Respan vs Weights & Biases (2026)
Updated March 9, 2026
Overview
Respan is LLM observability built into an AI router: nested traces for every agent run, evals on live production traffic, and a click from any score to its trace.
Weights & Biases (W&B) is the leading experiment tracking and ML operations platform, now extended to LLM applications. W&B Traces provides observability for LLM pipelines, while W&B Weave offers evaluation and production monitoring. The platform also supports model training tracking, hyperparameter sweeps, and artifact management, making it a comprehensive MLOps solution.
Key features
Core capabilities each platform advertises.
- Nested traces for every LLM call, tool run, and agent turn
- Threads for multi-turn sessions
- Online evals on live production traffic
- LLM-judge, code, and human evaluators
- Datasets from production logs and experiments at scale
- Monitors with Slack, email, and webhook alerts
- Prompt versioning and deployment without code changes
- ML experiment tracking
- Model and dataset versioning
- Collaborative dashboards
- Sweeps for hyperparameter tuning
- Prompt monitoring and evaluation
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Every score links to the trace that produced it
- Evals run on live production traffic, not only on test sets
- Built into the router, so there's no separate gateway to integrate
- Monitors alert the moment a metric breaches
- Unlimited seats on the free plan
Cons
- The free plan retains data for 7 days
- The free plan includes 1k scores and 2 evaluators
- Ingestion throughput is metered per tier, starting at 412 logs per minute on Free
Pros
- Free tier for personal projects and academic research provides excellent value
- Comprehensive experiment tracking and model versioning capabilities
- Strong visualization tools help teams understand model performance
- Acquisition by CoreWeave provides infrastructure synergies and stability
Cons
- Pricing can become expensive for large teams and enterprises
- Additional storage costs can add up quickly for data-intensive workflows
- Learning curve for advanced features may slow initial adoption
- Platform complexity may be overkill for simple ML projects
Respan or Weights & — which should you choose?
Choose Respan if you wantChoose if you want
- Debugging agent failures down to the step that broke
- Catching quality regressions before users report them
- Turning production failures into test datasets
- Testing a prompt or model change against real cases before shipping
- Attributing LLM cost to each end customer
Choose Weights & if you wantChoose if you want
- ML experiment tracking and comparison
- Model training run management
- Team collaboration on ML projects
- Hyperparameter optimization
- Model registry and versioning
Compare Respan and Weights & Biases on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.
Try Respan free