AIBrix is an open-source, cloud-native control plane for deploying, managing and scaling large-model inference on Kubernetes. Its documented components cover LLM gateway and routing, high-density LoRA management, multi-node and multi-engine inference, workload-aware autoscaling, a unified runtime sidecar, heterogeneous-GPU serving, GPU failure detection, KV-cache offloading and cross-engine cache reuse, batch inference and observability. The project originated at ByteDance and now lives under the vllm-project organization. It can orchestrate vLLM-based workloads but is not the vLLM engine or a single-model server. Vendor claims about cost efficiency and scalability are workload-dependent and should be validated against a team's cluster topology and traffic. AIBrix fits platform teams already operating Kubernetes and multiple models or replicas; its control-plane breadth is likely unnecessary for a single model on one machine.
AIBrix
Cloud-native control plane for scalable GenAI inference
Open-source Kubernetes-native building blocks for deploying, routing and scaling GenAI inference, including an LLM gateway, autoscaling, LoRA management and KV-cache offloading.
Pricing
Free and open source under Apache-2.0. There is no software license fee; users pay for their own Kubernetes, GPU and observability infrastructure.
Platforms
Kubernetes-native components, CRDs and deployment manifests for GPU clusters. Current documented release: v0.7.0. Includes gateway, autoscaling, model runtime, KV-cache and observability layers.
Categories
Tags
Alternatives
vLLM
High-throughput LLM serving engine
vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.
BentoML
ML model serving and deployment framework
BentoML is an open-source framework with 7K+ GitHub stars for packaging, deploying, and serving ML models as production-ready APIs. Bundles models, preprocessing, and serving logic into portable Bento archives with auto-generated REST/gRPC endpoints. Features adaptive batching for throughput optimization, GPU scheduling, multi-model inference pipelines, and containerization. Supports all major ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
KServe
Kubernetes-native model inference platform
KServe is an open-source Kubernetes-native platform for deploying and managing ML model inference at scale. It provides standardized inference protocols, autoscaling including scale-to-zero, canary rollouts, A/B testing, and multi-model serving. KServe supports all major ML frameworks including TensorFlow, PyTorch, scikit-learn, XGBoost, and LLM runtimes like vLLM and Triton through pluggable serving runtimes.
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