Ship generative video features with confidence
Frametail is the all-in-one evaluation and observability platform for building with AI generated video
Why evaluations matter for AI video
AI video generation presents unique challenges that make systematic evaluation essential.
Every generation is different
Video models are inherently stochastic, producing different outputs for the same prompt on each run. This non-deterministic nature means you can't rely on a single generation to judge quality or consistency.
Every generation is costly
Each video run consumes significant compute and budget. You can't afford to guess which prompts or models work — failed runs and re-runs add up fast. Systematic evaluation lets you compare options, catch issues early, and scale only what actually works.
Every generation is opaque
You can't peer inside the model to see why it produced a given frame or sequence. Without interpretable internals, the only way to understand quality, alignment, and failure modes is through systematic evaluation of the outputs themselves.
Everything you need to understand and improve your generative video pipeline
Evaluate
Run custom scorers using OpenAI, Anthropic, and Gemini models to assess video quality across multiple dimensions.
Observe
Monitor evaluations across entire datasets with real-time progress tracking, aggregated results, and comprehensive cost analytics.
Iterate
Compare versions, identify quality gaps, and systematically improve your prompts and model configurations based on evaluation insights.
The video evaluation platform that grows with you
Frametail helps teams of all sizes evaluate AI-produced video quality with precision and scale. Whether you're building a prototype or testing a large dataset, we can provide accurate assessments and observability.
Ready to evaluate AI video quality at scale?
Join research teams and production companies using Frametail to assess AI-generated video quality with precision, automation, and comprehensive analytics.