Looking for something like Modelz-LLM? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 24 are open source.
OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | FastChat | — | Yes | — |
| 2 | QuivrHQ/MegaParse | — | Yes | — |
| 3 | Rockpool | — | Yes | — |
| 4 | torchvision | — | Yes | — |
| 5 | climin | — | Yes | — |
| 6 | ormb | — | Yes | — |
| 7 | Cog | — | Yes | — |
| 8 | deepjavalibrary/djl | — | Yes | Android |
| 9 | Kitaru | — | Yes | — |
| 10 | Starwhale | — | Yes | — |
FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.
File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.
Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.
Torchvision provides datasets, transforms, and models for computer vision with PyTorch.
The climin package provides optimization methods focused on machine learning scenarios in Python.
ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.
Cog packages machine learning models into containers to support deployment across environments.
DJL is a Java framework for running deep learning models across different engines.
Kitaru provides agent traces that teams can run and inspect during application workflows.
Machine learning operations and large language model operations are the focus of Starwhale’s platform.
ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.
The Hydrosphere repository describes an MLOps platform for machine learning operations.
Manifest connects AI agents and tools to multiple model providers through an LLM gateway.
ModelDB tracks machine learning model versions, metadata, and experiments.
dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.
MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.
Flexible, high-performance serving system for machine learning models.
Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.
Latitude provides observability for AI agents and helps teams trace failures and verify fixes.
MMDeploy provides a model deployment framework from the OpenMMLab project.
Marquez collects, aggregates, and visualizes metadata about a data ecosystem.
Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.
Harbor sets up a preconfigured LLM stack with one command and includes hundreds of services to explore.
The abso TypeScript SDK calls more than 100 LLM providers through an OpenAI compatible format.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
