alvaropaco/haif

Production-ready microservices framework for AI inference over RPC. It provides a Gateway for client requests, an Orchestrator that schedules work, a Registry for model metadata, Workers that run inference, and a full observability stack (Prometheus, Grafana, Loki, Jaeger) — all wired together with Docker Compose.

28
/ 100
Experimental

HAIF helps developers operationalize AI models by providing a complete framework to deploy and manage AI inference services. It takes your trained AI models and handles client requests, distributes work, executes the inference on available machines (CPUs or GPUs), and streams results back. This is for software architects, DevOps engineers, and machine learning engineers who need to integrate AI models into their applications reliably and at scale.

No commits in the last 6 months.

Use this if you need to deploy AI models as robust, scalable microservices and require built-in tools for monitoring, logging, and tracing to ensure performance and reliability.

Not ideal if you are looking for a tool to train AI models or if you only need to run ad-hoc, low-volume inference directly within a script without complex service management.

AI-deployment MLOps microservices real-time-inference observability
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 5 / 25
Maturity 15 / 25
Community 6 / 25

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Stars

14

Forks

1

Language

TypeScript

License

Apache-2.0

Last pushed

Oct 07, 2025

Commits (30d)

0

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