Final takeaway: ModelBrew combines continual learning, data sanitation, and a verifiable fact layer to help organizations evolve AI systems responsibly, with a flexible pricing model and a free tier for evaluation.

The platform offers API, SDK, and interfaces with REST APIs (including OpenAI-compatible inference), plus Python and JavaScript/TypeScript SDKs, along with tools for prompt regression testing, drift monitoring, inference serving, and workspace dataset inspection and cleaning.

Open-Weight Model Support and Exportability enables fine-tuning of 17 production-grade open-weight models (1.1B to 14B parameters) and exporting adapters/models in formats compatible with Hugging Face, llama.cpp, vLLM, Ollama, and similar ecosystems, with license attribution.

Live Knowledge Layer creates a separate, erasable fact store distinct from model weights, allowing instant addition, updates, or erasure of facts, verifiable erasure certificates, and support for temporal queries and audit logs.

Continual Fine-Tuning with CRMA enables sequential training across multiple domains with near-zero drift, preserving earlier task performance on models like Mistral-7B and Gemma-2-9B.

Target users include regulated industries (legal, healthcare, financial), policy-driven operations needing adaptable disclaimers, AI/ML practitioners requiring cross-domain models, and organizations focused on data security and governance (no training on raw user queries; strong deletion and export controls).

Dataset Cleaning and Quality Assurance features automatically flag and fix issues such as duplicates, encoding errors, prompt-injection risks, misplaced tool-call arguments, and PII to improve data quality.

Pricing comprises a Free Tier for TinyLlama, per-token fees for fine-tuning and domain addition, inference charges, dataset cleaning fees, and Live Knowledge subscriptions, with a pilot-free tier, then 99 USD/month beta and 299 USD/month general availability, plus custom enterprise pricing.

ModelBrew is a platform designed to help businesses continually fine-tune AI models while maintaining performance and managing rapidly changing information through a verifiable fact layer.