then evaluates against datasets using custom or pre-built scorers measuring accuracy, latency, and cost. Supports side-by-side experiment comparison and CI/CD integration. Free tier with paid usage-based scaling.
Viable option — review the tradeoffs
You need to systematically measure whether your LLM application outputs are accurate, fast, and cost-effective across multiple model versions or prompt changes.
Fast iteration loops with visual comparison dashboards. Scorers run sequentially per example, so evaluation time scales with dataset size and scorer complexity. The UI is strong for spotting performance deltas and outliers, but you'll need to write custom scorers for domain-specific metrics—no pre-built LLM-as-judge scorers out of the box.
You're running A/B tests on LLM prompts or models and need to quickly identify which examples show the biggest performance differences between variants.
Excellent for spotting regressions and discovering novel behavior in challenger models. The visual scatter plot makes it easy to find edge cases. Expect to spend time manually reviewing flagged examples—no automated root-cause analysis.
You need to track what's happening inside your LLM application (inputs, outputs, latency, token costs) in production or during development without rewriting your code.
Lightweight instrumentation with minimal overhead. Traces are queryable and filterable in the UI. Useful for debugging unexpected outputs or understanding cost drivers. Not a replacement for full observability platforms—focused on LLM-specific signals.
No pre-built LLM-as-judge scorers
You must write custom scoring functions for most evaluation logic. The docs show examples of exact-match scorers, but semantic similarity, hallucination detection, or LLM-based grading require you to implement the logic yourself (including LLM API calls and parsing).
Scorer latency compounds with dataset size
If you define multiple scorers and run them on a large dataset, evaluation time grows linearly with (dataset size × number of scorers). Each example is scored sequentially. For 1000 examples with 3 scorers that each call an LLM, expect minutes to hours depending on LLM latency.
Trust Breakdown
What It Actually Does
W&B Weave tracks calls to large language models in your AI apps, logging inputs, outputs, costs, and speed. It lets you evaluate performance against test data using built-in or custom checks for things like accuracy and quality.
Evaluation and tracing toolkit by Weights & Biases for GenAI applications. Automatically captures LLM call inputs, outputs, costs, and latency via the @weave.op decorator, then evaluates against datasets using custom or pre-built scorers measuring accuracy, latency, and cost. Supports side-by-side experiment comparison and CI/CD integration.
Free tier with paid usage-based scaling.
Fit Assessment
Best for
- ✓llm-evaluation
- ✓tracing
- ✓monitoring
- ✓cost-tracking
Score Breakdown
Protocol Support
Capabilities
Governance
- guardrails
- audit-log
- tracing