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.
Bento
Freemium
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.
Bentoとは？
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.
Bentoでできること
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.
利用シーン
“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.
よくある質問
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.
クイック情報
- 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
Bentoの代替品
Baseten
baseten.co
Baseten is an inference platform for deploying, serving, and scaling open-source, custom, and fine-tuned AI models. It supports managed cloud, self-hosted, hybrid, and dedicated deployments for teams running production AI workloads.
Hyperstack
www.hyperstack.cloud
Hyperstack is a cloud GPU platform for running AI and machine learning workloads, including training, inference, data analytics, and model development. It also provides AI Studio, virtual machines, and managed Kubernetes for deploying and operating GPU-backed workloads.
SambaNova
sambanova.ai
SambaNova is an AI inference platform for developers, enterprises, and infrastructure operators running large open-source models and agentic workloads. It combines cloud services with deployable hardware and software for cloud, on-premises, hybrid, and air-gapped environments.
FriendliAI
friendli.ai
FriendliAI is an inference cloud for deploying frontier open-weight and custom AI models in production. It offers serverless Model APIs, dedicated GPU endpoints, and BYOG options for agent, multimodal, and other high-throughput workloads.
Chutes
chutes.ai
Chutes is a decentralized, serverless AI inference platform for deploying, running, and scaling open-source models. Developers can access hosted models through an API or deploy private custom workloads on confidential GPU capacity.
Ray
www.ray.io
Ray is an open-source, Python-native framework for distributing and scaling AI, machine learning, and other compute-intensive workloads. It helps developers run workloads from a laptop to clusters with thousands of GPUs and supports data processing, training, serving, inference, and reinforcement learning.