Helicone
Open-Source LLM Observability & Monitoring
Free
Sagify
Sagify accelerates machine learning and LLM deployment on AWS SageMaker with minimal configuration. Streamline training, tuning, and deployment using a unified, no-code-friendly interface.
About Sagify
Simplifying Machine Learning Deployment
Sagify is a developer-friendly tool that removes the complexity of building and deploying machine learning (ML) and large language model (LLM) applications on AWS SageMaker. It provides a clean, command-line interface and modular structure so users can focus on model development, not infrastructure.
Designed for ML Engineers and Data Teams
Whether you’re a solo developer, part of a data science team, or building AI products at scale, Sagify offers a practical framework to move from prototype to production faster, without managing low-level cloud configurations.
Core Capabilities of Sagify
From Code to Deployed Model in a Day
Sagify lets you train, tune, and deploy models with a single command. You only need to write your model logic—Sagify takes care of provisioning, scaling, hyperparameter tuning, and deployment to AWS SageMaker.
Unified Gateway for Large Language Models
Sagify includes an LLM Gateway that connects to both proprietary models (like OpenAI or Anthropic) and open-source models (like LLaMA or Stable Diffusion). This lets you use different models via a single REST API, reducing integration overhead.
Machine Learning Automation on AWS
Full AWS SageMaker Integration
Sagify deeply integrates with SageMaker, allowing automated Docker builds, training jobs, model deployments, and batch inference through simple CLI commands. It supports spot instances, resource tagging, and hyperparameter optimization.
One-Line Deployment of Foundation Models
You can deploy Hugging Face, OpenAI, or custom foundation models using predefined templates—no need to write code or configure infrastructure manually.
LLM Infrastructure Without the Headaches
RESTful API for LLMs
The LLM Gateway offers a consistent interface to send prompts, receive completions, generate images, or extract embeddings across multiple providers. This is ideal for apps that need to switch or test LLM performance without rewriting backend logic.
Local and Cloud Hosting Options
Sagify supports running the LLM Gateway locally via Docker or deploying it to AWS Fargate. This flexibility allows you to prototype locally and scale in production effortlessly.
Advanced ML Use Cases
Batch Inference for High-Volume Workflows
Sagify supports large-scale batch processing of ML or embedding jobs using S3 and AWS SageMaker. Ideal for recommendation systems, search indexing, and offline predictions.
Hyperparameter Optimization Built-In
With support for Bayesian optimization, you can fine-tune your models for better performance. Sagify provides all the tools needed to define parameter ranges, set objectives, and monitor results directly through AWS.
Developer Tools and Extensibility
SDK and CLI
Sagify includes both a Python SDK and a full-featured CLI. This dual interface allows you to automate workflows within your apps or manage experiments interactively from the terminal.
Modular Architecture for Customization
The tool is built around a modular structure, making it easy to replace or extend components such as model logic, endpoints, or training configurations without affecting the overall pipeline.