Weights & Biases

Weights & Biases (W&B) costs $50 per user/month for the Pro plan, plus usage fees for its GenAI module, "Weave." While it remains the undisputed heavyweight champion for tracking model training experiments, its expansion into LLM observability comes with a premium price tag and a distinct split in maturity between its Python and TypeScript offerings.

For a team of five engineers building a RAG application, the base math starts at $250/month. This covers your dashboard and 500 tracked training hours, but the real variable is Weave ingestion. If your application logs heavily—say, full prompt/completion traces for a high-traffic agent—the costs can spiral. W&B includes a small allowance (around 1.5GB in some tiers), but overages are reportedly steep, sometimes calculated around $0.10/MB or packaged in high-five-figure annual commitments for enterprise data volumes. Unlike standard log aggregators like Datadog that charge pennies per GB, Weave prices data like it's gold dust.

The platform feels like a dual-cockpit jet. On the left, you have the traditional W&B experiment tracker: robust, beautiful, and essential for anyone fine-tuning Llama or Mistral models. On the right is Weave: a newer, code-first canvas for tracing and evaluating LLM calls. The visualization engine is Weave's superpower; you can drill down into a latency spike, view the exact prompt version, and replay the trace with a click. It handles complex, nested agent loops better than almost anything else on the market.

However, the polish wears thin if you leave the Python ecosystem. The TypeScript/Node.js SDK trails significantly behind, missing key features like custom cost tracking and advanced query capabilities found in the Python client. Additionally, the UI can feel overwhelming. W&B was built for data scientists who want to see every hyperparameter; for an application engineer just wanting to know why a user got a 500 error, the density of information can be paralyzing.

Skip W&B if you are a pure application developer wrapping APIs without any model training needs—LangSmith or Arize Phoenix offer better value and focus for that persona. Use W&B if you are an ML engineering team that needs a single source of truth for both your fine-tuning experiments and your production LLM traces, and you have the budget to pay for the best visualization in the game.

Pricing

The "Free" tier is decent for solo students but restrictive for startups: you get 1 user, 100GB of storage (for training artifacts), but very limited Weave ingestion (often capped at 1-2GB). The real cliff is the $50/user/month Pro tier, which is mandatory for teams >1. Unlike usage-based competitors where you pay $0 until you scale, W&B demands an upfront seat tax. Furthermore, Weave's data ingestion overages are notoriously opaque and expensive compared to competitors like LangSmith, which charges clearly per trace (e.g., $0.50/1k traces). Be extremely careful with logging verbose payloads in Weave; 100GB of text logs could theoretically cost thousands of dollars if billed at list ingestion rates.

Technical Verdict

The Python SDK is excellent—unintrusive decorators (@weave.op) make instrumentation trivial, and the integration with major libraries (OpenAI, LangChain) is solid. Data reliability is high, but the UI can become sluggish with massive trace volumes. TypeScript support is a second-class citizen, often lacking parity with Python features. Documentation is pretty, but sometimes fragments between the "old" W&B and the "new" Weave paradigms.

Quick Start

import weave
from weave import op
 
weave.init("my-project")
 
@op()
def hello(name: str):
    return f"Hello {name}"
 
print(hello("Dave"))

Watch Out

• Weave ingestion costs are extremely high ($100s/GB equivalent) compared to standard logging tools.
• TypeScript SDK lacks feature parity with Python (e.g., missing custom cost tracking).
• The UI is dense; finding a specific trace among thousands of training runs can be confusing.
• Free tier is strictly single-user; no collaboration without paying $50/month/user.