13 Open-Source Tools for Foundation Model Deployment

Foundation model deployment is no longer one problem. It can mean running a local LLM for private experimentation, serving an open-weight model under heavy GPU traffic, packaging a model behind an API, or managing ML systems across Kubernetes and CI/CD. The right open-source tool depends on the workload, not the logo on the repo.

TL;DR: Use Ollama for local LLM experiments, vLLM for high-throughput GPU inference, TGI for production serving inside the Hugging Face ecosystem, BentoML for model APIs, and Kubeflow or Seldon Core when Kubernetes is already your operating layer.

Some of these tools are specialized LLM-serving engines. Some are older but still useful model-serving systems. Some are broader MLOps platforms. Be aware: “foundation model deployment” now covers several layers of infrastructure: local execution, inference serving, lifecycle management, orchestration, monitoring, and scaling.

vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. It is especially useful for teams deploying open-weight LLMs on GPUs and trying to improve serving throughput, batching, and memory use. Its best-known technique is PagedAttention, which helps manage attention key-value memory more efficiently during inference. The current vLLM documentation also highlights continuous batching, chunked prefill, prefix caching, quantization, and distributed inference.

Best for: production LLM serving, high-throughput inference, open-weight models, GPU-heavy workloads.

Status: Highly current. One of the most important open-source LLM-serving tools for GPU-heavy production inference.

Ollama is an open-source tool for running large language models locally on a laptop, workstation, or private server. It is useful for local development, demos, privacy-sensitive prototyping, small internal tools, and teams that want a simple way to pull and run models without building a full production serving stack. It is much simpler than Kubernetes-based deployment systems, but it is not designed to be the main serving layer for high-scale production workloads.

Best for: local LLMs, demos, private experiments, lightweight internal tools.

Status: Highly current. Best for local LLM use, fast prototyping, and small-scale private deployments.

Hugging Face Text Generation Inference, or TGI, is a toolkit for deploying and serving large language models. It supports high-performance text generation for popular open-source LLMs and is tightly connected to the Hugging Face ecosystem. Hugging Face describes TGI as a toolkit for deploying and serving LLMs, and its GitHub page says it is used in production to power Hugging Chat, the Inference API, and Inference Endpoints.

Best for: production LLM serving, Hugging Face models, teams already working inside the Hugging Face ecosystem.

Status: Highly current. Best for production LLM serving when Hugging Face is already part of the stack.

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It is strongest when teams already use TensorFlow and need a stable serving layer for trained models. It can be extended beyond TensorFlow models, but it is not the first tool most teams reach for when deploying modern open-weight LLMs.

Best for: production TensorFlow model serving, stable ML inference systems, older production ML stacks.

Status: Stable but older. Still useful for TensorFlow production models, but less central for modern foundation model deployment.

TorchServe is a model-serving framework for PyTorch models. It was designed to simplify deployment and serving for PyTorch-based ML systems. However, the project is now marked as being in limited maintenance: existing releases remain available, but there are no planned updates, bug fixes, new features, or security patches.

Best for: existing PyTorch deployments where TorchServe is already installed and migration is not immediate.

Status: Use with caution. It should not be the default choice for a new foundation model deployment in 2026.

MLflow is a platform for managing the machine learning lifecycle, including experiment tracking, model packaging, model registry, and deployment workflows. It is useful when the problem is not only serving the model, but managing the path from experiment to production. MLflow’s deployment tools can serve models locally and connect to other serving targets, but it is not a specialized high-throughput LLM inference server.

Best for: model lifecycle management, experiment tracking, model registry, reproducible deployment workflows.

Status: Current. Strong for lifecycle management and deployment workflows, but not LLM-serving-first.

Kubeflow is a Kubernetes-native platform for building, deploying, and managing machine learning workflows. It is useful for teams that already operate on Kubernetes and need a broader ML platform rather than a single model server. Kubeflow can support pipelines, model metadata, notebooks, and other parts of the ML lifecycle, but it may be too heavy if the only goal is to run one model.

Best for: Kubernetes-native ML platforms, scalable ML workflows, teams with platform engineering support.

Status: Current. Powerful, but heavy. Best for organizations that already have Kubernetes maturity.

Seldon Core is a Kubernetes-native framework for deploying, managing, and scaling AI systems. Seldon Core 2 is positioned for both MLOps and LLMOps, with support for standardized deployment across model types, on-prem environments, and cloud environments. It is a good fit when the deployment problem includes scaling, monitoring, pipelines, and governance around production models.

Best for: Kubernetes model serving, MLOps, LLMOps, monitoring, production AI systems.

Status: Current. Especially useful for teams that want Kubernetes-native deployment and production controls.

Metaflow is an open-source framework for building and managing real-world ML, AI, and data science projects. It was originally developed at Netflix and is especially useful for moving data science work from local development into production workflows. It is not a dedicated model-serving server, but it can help teams manage the broader workflow around ML and AI systems.

Best for: ML workflows, data science projects, productionizing research code, managing dependencies and execution.

Status: Current. More workflow platform than serving engine, but still relevant in foundation model deployment stacks.

MLRun is an open-source AI orchestration framework for managing ML and generative AI applications across their lifecycle. It supports data preparation, model tuning, customization, validation, optimization, real-time serving, pipelines, observability, and deployment across cloud, hybrid, and on-prem environments.

Best for: MLOps, GenAI orchestration, lifecycle management, real-time serving pipelines.

Status: Current. Useful for teams building production ML and GenAI applications that need orchestration beyond simple model serving.

BentoML is a framework and platform for building, serving, and deploying AI applications and model inference APIs. It helps package models into reproducible services and supports production-grade deployment patterns. It is useful when teams need to turn models into APIs and manage inference services without building every serving layer from scratch.

Best for: model APIs, AI inference services, custom model serving, production deployment.

Status: Current. Strong general-purpose platform for building and deploying AI inference services.

SGLang is a serving framework for LLMs and multimodal models. It is designed for low-latency, high-throughput inference on anything from a single GPU to massive distributed GPU clusters. SGLang focuses on production-scale serving, advanced scheduling, distributed parallelism, and RL rollout generation for frontier AI systems. Its core features include continuous batching, RadixAttention prefix caching, speculative decoding, tensor/pipeline/expert parallelism, quantization support and multi-LoRA serving.

Best for: large-scale LLM serving, distributed inference, RL rollouts, multimodal production systems

Status: Current. Used in both frontier-model training and high-scale production deployments.

llama.cpp is inference engine and runtime for running LLMs locally with minimal setup. Written entirely in C/C++, it focuses on efficient CPU and GPU inference, lightweight deployment, hardware portability, and quantized execution across consumer devices, edge systems, laptops, workstations, and servers. It is one of the foundational tools behind the modern GGUF local-LLM.

Best for: local LLM and highly optimized quantized inference, lightweight deployment, CPU-based LLMs.

Status: Current. Widely used open-source runtime for local and edge LLM inference.

The original model-serving world was mostly about taking a trained model and exposing it through a production endpoint. That is still important, but foundation models changed the deployment problem.

Modern teams now need to think about:

That is why vLLM, Ollama, and TGI are in this list. They reflect where foundation model deployment has moved: away from generic model serving alone and toward LLM-specific inference, local model running, and high-throughput production serving.

Use Ollama if you want the fastest way to run an LLM locally.

Use vLLM if you care about high-throughput GPU inference for open-weight LLMs.

Use Hugging Face TGI if your team already works heavily with Hugging Face models and wants a production LLM-serving stack.

Use BentoML if you want to package models into production APIs.

Use Seldon Core or Kubeflow if Kubernetes is already central to your infrastructure.

Use MLflow, Metaflow, or MLRun if the bigger problem is lifecycle management, workflow orchestration, or production ML operations.

Use TensorFlow Serving if you still have TensorFlow models in production.

Use TorchServe only if you already depend on it and understand the maintenance risk.

Use SGLang for more optimized for advanced scheduling, RL/post-training, large distributed deployments.

Use llama.cpp if you want highly optimized local or edge LLM inference on consumer hardware.

Foundation model deployment is the process of running, serving, scaling, and managing large AI models in real applications. It can include local model execution, cloud inference, API packaging, Kubernetes deployment, monitoring, and lifecycle management.

Ollama is usually the simplest choice for local LLM deployment. It is built for running models on a laptop, workstation, or private server without setting up a large production serving system.

vLLM is one of the strongest open-source choices for high-throughput LLM serving, especially for open-weight models running on GPUs. It focuses on serving efficiency, batching, memory management, and inference throughput.

vLLM is often chosen for high-throughput open-weight LLM serving and memory-efficient inference. TGI is Hugging Face’s production-oriented LLM serving toolkit and is especially useful for teams already working inside the Hugging Face ecosystem.

TorchServe can still be used in existing PyTorch deployments, but it is no longer actively maintained. For new projects, teams should usually consider more current serving options unless they have a specific reason to keep TorchServe.

Further reading

If you're just getting started with ML and AI, check out our curated list of Top 10 GitHub repos for AI & ML practitioners— collections of courses, guides, and projects to build your foundations.

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