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OpenLLM

Free

Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

Model APIsFreeFree tier
Inputs: textOutputs: text
Type
Open Source
Company
BentoML
Visit Website GitHub (12.3k stars)

About OpenLLM

OpenLLM is an open-source platform by BentoML that enables users to run any open-source large language model (LLM), such as DeepSeek and Llama, as an OpenAI-compatible API endpoint. It provides a unified framework for packaging, deploying, and scaling models across various architectures and frameworks including vLLM, TRT-LLM, JAX, SGLang, and PyTorch Transformers. OpenLLM offers a model catalog with pre-optimized models for quick deployment, custom model serving, intelligent scaling with cross-region and elastic auto-scaling, cold-start acceleration, and fine-grained access control. Deployments can be on your own cloud, on-premises Kubernetes, or Bento Cloud with access to cutting-edge GPUs like NVIDIA H100 and AMD MI300X. The platform emphasizes tailored optimization, distributed inference, and inference-specific metrics for efficient resource utilization.

Key Features

Open Model Catalog for one-click deployment of popular open-source models like Llama 4, DeepSeek, Qwen, Flux
Custom model serving supporting any architecture, framework, or modality
Integration with inference engines: vLLM, TRT-LLM, JAX, SGLang, PyTorch Transformers
Deployment automation and CI/CD with comprehensive observability
Fine-grained access control, resource and quota tracking, performance tuning
Intelligent resource management with cross-region scaling, elastic auto-scaling, cold-start acceleration
Multi-cloud compute orchestration and scaling-to-zero
Bring Your Own Cloud or on-premises Kubernetes support
Bento Cloud with access to NVIDIA and AMD GPUs (H100, MI300X, B200, etc.)
Distributed LLM inference across multiple GPUs

Pros & Cons

Pros
  • Open-source and free to use with no licensing costs
  • OpenAI-compatible API simplifies integration with existing applications
  • Supports a wide range of model architectures and inference frameworks
  • Offers both cloud and on-premises deployment options
  • Provides intelligent auto-scaling and cold-start acceleration for performance
  • Includes pre-optimized models for immediate deployment
  • Fine-grained control over deployment configurations and resource allocation
Cons
  • Requires GPU hardware (e.g., NVIDIA H100, AMD MI300X) for running large models effectively
  • Setup and configuration may require technical expertise for custom models
  • Bento Cloud usage incurs compute costs for GPU resources

Best For

Deploying open-source LLMs like Llama, DeepSeek, and Qwen for production inferenceServing fine-tuned or custom models with full customization and optimizationBuilding scalable AI applications requiring real-time or batch inferenceRunning multi-cloud or hybrid deployments with consistent managementUsing intelligent scaling to handle variable traffic patterns efficiently

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