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.
About AIBrix
Pricing & Platform Specs
Pricing Summary
Free and open-source under the Apache-2.0 license. Operating costs are limited to the underlying Kubernetes nodes, GPU infrastructure, and networking provisioned by the user.
Supported 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.
Explore categories, tags & use cases
Categories
Alternatives
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.
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.
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.
Community experience
Sources & verification
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- Content verified
Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.
FAQ
What is AIBrix?
Open-source Kubernetes-native building blocks for deploying, routing and scaling GenAI inference, including an LLM gateway, autoscaling, LoRA management and KV-cache offloading.
Is AIBrix free?
Yes — AIBrix is open source and free to use. Free and open-source under the Apache-2.0 license. Operating costs are limited to the underlying Kubernetes nodes, GPU infrastructure, and networking provisioned by the user.
Is AIBrix open source?
Yes — AIBrix is open source.
Is AIBrix still maintained?
Yes — AIBrix is active. Its listing was last verified on August 26, 2026.
What are the best AIBrix alternatives?
The first editor-selected AIBrix alternatives are vLLM, BentoML, KServe.
