Alternatives to AIBrix

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 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 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.