# Bento Bento is an inference platform for packaging, deploying, optimizing, and operating AI and machine-learning models at scale. It supports open and custom models across cloud, on-premises, Kubernetes, and bring-your-own-cloud environments. - Findings24: - Site: - Preços: Freemium - Categorias: Plataformas de inferência de IA, Implantação de modelos de IA ## O que é Bento? Bento is an inference platform for teams that need to deploy and operate AI and machine-learning models in production. It provides a unified way to serve open-source and custom models across architectures, frameworks, and modalities, while giving teams control over deployment environments and infrastructure. The platform combines deployment automation, observability, access control, resource and quota tracking, and versioned release workflows. Its compute layer supports elastic and cross-region scaling, auto-scaling based on inference workloads, scaling to zero, and cold-start acceleration. Bento supports deployments on a user's own cloud or on-premises Kubernetes environments, as well as Bento Cloud. It also provides inference optimization controls for balancing latency, throughput, and cost, with serving patterns for interactive, asynchronous, batch, and multi-model workflows. ## O que Bento pode fazer? ### Unified model serving Package and deploy open-source or custom models across architectures, frameworks, and modalities. The site lists serving examples including vLLM, TRT-LLM, JAX, SGLang, PyTorch, and Transformers. ### Inference-aware scaling Scale deployments according to inference workload patterns with traffic-based auto-scaling, elastic and cross-region scaling, scaling to zero, and cold-start acceleration. ### Performance optimization Tune deployment components and configurations against latency, throughput, or cost goals. Distributed LLM inference can run large models across multiple GPUs. ### Deployment and release operations Manage deployment automation and CI/CD with version control, rollbacks, canary releases, shadow testing, and A/B testing. ### Observability and resource controls Monitor compute, system health, performance, and LLM-specific metrics while using fine-grained access control and resource and quota tracking. ### Flexible infrastructure choices Run inference on a bring-your-own-cloud setup, on-premises Kubernetes, or Bento Cloud. The site also presents access to NVIDIA and AMD GPU hardware through Bento Cloud. ## Casos de uso ### Interactive AI applications Serve chatbots, recommendation features, and other user-facing AI functions where sub-second latency is a stated deployment goal. ### Asynchronous AI workloads Run long-running tasks that do not require an immediate response, using an async serving pattern instead of an interactive request path. ### Large-scale batch inference Process large datasets in batches and optimize the deployment for compute efficiency rather than instant responses. ### Compound AI and RAG workflows Chain multiple models to build more complex retrieval-augmented generation or compound AI systems. ### Production LLM evaluation and planning Use the LLM handbook and performance explorer to study inference metrics, compare models, GPUs, and frameworks, and assess optimization approaches before deployment. ## Perguntas frequentes ### What kinds of models can Bento serve? The site describes support for popular open-source models and custom models of any architecture, framework, or modality. Listed serving technologies include vLLM, TRT-LLM, JAX, SGLang, PyTorch, and Transformers. ### Where can Bento deployments run? The documented deployment choices include a user's own cloud, on-premises Kubernetes, and Bento Cloud. The platform is positioned for self-hosted deployment and also presents managed access to selected GPU hardware through Bento Cloud. ### Can Bento handle different inference workload patterns? Yes. The site describes serving patterns for interactive applications, asynchronous long-running tasks, large-scale batch inference, and workflows that chain multiple models. ### How does Bento support safer model releases? Its operations features include deployment automation and CI/CD, version control with rollbacks, and canary, shadow, and A/B testing workflows. ### What information is available for optimizing LLM inference? Bento provides an LLM inference handbook covering metrics and techniques such as time to first token, tokens per second, continuous batching, and prefix caching. Its performance explorer compares models, GPUs, and inference frameworks. ## Informações rápidas - **Product category**: AI inference platform - **Primary users**: AI, machine-learning, data science, and infrastructure teams - **Deployment environments**: Own cloud, on-premises Kubernetes, or Bento Cloud - **Serving scope**: Open-source and custom models - **Scaling capabilities**: Auto-scaling, scaling to zero, elastic scaling, and cold-start acceleration - **Official domain**: bentoml.com