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substratusai avatar

substratusai/kubeai

0
View on GitHub↗
1,210 stars·125 forks·Go·Apache-2.0·7 viewswww.kubeai.org↗

Kubeai

AI Inference Operator for Kubernetes. The easiest way to serve ML models in production. Supports VLMs, LLMs, embeddings, and speech-to-text.

Features

  • Serving Frameworks - Kubernetes-native deployment and scaling for AI models.

Star history

Star history chart for substratusai/kubeaiStar history chart for substratusai/kubeai

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Kubeai

These projects share indexed features with Kubeai. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • bentoml/bentomlbentoml avatar

    bentoml/BentoML

    8,456View on GitHub↗

    BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It

    Pythonai-inferencedeep-learninggenerative-ai
    View on GitHub↗8,456
  • jina-ai/jinajina-ai avatar

    jina-ai/jina

    21,858View on GitHub↗

    Jina is a cloud-native framework for building and deploying multimodal AI applications that process text, images, and audio across distributed microservices. It functions as an inference orchestrator and a distributed model gateway, providing a containerized stack to organize AI executors into operational pipelines. The system manages large language model workloads through token-streamed response delivery and dynamic batching to increase hardware throughput. It utilizes a protocol-agnostic communication layer to route data across different machine learning frameworks. The framework covers hi

    Python
    View on GitHub↗21,858
  • jina-ai/langchain-servejina-ai avatar

    jina-ai/langchain-serve

    1,643View on GitHub↗

    ⚡ Langchain apps in production using Jina & FastAPI

    Python
    View on GitHub↗1,643
  • ajndkr/lanarkyajndkr avatar

    ajndkr/lanarky

    994View on GitHub↗

    The web framework for building LLM microservices deprecated

    Python
    View on GitHub↗994
Compare all 12 related projects→

Frequently asked questions

What does substratusai/kubeai do?

AI Inference Operator for Kubernetes. The easiest way to serve ML models in production. Supports VLMs, LLMs, embeddings, and speech-to-text.

What are the main features of substratusai/kubeai?

The main features of substratusai/kubeai are: Serving Frameworks.

Which projects share features with substratusai/kubeai?

Projects with overlapping indexed features include: ajndkr/lanarky — The web framework for building LLM microservices [deprecated]. bentoml/bentoml — BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package,… jina-ai/jina — Jina is a cloud-native framework for building and deploying multimodal AI applications that process text, images, and… jina-ai/langchain-serve — ⚡ Langchain apps in production using Jina & FastAPI. kaito-project/kaito — Kubernetes AI Toolchain Operator. mosecorg/mosec — A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your…