Bento: Run Inference at Scale

Description

Bento is a comprehensive inference platform built for speed and control, empowering AI teams to deploy any model anywhere with tailored optimization, efficient scaling, and streamlined operations. It simplifies inference infrastructure while providing granular control over deployments, making it easier to manage, monitor, and optimize AI model inference.

Bento supports deploying a wide range of models, including popular open-source options like Llama 4, DeepSeek, Ling/Ring, Flux, Qwen, and GPT-OSS, through its Open Model Catalog. It also offers a unified framework for packaging and deploying custom models of any architecture, framework, or modality, including fine-tuned open-source models and proprietary custom models. The platform automates deployment and CI/CD processes, provides comprehensive observability, fine-grained access control, and resource/quota tracking.

For efficient scaling, Bento features the Bento Compute Engine, which provides intelligent resource management for optimal compute utilization. It supports cross-region scaling, elastic auto-scaling, cold-start acceleration, multi-cloud compute orchestration, and scaling-to-zero capabilities. Users have complete control over their infrastructure and deployment environment, with options to deploy on their own cloud, on-premises Kubernetes, or leverage Bento Cloud for access to cutting-edge GPU hardware without procurement hassles, including Nvidia GPUs, AMD GPUs, and B200, H100, MI300X.

Bento accelerates the path to production for AI applications by providing developers with everything they need to build, ship, and scale AI inference. This includes a Dev Codespace for rapid cloud iteration, an LLM Gateway for a unified interface to all LLM providers with centralized cost control, and streamlined operations with full deployment lifecycle management, version control, rollbacks, and A/B testing. Full observability is achieved through comprehensive monitoring of compute, performance, and LLM-specific metrics.

The platform is built for enterprise-grade security, compliance, and operational capabilities, ensuring reliability with performance SLAs, 24/7 monitoring, uptime guarantees, and automatic failover. Bento also offers Forward Deployed Engineering with dedicated technical experts, inference optimization research, and continuous benchmarking. Data sovereignty is maintained with full control over user data. BentoML is now part of Modular.

Bento: Run Inference at Scale's Core Features

• Deploy any model anywhere

• Tailored inference optimization

• Efficient scaling capabilities

• Streamlined operations

• Open Model Catalog for popular open-source models

• Unified framework for custom model packaging and deployment

• Automated deployment and CI/CD

• Comprehensive observability and monitoring

• Fine-grained access control

• Resource and quota tracking

• Intelligent resource management for compute utilization

• Cross-region and elastic auto-scaling

• Cold-start acceleration

• Multi-cloud compute orchestration

• Scaling-to-zero

How to use Bento: Run Inference at Scale?

1. 1

   Build: Package your model using the unified framework.

2. 2

   Deploy: Automate deployment with CI/CD pipelines.

3. 3

   Scale: Utilize intelligent resource management for efficient scaling.

4. 4

   Monitor: Track performance and system health with comprehensive observability.

5. 5

   Optimize: Tune every layer of your deployment for speed, cost, and quality.

Bento: Run Inference at Scale's Use Cases

• Model Deployment
• Inference Scaling
• Performance Optimization
• Streamlined Operations
• Cloud-Native Inference
• LLM Serving
• Batch Inference
• Real-time AI Features

FAQ from Bento: Run Inference at Scale

• What is Bento: Run Inference at Scale?

  Bento is an inference platform designed for speed and control, enabling AI teams to deploy any model anywhere with tailored optimization, efficient scaling, and streamlined operations.

• Can I deploy custom models with Bento?

  Yes, Bento provides a unified framework for packaging and deploying custom models of any architecture, framework, or modality, including fine-tuned open-source models and proprietary custom models.

• How does Bento handle scaling for AI inference?

  Bento features intelligent resource management, cross-region scaling, elastic auto-scaling, cold-start acceleration, multi-cloud compute orchestration, and scaling-to-zero capabilities to efficiently handle AI inference workloads.

• What kind of hardware support does Bento offer?

  Bento supports various hardware, including Nvidia GPUs, AMD GPUs, and specific models like B200, H100, and MI300X, accessible through Bento Cloud or self-hosted options.

• Does Bento offer tools for faster path to production for AI?

  Yes, Bento provides a Dev Codespace for rapid cloud iteration, an LLM Gateway for unified LLM access, and streamlined operations with full deployment lifecycle management.

• What are the observability features of Bento?

  Bento offers comprehensive monitoring of compute and performance, LLM-specific metrics, and system health, ensuring full observability of your inference deployments.

• Is Bento suitable for enterprise deployments?

  Yes, Bento is built for enterprise-grade security, compliance, and operational capabilities, ensuring reliability with performance SLAs, 24/7 monitoring, and automatic failover.

• Can I deploy Bento on my own infrastructure?

  Yes, Bento supports self-hosted deployments anywhere, including on any cloud or on-premises Kubernetes environments, offering full control over your infrastructure.

• What open-source models can be deployed with Bento?

  Bento supports popular open-source models such as Llama 4, DeepSeek, Ling/Ring, Flux, Qwen, and GPT-OSS through its Open Model Catalog.

• What are the advanced serving patterns supported by Bento?

  Bento supports advanced serving patterns for interactive applications, async long-running tasks, large-scale batch inference, and orchestrating complex workflows with chained models.