Sagify

Sagify bridges the gap between ML experimentation and production deployment on AWS. Rather than wrestling with SageMaker's API and infrastructure configuration, you define workflows in code and Sagify handles resource provisioning, scaling, and cloud plumbing automatically.

The platform excels at reducing time-to-production for both custom trained models and LLM-powered applications. It includes local Docker testing before cloud deployment, hyperparameter tuning, batch inference for cost-effective predictions, and a FastAPI gateway for standardized access across OpenAI, Anthropic, and open-source models.

Highlights

• Local model testing with Docker before deploying to AWS
• One-click training and deployment without manual infrastructure setup
• Unified LLM interface supporting proprietary and open-source models
• Batch processing for large-scale predictions at lower cost
• Pre-trained model support for quick prototyping

Sagify is ideal for ML engineers and data scientists who want to focus on model quality rather than cloud configuration. As open-source software, you only pay for the AWS compute and storage resources you use.