Frontend Regression Validator
Frontend Regression Validator (FRED) is an open-source tool for comparing a baseline website with an updated version.
What Frontend Regression Validator is
Frontend Regression Validator (FRED) is an open-source tool for comparing a baseline website with an updated version. Give it two URLs and it crawls them to find pages to render and compare. It checks screenshots alongside console and network logs, with optional machine-learning analysis of images. Its segmentation analysis identifies broad text and image structures to help reduce false positives from dynamic content. You can use FRED through a web interface or API, and it saves results locally with divergence scores and links to raw and analysis images. An internal queue allows parallel processing, subject to available RAM, CPUs, or GPUs. The project documents running it as a Docker container or a local process. The README recommends at least 8 GB of Docker memory, preferably 16 GB when using machine learning. Crawl duration varies with site complexity; the documented machine-learning rule of thumb is one minute or less per page. Version 2.x has no model training or retraining code; the repository points to its v1 folder for that code. FRED is free under Apache License 2.0.
Good
- Compares screenshots, console logs, and network logs.
- Machine-learning image analysis can reduce dynamic-content false positives.
- Offers both a web interface and an API.
- Saves divergence scores and links to analysis images.
- Free, open-source software under Apache License 2.0.
Watch out
- Docker use requires at least 8 GB memory, preferably 16 GB.
- Version 2.x does not include model training or retraining code.
- Crawl time varies with site complexity.
Who it suits
FRED suits teams that need to compare website versions and can run a local or Docker deployment. Its API and parallel processing may fit workflows that need automated or multi-site comparisons.
Verdict
FRED combines visual comparison with console and network-log checks, and provides both web and API access. Consider its memory recommendations and the lack of model-training code in version 2.x before choosing it.
Plans and prices
From Frontend Regression Validator's own pricing page, checked 4 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Open-source | Free | Apache-2.0 licensed software · run locally or with Docker · no paid plans stated |
The facts
Each one read from Frontend Regression Validator's own pages, with the date it was checked.
What it does
- Checks
- It compares console and network logs, screenshots, and optionally screenshots using machine-learning analysis.checked 4 Oct 2026
- Deployment
- The repository documents running FRED as a Docker container or as a local process.checked 4 Oct 2026
- Integrations
- The README describes API calls and Docker deployment but does not name third-party integrations.checked 4 Oct 2026
- Interfaces
- Users interact with FRED through a web UI or API.checked 4 Oct 2026
- License
- The repository includes an Apache License 2.0, which grants no-charge, royalty-free rights subject to its terms.checked 4 Oct 2026
- Model training
- Version 2.x does not include code to train or retrain the machine-learning model; the repository points users to the v1 folder for that code.checked 4 Oct 2026
- Purpose
- FRED is an open-source visual regression tool for automatically comparing baseline and updated website instances.checked 4 Oct 2026
- Resource needs
- The README recommends at least 8 GB of Docker memory and preferably 16 GB, especially when using machine learning.checked 4 Oct 2026
- Results
- Results are saved locally and include divergence scores and links to raw and analysis images.checked 4 Oct 2026
- Runtime
- The documented rule of thumb for a machine-learning-enabled crawl is one minute or less per page, while crawl time varies with site complexity.checked 4 Oct 2026
- Scalability
- FRED has an internal queue and can process websites in parallel depending on available RAM, CPUs, or GPUs.checked 4 Oct 2026
- Visual AI
- Its image-segmentation analysis identifies high-level text and image structures to reduce false positives from dynamic content.checked 4 Oct 2026
- Workflow
- A comparison starts with two URLs, which FRED crawls to find pages to render and compare.checked 4 Oct 2026
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