Forefront

Forefront is a platform for fine tuning and serving open source language models. Developers upload their own data, train a model on it, check accuracy against a validation set and evaluation suites, test the result in a playground, then call it from a serverless API endpoint using a familiar chat or completion interface. It is aimed at teams that want the convenience of a hosted closed model provider without giving up control of the weights. Training progress is visible through loss charts, and built in evaluations cover common public benchmarks. Models can be imported from Hugging Face by pasting a model string, and exported so they can be self hosted or run with another provider. A data layer stores API responses from an existing provider, so a team can accumulate a training set first and fine tune later. The vendor frames the pitch around avoiding deprecated models, inconsistent performance and arbitrary usage policies. Pricing is published. Every plan includes $20 in free credits. Free costs $0 per month and allows one member, three fine tuned models, datasets up to 10 KB and three dataset uploads. Team costs $99 per month and raises those to five members, ten models, 1 MB datasets and ten uploads. Enterprise is custom priced, lifts members, models and uploads to unlimited with 1 GB datasets, and adds single sign on, service level agreements, self hosting and dedicated support. Usage is metered at $0.001 per 1,000 tokens for inference and $0.008 per 1,000 tokens for fine tuning. Worth knowing: the site still labels the product a beta, and the footer carries a 2024 copyright.