Otto-m8 – A low code AI/ML API deployment Platform
Hi all, so I've been working on this low to no code platform that allows you to spin up deep learning workloads(I'm talking LLM's, Huggingface models, etc), interconnect a bunch of them, and deploy them as API's. The idea essentially came up early in September, when experimenting with combining a Huggingface based BERT model with an LLM at work, and I realized it would be cool if I could do that instantly(especially since it was a prototype). At the time, I was considering a platform that could essentially help you train deep learning models without any code. It was my observation that much…
In plain words
Otto-m8 is a low-code platform for deploying deep learning workloads as APIs. It lets users connect multiple models—including large language models and Hugging Face models—without writing code, then instantly deploy them as functional APIs. The platform is designed for developers and teams who need to prototype and launch AI model combinations quickly, focusing on inference deployment rather than model training.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi all, so I've been working on this low to no code platform that allows you to spin up deep learning workloads(I'm talking LLM's, Huggingface models, etc), interconnect a bunch of them, and deploy them as API's. The idea essentially came up early in September, when experimenting with combining a Huggingface based BERT model with an LLM at work, and I realized it would be cool if I could do that instantly(especially since it was a prototype). At the time, I was considering a platform that could essentially help you train deep learning models without any code. It was my observation that much of the code required to train or even run inference on HF models have matured significantly. But before I solved that problem, I wanted to solve inference. Initially inspired by n8n and AWS Cloudformation, I built out otto-m8 (translates to automate). Given a json payload that lists out all the resources, and how each model is interconnected, launch it as one-off API the user can query. And thanks to Reactflow, the UI was just something I couldn't just not implement. And as I built it out, I did not want to miss out on the LLM and Agent bit. With otto-m8, today, you can launch complex workflows by interconnecting HF models and LLM's(currently it supports OpenAI and Ollama only). But I like to see it being more than that. At the core, every workflow is an input process output model. Inputs get processed and there's an output. Therefore, with the way things are setup, one can integrate almost anything and make it interconnectable. Project Link: https://github.com/farhan0167/otto-m8 Let me know what you guys think. I really would love feedback!
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