Frametail

Applied ML engineers

Eval workflows built for ML engineers on fal

Traces that show the clip, benchmarks that stay immutable, and a path from experiments to linkable release artifacts.

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Common pain points

  • Traces are noise without video artifacts attached
  • Playground experiments do not persist into comparable runs
  • Regressions surface late because eval lives in ad-hoc scripts
  • PMs ask for proof you cannot link to

Day one: trace your fal pipeline

Wrap `@fal-ai/client` with the Frametail SDK and enable tracing. Every generation exports spans with latency, inputs, outputs, and errors. You debug in the same UI your PM can read — not a JSON dump in a terminal.

See the integration guide at `/docs/integrations/fal` for setup.

Week one: pin a dataset and scorers

Pull representative rows from staging or production traces. Attach scorers your team agrees on — automated VQA, rubric prompts, or hybrid human spot-checks. Run a benchmark before you change model endpoints.

Release week: lock a benchmark

Use experiments to try prompt and parameter changes. When direction is approved, run the evaluation workflow to create a benchmark. Paste the benchmark link in the PR or release doc so reviewers can open the run.