Weights & Biases Weave vs Helicone for LLM tracing

Here’s the practical comparison:

Quick take

• Use Weights & Biases Weave if you want LLM app observability tightly integrated with the W&B ecosystem, plus evaluation, dataset/version tracking, and more of an MLOps-style workflow.
• Use Helicone if you want a drop-in LLM gateway/proxy for tracing, cost, latency, prompt logging, and analytics with very fast setup and strong API-provider-centric observability.

Core difference

Weave

• More of an application + experiment tracking platform for LLM workflows.
• Strong for:
  • tracing chains/agents/tools
  • logging inputs/outputs
  • evals and datasets
  • prompt/version iteration
  • linking traces to experiments/runs

Helicone

• More of an LLM observability layer and proxy.
• Strong for:
  • request/response logging
  • latency, token, and cost analytics
  • multi-provider monitoring
  • minimal code changes
  • traffic-level insight across OpenAI/Anthropic/etc.

Feature comparison

Area	Weave	Helicone
Setup	SDK integration	Often proxy-based, very quick
Tracing	Strong for app-level traces	Strong for API request tracing
Evals	Better built-in workflow	More limited / externalized
Cost tracking	Available, but not main focus	Excellent
Prompt/version tracking	Strong	Good
Agent/tool tracing	Strong	Moderate
Dashboarding	Good, especially with W&B	Strong for ops/usage analytics
Open-source / portability	More tied to W&B	Easy to adopt as an observability layer
Best for	Iteration + eval + experiment workflow	Production monitoring + cost analytics

Choose Weave if:

• You’re already using Weights & Biases.
• You care about systematic evaluation of prompts/models.
• You want to compare runs, datasets, and outputs.
• You’re building complex chains/agents and want traceability tied to development.

Choose Helicone if:

• You want fast production tracing with minimal integration work.
• You care most about token usage, latency, errors, and cost.
• You use multiple LLM vendors and want one place to monitor them.
• You prefer a proxy-style approach rather than instrumenting every call deeply.

A common pattern

Some teams use both:

• Helicone for production request logging and cost/latency monitoring
• Weave for deeper debugging, prompt iteration, and evals in development

My recommendation

• If your primary question is “How do I monitor and understand LLM API traffic in production?” → Helicone
• If your primary question is “How do I build, trace, and evaluate LLM applications during development?” → Weave

If you want, I can also give you:

1. a decision tree based on your use case, or
2. a side-by-side comparison for OpenAI/Anthropic tracing specifically.