What Each Tool Is For

W&B Weave is the LLM evaluation layer of the Weights & Biases platform. If your team already uses W&B for ML training — experiment tracking, model versioning, artefact management — Weave extends that into LLM traces, evaluations, and feedback. It's a natural fit for teams with existing W&B infrastructure.

DriftWatch solves a different problem: what happens after deployment. When OpenAI, Anthropic, or Google update a model, your prompts may return different outputs — different format, different instruction compliance, different verbosity — without any error or warning. DriftWatch runs your production prompts on a schedule and alerts you the moment something changes, before your users notice.

Feature Comparison

Feature	DriftWatch	W&B Weave
Silent model update detection	✓ Core feature	✗ Not built for this
Scheduled hourly prompt runs	✓ Automatic	✗ Manual / CI triggered
Baseline vs current comparison	✓ Automatic	◐ Manual via eval datasets
Slack/email drift alerts	✓ Included	◐ Via W&B notifications
Free tier (no card)	✓ 3 prompts	✓ Free tier available
ML experiment tracking	✗ Out of scope	✓ Core W&B feature
LLM call tracing	✗ Not the focus	✓ Built-in
Existing W&B users	◐ Works standalone	✓ Native integration
Works without W&B account	✓ Standalone	✗ Requires W&B
Setup time for drift alerting	✓ 5 minutes	◐ Hours (eval setup)

When to Use W&B Weave

• Your team already uses W&B for ML training and wants a unified platform
• You need LLM call tracing and logging integrated with your existing W&B dashboards
• You want to run evaluations as part of your training/fine-tuning pipeline
• You need model artefact versioning alongside LLM prompt management
• Your team has ML engineers who are comfortable with the W&B ecosystem

When to Use DriftWatch

• You need to know immediately when a model provider pushes an update that changes your outputs
• You're an app developer (not an ML team) who just wants to monitor their LLM API calls
• You don't use W&B and don't want to add it to your stack just for LLM monitoring
• Your LLM calls are business-critical parsers or classifiers where format changes break things silently
• You want a standalone SaaS — register, add prompts, start monitoring in 5 minutes

The Production Monitoring Gap

W&B Weave is excellent during development. But it assumes you're actively running evaluations — it doesn't continuously check whether your production prompts are still behaving the same way.

Here's what can go wrong without continuous production monitoring: a model update causes your single-word classifier to return "Neutral." instead of "Neutral". One trailing period. json.loads() still works. Your tests still pass (they check format, not exact output). But any downstream code doing exact-match comparison silently starts misfiring.

In our own test run — same model, two consecutive calls, no update between them — we measured a drift score of 0.575 on this exact pattern. That's the class of regression DriftWatch catches automatically, on a schedule, without you having to think about it.

Using Both Together

The tools complement rather than compete:

• Development phase: W&B Weave for tracing, eval datasets, experiment comparison
• Production phase: DriftWatch for continuous behavioral monitoring, drift alerts

If you're already in the W&B ecosystem, DriftWatch adds the one layer W&B doesn't cover: scheduled hourly regression checks against your production baseline with instant alerts when something drifts.