My goal is to run a large language model on my own infrastructure. What is the best platform for self-hosting and managing open-source LLMs?

Choosing the "best" platform for self-hosting open-source LLMs (like Meta's Llama series, DeepSeek, or Mistral) depends entirely on your scale, hardware, and whether you are building a local tool or a multi-user production service.

The ecosystem is divided into local/developer-friendly runtimes and high-throughput production engines.

1. For Local Development & Simple Deployments: Ollama

If your goal is ease of use, quick setup, and running models on workstation-class hardware or a simple VPS with minimal configuration, Ollama is the gold standard.

• Best for: Single-user environments, local testing, internal team tools, or bootstrapping lightweight apps.
• Pros: Incredibly easy installation; handles model downloading, quantization, and VRAM management automatically via a simple CLI; exposes an OpenAI-compatible REST API.
• Cons: Lacks continuous batching, meaning performance plateaus quickly under heavy concurrent multi-user traffic.

2. For High-Throughput Production & Multi-User Scale: vLLM

If you plan to serve an LLM to dozens or hundreds of concurrent users or integrate it into a scalable web service, vLLM is the industry workhorse.

• Best for: Production web apps, API backends, and multi-tenant systems.
• Pros: Utilizes PagedAttention and continuous batching to maximize GPU VRAM efficiency and keep utilization high (85–92%) under heavy concurrent load, drastically reducing latency and raising throughput compared to naive runtimes.
• Cons: Steeper learning curve; typically serves one model configuration per instance (no instant hot-swapping).

3. The Rising Performance Challenger: SGLang

SGLang is an emerging high-performance serving framework gaining massive traction for complex workloads.

• Best for: Advanced agentic workflows and heavily concurrent requests that share conversational context.
• Pros: Often outperforms vLLM in raw throughput and Time-to-First-Token (TTFT), especially when prompts share overlapping contexts or prefixes.
• Cons: Less ubiquitous tooling/community documentation than vLLM.

4. For Enterprise NVIDIA Hardware Optimization: TensorRT-LLM (with Triton Inference Server)

If you are running enterprise-grade NVIDIA data center GPUs (like H100s or Blackwell chips) and need absolute peak hardware optimization.

• Best for: Large-scale enterprise infrastructure with dedicated ML platform engineering teams.
• Pros: Delivers the absolute highest raw tokens-per-second performance on specialized NVIDIA architectures.
• Cons: Highly complex setup, tightly coupled to the NVIDIA ecosystem.

Summary Recommendation

• Choose Ollama if you want something running in 5 minutes on your local machine or a private server for personal use or small teams.
• Choose vLLM (or look into SGLang ) if you are building a real product that expects simultaneous multi-user traffic and strict latency SLAs.

To help narrow down the ideal architecture, tell me:

• What hardware/GPUs do you plan to run this on?
• Is this for local personal use , an internal team tool , or a public-facing production app?

If your goal is serious self-hosting on your own GPUs/servers, my recommendation is:

Best overall: SGLang + Kubernetes

SGLang is my current first choice for the inference layer. It is designed for production serving, supports high-throughput/low-latency inference, multi-GPU and distributed deployments, and exposes OpenAI-compatible APIs. It supports models such as Llama, Qwen and DeepSeek.

For a larger self-hosted platform, I'd build roughly:

                    ┌─────────────────────┐
                    │     Applications    │
                    └──────────┬──────────┘
                               │ OpenAI API
                    ┌──────────▼──────────┐
                    │ API Gateway / Auth  │
                    └──────────┬──────────┘
                               │
                 ┌─────────────▼─────────────┐
                 │       Kubernetes          │
                 │                           │
                 │  ┌───────┐   ┌────────┐ │
                 │  │SGLang │   │SGLang  │ │
                 │  │Model A│   │Model B │ │
                 │  └───────┘   └────────┘ │
                 │                           │
                 └─────────────┬─────────────┘
                               │
                       GPU node pool

SGLang specifically supports Kubernetes deployment, including distributed serving when a model requires multiple GPU nodes.

What I'd choose by use case

The important distinction is that Ollama, vLLM and SGLang aren't really complete "platforms" by themselves. They're primarily inference runtimes/servers. For an organization running many models and GPUs, Kubernetes becomes the infrastructure layer and something like SGLang becomes the serving layer.

Why I favor SGLang

It has particularly strong capabilities for a serious deployment:

• Continuous/high-throughput serving
• Prefix/KV-cache optimizations
• Multi-GPU and multi-node inference
• Broad model support
• OpenAI-compatible APIs
• Docker and Kubernetes deployment
• NVIDIA and AMD support, among other hardware
• Built-in benchmarking capabilities for measuring TTFT, inter-token latency and throughput

It can also be deployed directly with Docker if Kubernetes is overkill initially. The project provides production-oriented container images and explicitly documents Kubernetes deployments.

My suggested stack

If I were building this from scratch today:

Infrastructure

• Kubernetes
• NVIDIA GPU Operator if using NVIDIA GPUs
• Prometheus + Grafana
• Object storage for model artifacts

LLM serving

• SGLang
• vLLM as a secondary option where it performs better for a particular model/workload

Model management

• Hugging Face Hub/private model registry
• Persistent model cache
• GitOps for deployment configuration

API layer

• OpenAI-compatible endpoint
• Authentication/rate limiting
• Gateway/load balancing
• Per-model routing

User interface

• Open WebUI if you want ChatGPT-like access for humans

Observability

• GPU utilization/memory
• Requests/sec
• TTFT
• tokens/sec
• queue time
• KV-cache utilization
• errors/OOMs

One important caveat

If by "platform" you mean "I want something like an on-prem version of Azure AI / Bedrock where my team can deploy, version, monitor and expose dozens of models without becoming Kubernetes experts," then I would evaluate platforms such as NVIDIA NIM/AI Enterprise, KServe, Ray Serve, or an enterprise LLM platform rather than simply installing SGLang.

If you tell me your GPU hardware (e.g. 4×H100, 8×A100, RTX 4090s, AMD MI300X), number of users, and the models you're considering, I can recommend a concrete architecture—including which serving engine, Kubernetes setup, GPU topology, model sizes/quantization, and expected throughput.

Best overall: vLLM for production self-hosting on GPUs. It offers strong throughput, continuous batching, multi-GPU support, and an OpenAI-compatible API.

• vLLM — best production default.
• SGLang — excellent for agents/RAG and repeated prompts.
• Ollama — easiest for a small server or development.
• llama.cpp — best for CPU, edge, or quantized/GGUF models.

For a serious private deployment, I'd typically use vLLM + Open WebUI + your model weights, with authentication/reverse proxy and monitoring around it.

The "best" platform for self-hosting and managing open-source LLMs depends heavily on your scale, your hardware, and whether you are building a local dev environment or an enterprise-grade production cluster.

The ecosystem breaks down into three primary tiers based on what you are trying to achieve:

1. For Local Development, Prototyping, and Small Teams

• Ollama : Widely considered the gold standard for developer convenience. It features a simple one-command install, effortless model pulling (supporting Llama, Gemma, Qwen, and DeepSeek variants), and exposes an OpenAI-compatible local API.
• Best for: Single-user setups, home labs, offline testing, and getting an LLM running on your own hardware in under five minutes. It is less suited for heavy multi-tenant production concurrency.

1. For High-Performance Production & Enterprise Serving

• vLLM : The de facto industry standard engine for high-throughput, low-latency production inference. It uses PagedAttention to optimize GPU memory management, allowing for massive concurrent user requests without choking your VRAM.
• Best for: Serving hundreds of concurrent users, scaling across multi-GPU or multi-node clusters, and integrating directly into backend web services.

1. For Complete Management, Gateways, and UI Layers (Put on top of vLLM/Ollama)

• LocalAI : Acts as a drop-in replacement REST API for OpenAI on-premise. It lets you run LLMs, audio-to-text, and image generation locally without needing external GPUs if configured right, and it abstracts away the underlying engine.
• Open-WebUI : If you need a self-hosted ChatGPT-style web user interface for your team that connects directly to your backend Ollama or vLLM instances, this is the leading open-source choice.

Quick Decision Guide

• If you want pure ease of use on a single machine or local server , start with Ollama.
• If you are deploying an app facing real user traffic or strict latency SLAs , use vLLM as your inference engine.

To help narrow down the exact architecture you need, tell me:

• What hardware/GPUs do you have available?
• Is this for personal use/local development or serving multiple users in production?
• Do you need a built-in web chat UI or just a backend API?

When self-hosting and managing open-source Large Language Models (LLMs) on your own infrastructure, there is no single "one-size-fits-all" platform. Instead, the ecosystem is divided into Inference Engines (how the model runs on your GPUs) and Management/Gateway Layers (how you route traffic, track usage, and secure endpoints).

The best choice depends entirely on your scale, hardware, and operational environment.

Phase 1: High-Performance Inference Engines (The Serving Layer)

If your goal is production-grade, multi-user hosting on dedicated servers or local GPU clusters, you need an optimized inference runtime:

1. vLLM (The Production Standard)
   • Best for: High-throughput, multi-user API serving on NVIDIA/AMD hardware.
   • Why it’s great: Features PagedAttention , which optimizes GPU memory fragmentation and drastically increases concurrent user capacity. It spins up an OpenAI-compatible API endpoint instantly.
2. SGLang
   • Best for: Complex agentic workflows, multi-turn chat, and RAG (Retrieval-Augmented Generation) pipelines.
   • Why it’s great: Uses radix tree caching to prevent redundant prefix/document recalculations, yielding massive speed and cost advantages for interactive applications.
3. Ollama / Llama.cpp
   • Best for: Local developer workstations, single-user setups, edge servers, or rapid prototyping.
   • Why it’s great: Incredibly easy to install, supports GGUF models, and runs smoothly across cross-platform hardware (including Apple Silicon via MLX). Note: Ensure your deployments are updated and secured, as older unauthenticated configurations have historically faced exposure risks.

Phase 2: Management & Gateways (The Control Layer)

Once you have models running on an inference engine, you need a management layer for rate limiting, tracking spend/tokens, auth, and fallbacks:

1. LiteLLM
   • Best for: Provider compatibility, drop-in OpenAI replacement, and managing multiple disparate models/keys.
   • Why it’s great: Acts as a self-hosted proxy/gateway translating standard OpenAI calls to your backend vLLM, Ollama, or external APIs with built-in budget tracking and user management.
2. Bifrost or Portkey Gateway
   • Best for: High-performance enterprise production, guardrails, and observability.
   • Why it’s great: Bifrost offers ultra-low overhead routing and governance, while Portkey excels at adding semantic caching, guardrails, and detailed tracing to your self-hosted stack.

Recommended Architecture by Use Case

• For a Local/Dev Environment: Use Ollama paired with a frontend like Open WebUI for a private ChatGPT-like interface.
• For a Scalable Production API Server (NVIDIA GPUs): Deploy vLLM to serve model weights from Hugging Face , fronted by LiteLLM Proxy to manage user routing and API governance.

To help tailor this recommendation, tell me:

• What hardware/GPUs do you have available (e.g., local NVIDIA cards, a cloud cluster)?
• Is this for a single user/local project or a multi-tenant production app?

If your goal is running open-source LLMs on infrastructure you control, my default recommendation in 2026 is:

Best overall: vLLM + Kubernetes

docs.vllm.ai is the best foundation for a serious self-hosted LLM platform. It is an open-source, high-throughput inference engine with an OpenAI-compatible API, and it can scale from a single GPU to multi-node deployments.

I'd build the stack roughly like this:

• Inference: vLLM
• Orchestration: Kubernetes
• GPU scheduling: NVIDIA GPU Operator
• Model storage: S3-compatible object storage
• API gateway: LiteLLM or an API gateway of your choice
• Observability: Prometheus + Grafana
• Web UI: Open WebUI, if humans need direct chat access
• Model registry: Hugging Face Hub or an internal artifact/model registry
• Autoscaling: Kubernetes/Ray depending on scale

This gives you considerable control without locking the whole platform to one model vendor.

If you want the easiest enterprise platform

Consider developer.nvidia.com.

NIM packages models and optimized inference runtimes into containers, provides standard APIs, hardware-aware configurations, Kubernetes deployment options, observability, and enterprise support. NIM LLM 3.0 also adds distributed inference through NVIDIA Dynamo.

The important distinction is:

One particularly attractive architecture is Ray Serve + vLLM if you're operating a substantial cluster: Ray's serving layer adds autoscaling, load balancing, back-pressure, observability, and multi-node scaling on top of vLLM.

What I'd choose

For a company building its own internal AI platform, I'd start with:

Kubernetes → vLLM → OpenAI-compatible API → model registry/object storage

Then add Ray/KServe only when your workload actually needs their additional orchestration capabilities.

If you're 100% NVIDIA GPU, NIM is also compelling—particularly if paying for enterprise support and validated configurations is preferable to maintaining the inference stack yourself. NIM itself is built around upstream open-source technologies including vLLM.

If you tell me your GPU hardware (e.g. 4×H100, 8×A100, RTX 6000s), number of users/requests, and which models you want to run, I can recommend a concrete architecture—including Kubernetes setup, model serving, networking, storage, monitoring, and estimated throughput.

The "best" platform for self-hosting and managing open-source LLMs depends entirely on your use case—whether you are prototyping on a local machine, building a multi-user enterprise production cluster, or setting up a personal home lab.

The self-hosted LLM stack is best understood as a two-layer architecture: the Inference Engine (which loads the weights and generates tokens) paired with the Management/UI Layer (which handles user access, APIs, and orchestration).

Phase 1: Choosing the Inference Engine (The Backend)

1. vLLM (Best for Production & High Throughput)
   • Why it’s great: Industry standard for heavy production workloads. It features PagedAttention, which drastically improves memory efficiency and throughput, and supports continuous batching and tensor parallelism across multi-GPU setups.
   • Best for: High-concurrency environments, multi-user applications, and serving large models (like Llama or DeepSeek variants) efficiently.
   • Get started: Deploy via the vLLM GitHub Repository.
2. Ollama (Best for Simplicity & Local Development)
   • Why it’s great: Makes running local models as easy as running a Docker container or a single CLI command (ollama run llama3 ). It handles model downloading, quantization, and memory management automatically on both Linux/Windows and Apple Silicon.
   • Best for: Local development, small teams, prototyping, and environments where setup friction needs to be zero.
   • Get started: Download from Ollama's Official Website.
3. Hugging Face TGI - Text Generation Inference (Best for Enterprise Ecosystems)
   • Why it’s great: Built by Hugging Face, TGI powers their massive inference infrastructure. It includes robust production features like optimized transformers code, token streaming, continuous batching, and Prometheus metrics.
   • Best for: Teams deeply entrenched in the Hugging Face model hub ecosystem.
4. LocalAI (Best All-in-One Drop-In Replacement)
   • Why it’s great: Acts as a drop-in replacement for OpenAI’s API, but runs locally on consumer or enterprise hardware without needing a GPU. It handles not just LLMs, but also audio transcription, image generation, and embeddings.
   • Best for: Migrating existing OpenAI-dependent apps to local infrastructure with zero code changes.

Phase 2: Choosing the Management & User Interface (The Frontend)

If you need a ChatGPT-like web interface, multi-user management, role-based access control (RBAC), and RAG (Retrieval-Augmented Generation) capabilities on top of your backend engine:

1. Open WebUI (The Gold Standard UI)
   • Why it’s great: It connects seamlessly to Ollama, vLLM , or any OpenAI-compatible API backend. It offers robust multi-user management, fine-grained authentication, integrated web search for RAG, and an intuitive Docker-based installation.
   • Get started: Explore the Open WebUI GitHub Repository.
2. AnythingLLM (Best for Private Knowledge Bases)
   • Why it’s great: An all-in-one desktop or self-hosted application that turns any documents (PDFs, TXT, code) into a private chatbot workspace with built-in vector database management.

Phase 3: Infrastructure Orchestration (For Scale)

If you are deploying across a cluster of servers rather than a single machine:

• KubeAI / Kubernetes + NVIDIA GPU Operator: If your infrastructure already runs on Kubernetes, KubeAI or native container orchestration with NVIDIA tools provides automated scaling, GPU slicing, and lifecycle management for enterprise AI loads.

Summary Recommendation

• For local dev / solo testing: Use Ollama paired with Open WebUI . It takes 5 minutes to set up.
• For a production app with multiple concurrent users: Use vLLM as your backend inference engine, exposed via an OpenAI-compatible endpoint, paired with Open WebUI or a custom backend.

To help narrow down the exact recommendation, tell me:

• What kind of hardware do you have available (e.g., Mac Studio, single NVIDIA A100/RTX 4090, or a multi-node cluster)?
• Is this for personal use/home lab , or a multi-tenant production environment for an organization?

If your goal is serious self-hosting of open-source LLMs on infrastructure you control, my recommendation in 2026 is:

Best overall: NVIDIA NIM + Kubernetes

NVIDIA NVIDIA NIM is probably the best choice if you're running NVIDIA GPUs and want production-grade model serving rather than just experimenting.

NIM packages models, optimized inference runtimes, APIs, health checks, and deployment configuration into containers. The current NIM LLM stack is built around vLLM, and supports Kubernetes, multi-node deployments, air-gapped environments, and custom/fine-tuned models.

How I'd rank the options

The important distinction is that Ollama/Open WebUI aren't really alternatives to vLLM/NIM. Open WebUI is primarily an interface, while Ollama is a very convenient local model runtime. For a production platform, I'd build around an inference server such as vLLM and put a management/API layer around it.

The architecture I'd use

                    ┌──────────────────────┐
                    │      Users / Apps    │
                    └──────────┬───────────┘
                               │ OpenAI API
                               ▼
                    ┌──────────────────────┐
                    │ API Gateway / Auth   │
                    │ Rate limits / quotas │
                    └──────────┬───────────┘
                               │
                    ┌──────────▼───────────┐
                    │      Kubernetes      │
                    └──────────┬───────────┘
                               │
             ┌─────────────────┼─────────────────┐
             ▼                 ▼                 ▼
       ┌───────────┐     ┌───────────┐     ┌───────────┐
       │ NIM/vLLM  │     │ NIM/vLLM  │     │ NIM/vLLM  │
       │ Model A   │     │ Model B   │     │ Model C   │
       └─────┬─────┘     └─────┬─────┘     └─────┬─────┘
             │                 │                 │
             └─────────────────┼─────────────────┘
                               ▼
                       NVIDIA GPU cluster

That gives you a standardized API while letting you swap models underneath it.

When I'd choose plain vLLM instead

If you're comfortable operating infrastructure, vLLM is arguably the better technical choice.

You get a highly optimized, open-source inference engine without tying the serving layer to NVIDIA's packaging and enterprise ecosystem. NIM itself is built on vLLM, and NVIDIA explicitly describes vLLM as its underlying open-source high-throughput inference engine.

I'd choose:

• vLLM → you want maximum control and open-source flexibility.
• NIM → you want someone to solve more of the deployment/optimization/operational burden.
• Ollama → you want "install it and run a model" simplicity.
• Open WebUI + vLLM/Ollama → you want an internal ChatGPT-style application for employees.

NIM is particularly compelling for an enterprise because its current stack includes Kubernetes/Helm deployment, observability, air-gapped operation, validated hardware configurations, and enterprise security/update capabilities.

One important caveat

"NVIDIA NIM" isn't synonymous with "open source." The inference foundation (vLLM, etc.) is open source, but NIM is NVIDIA's packaged/enterprise layer, and individual model weights have their own licenses. So if by "open-source" you specifically mean every component must have an OSI-style open-source license, I'd use Kubernetes + vLLM/SGLang + open model weights instead.

Also, the ecosystem is moving quickly: as of September 2026, NVIDIA has NIM LLM 3.0, while Hugging Face has also just undergone a major corporate development with NVIDIA.

My bottom line: for a company building a private AI platform today, I'd start with Kubernetes + vLLM, and use NVIDIA NIM if you value enterprise packaging, validated configurations, and support more than having the thinnest possible open-source stack.

If you tell me your GPU hardware (e.g. 4× H100, 8× A100, RTX 6000, AMD MI300X), number of users, and whether this is a homelab or enterprise deployment, I can recommend the exact stack and architecture.

                    ┌──────────────────────┐
                    │      Users / Apps    │
                    └──────────┬───────────┘
                               │ OpenAI API
                               ▼
                    ┌──────────────────────┐
                    │ API Gateway / Auth   │
                    │ Rate limits / quotas │
                    └──────────┬───────────┘
                               │
                    ┌──────────▼───────────┐
                    │      Kubernetes      │
                    └──────────┬───────────┘
                               │
             ┌─────────────────┼─────────────────┐
             ▼                 ▼                 ▼
       ┌───────────┐     ┌───────────┐     ┌───────────┐
       │ NIM/vLLM  │     │ NIM/vLLM  │     │ NIM/vLLM  │
       │ Model A   │     │ Model B   │     │ Model C   │
       └─────┬─────┘     └─────┬─────┘     └─────┬─────┘
             │                 │                 │
             └─────────────────┼─────────────────┘
                               ▼
                       NVIDIA GPU cluster

That gives you a standardized API while letting you swap models underneath it.

When I'd choose plain vLLM instead

If you're comfortable operating infrastructure, vLLM is arguably the better technical choice.

You get a highly optimized, open-source inference engine without tying the serving layer to NVIDIA's packaging and enterprise ecosystem. NIM itself is built on vLLM, and NVIDIA explicitly describes vLLM as its underlying open-source high-throughput inference engine.

I'd choose:

Choosing the "best" platform to self-host and manage open-source LLMs (like Meta's Llama , Alibaba's Qwen, or DeepSeek) depends entirely on your scale and technical requirements. The landscape centers on a few dominant tools, categorized by their primary strengths:

1. Production Inference Engines (High Throughput & Concurrency)

If you are serving multiple users, building applications, or running heavy production workloads, raw performance and memory optimization (like PagedAttention) are critical.

• vLLM: The gold standard and industry default for production LLM serving. It features continuous batching and PagedAttention, which drastically cuts GPU memory waste and multiplies throughput under concurrent user load. It also exposes an OpenAI-compatible API out of the box.
• SGLang: An emerging powerhouse that often outperforms vLLM in raw throughput and Time-to-First-Token (TTFT), especially for multi-turn chat applications, RAG pipelines, and complex agent workflows that share context (via RadixAttention).

2. Local & Developer-Friendly (Quick Setup)

If your goal is individual use, local testing, or a zero-hassle developer environment on a private server without configuring Python environments or CUDA wrappers.

• Ollama: Unrivaled for developer experience and simplicity. With a single command, you can download, manage, and run models locally. However, it is built for single-user or light local use cases and doesn't scale well to high-concurrency production environments.

3. Enterprise Control & Orchestration Layers

If you need a control plane to manage multiple models, route traffic, track budgets, enforce access controls, or spin up agent frameworks.

• TrueFoundry: A top choice for enterprise governance, acting as a VPC-native AI gateway that handles audit logs, rate limits, and multi-model routing securely.
• Onyx AI: An open-source enterprise search and AI chat platform that lets you self-host LLMs and seamlessly connect them directly to your team’s internal documents and applications.

Recommendation Summary

• Start here for Production: Use vLLM deployed via Docker for a robust, high-performance, scalable API backend.
• Start here for Local/Testing: Use Ollama if you want a model up and running in under five minutes.

To help narrow down the exact platform architecture you need, tell me:

• What hardware/GPUs do you plan to use?
• Is this for local development , an internal team tool , or a public-facing production app?
• Do you need an all-in-one UI/Chat interface or just a backend API?

If your goal is serious self-hosting on your own servers, my default recommendation in 2026 is:

Best overall: vLLM + Kubernetes

docs.vllm.ai is the strongest foundation for an open-source LLM serving stack. It is a high-throughput inference engine, exposes an OpenAI-compatible API, and has become a major open-source serving project.

For a production environment, I'd build around:

• Model layer: Hugging Face/open-weight models such as Qwen, Llama, DeepSeek, etc.
• Inference: vLLM
• Orchestration: Kubernetes
• LLM serving/control plane: KServe or a comparable Kubernetes-native layer
• GPU scheduling: NVIDIA GPU Operator, optionally Run:ai
• API gateway: LiteLLM or your existing API gateway
• Observability: Prometheus + Grafana + OpenTelemetry
• UI: Open WebUI if humans need a ChatGPT-like interface

KServe specifically recommends vLLM as its backend for LLM serving and supports scalable distributed deployment.

Why I wouldn't call Ollama the "best platform"

ollama.com is fantastic for developer machines, experimentation, and small deployments. It's dramatically easier to get started with.

But once you're operating multiple GPUs, multiple models, multiple users, autoscaling, monitoring, HA, and production APIs, vLLM + Kubernetes gives you substantially more control.

What about NVIDIA NIM?

If you're willing to use NVIDIA's commercial/enterprise ecosystem, NVIDIA NIM is arguably the easiest enterprise answer.

NIM packages models and optimized inference runtimes into containers and currently builds its LLM serving stack around vLLM, with Kubernetes deployment, observability, multi-node deployment, and enterprise support available.

So I'd frame the choices like this:

One important distinction: vLLM isn't really a complete "platform"—it's the inference/serving engine. If by platform you mean "I want something like an internal AI cloud where my team can deploy, manage, scale, monitor, and expose dozens of models," then the architecture matters more than picking a single product.

What I'd deploy

For a company building an internal LLM platform, I'd start with:

                    ┌─────────────────┐
                    │  Applications   │
                    └────────┬────────┘
                             │
                    OpenAI-compatible API
                             │
                    ┌────────▼────────┐
                    │ API Gateway /   │
                    │    LiteLLM      │
                    └────────┬────────┘
                             │
                    ┌────────▼────────┐
                    │    KServe /     │
                    │   Kubernetes    │
                    └────────┬────────┘
                             │
             ┌───────────────┼───────────────┐
             │               │               │
        ┌────▼────┐     ┌────▼────┐     ┌────▼────┐
        │  vLLM   │     │  vLLM   │     │  vLLM   │
        │ Model A │     │ Model B │     │ Model C │
        └────┬────┘     └────┬────┘     └────┬────┘
             │               │               │
             └───────────────┼───────────────┘
                             │
                     NVIDIA GPU cluster

That gives you a relatively clean separation between model management, inference, orchestration, and applications.

If you tell me your GPU hardware (e.g. 4×H100, 8×A100, RTX 6000, AMD, Apple Silicon), number of users, and whether this is a homelab or enterprise deployment, I can recommend a much more specific stack—including which platform to use, Kubernetes vs. bare metal, model sizes, GPU allocation, and an architecture for serving multiple LLMs concurrently.