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The Inference Viewer is a filterable table of every LLM call that flows through Catalyst. Use it to inspect individual requests, debug issues, and find samples to save as datasets for evals and training.
Inference Viewer

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Table columns

Each row in the table represents a single LLM call. The visible columns are configurable, and include: You can sort by time, status, cost, duration, tokens, or payload size.

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Filtering

The filter builder lets you combine multiple conditions to narrow down your traffic. Filters are available for both categorical and numeric fields. Categorical filters:
  • Model - filter to specific models
  • Provider - filter by upstream provider
  • Task - filter by task ID
  • Environment - filter by environment tag
  • Status - filter by HTTP status code or range (success, error, 2xx, 4xx, 5xx, or specific codes like 429)
  • Streaming - filter streaming vs non-streaming requests
Numeric filters:
  • Duration - filter by latency (e.g. requests slower than 5s)
  • Cost - filter by cost (e.g. requests costing more than $0.05)
  • Input / Output Tokens - filter by token count (e.g. input > 5k tokens)
  • Request / Response Size - filter by payload size in bytes
Quick filters are available for common queries: input tokens > 5k, cost > $0.05, duration > 5s, and status = error.

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Detail view

Click on any row to open the detail panel. This shows the full picture of a single inference:
  • Full request and response payloads (viewable as raw JSON)
  • Cost breakdown (input, output, reasoning, cached)
  • Token breakdown (input, output, reasoning, cached) with visual bars
  • Duration and time to first token
  • Model, provider, task, and environment
  • Streaming status
  • Request metadata (key-value pairs)
  • Geolocation (country, city)

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Save as dataset

You can build datasets from live traffic directly in the Inference Viewer or from the Datasets tab. Apply filters to get a representative slice of your data, then click Save as Dataset to create an eval or training dataset from the filtered results. The dataset creation flow:
  1. Apply your filters to narrow down the traffic
  2. Review the matching inferences
  3. Optionally set a limit on how many inferences to include
  4. Choose whether this is an eval dataset or a training dataset
  5. Name the dataset and save
The saved dataset is immediately available for running evals or training a model.
Use task tags to filter by objective before saving a dataset. This gives you clean, focused samples instead of a mix of unrelated traffic.

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Next steps

Build a dataset from traffic

Step-by-step guide for turning filtered traffic into datasets.

Set up your first eval

Use your dataset to compare models with rubric-based scoring.

Upload a dataset

Already have data? Upload a JSONL file directly.

Organize with tasks

Group calls by objective for better filtering and per-feature metrics.