Model Hubs & Serving

Xcessiv

Web-based hyperparameter tuning and stacked ensembling for scalable ML.

Status
UnmaintainedNo commits in 3292 days.
Last checked
Aging · Sep 5, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

Xcessiv is a web application designed to automate hyperparameter tuning and enable stacked ensembling, making it easier to scale machine learning models efficiently.

Xcessiv stands out by offering an intuitive web interface combined with powerful automated hyperparameter tuning and stacked ensembling capabilities, making it ideal for scalable ML projects.

When it fits — and when it doesn’t

A good fit for

  • Teams needing automated hyperparameter tuning for large datasets

  • Developers looking to quickly prototype and test ensemble models

  • Researchers who require a scalable solution for model deployment

Look elsewhere if

  • Projects requiring real-time parameter tuning or dynamic model updates

  • Applications where the overhead of web-based interface is undesirable

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited integrations with other ML libraries and frameworks

    Documentation highlights only a few supported integration points, lacking broad ecosystem support

  2. 02

    Performance bottlenecks during hyperparameter tuning at scale

    Users report slow execution times when handling large datasets or complex models

What it does well

  • Automated hyperparameter tuning

  • Support for stacked ensembling

  • Scalable machine learning model deployment

  • Web-based interface for ease of use

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up Xcessiv

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →