Helicone
Open-Source LLM Observability & Monitoring
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.
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.
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.
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.
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.
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.
You can deploy Hugging Face, OpenAI, or custom foundation models using predefined templates—no need to write code or configure infrastructure manually.
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.
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.
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.
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.
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.
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.