The right LLM hosting platform depends on how much control you need over the model, the data and the country where both are stored, and how much server work your team can take on. This page lists ten LLM hosting options in no particular order, from self-hosted serving software to managed endpoints and serverless APIs.
Hosting your own large language model means running an open model on infrastructure you choose, instead of sending every request to a closed API. People do it for privacy, for control over the model version and to keep data in a particular country.
The ten options below are listed alphabetically and in no particular order. Every description comes from the provider's own website, read this week. If you want to try a model on your laptop first, my guide to running models locally is the quickest start.
LLM hosting platforms at a glance
I checked the free plan column on each provider's own website, as of Oct 2026. Yes means the page I read names a free plan, free tier, free credits or open source software. Trial only means a free trial or starter credit. Not stated means the pages I read named neither, even where there's a button inviting you to start, so check the provider's page before you sign up. A free tier rarely covers the biggest GPUs.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Cloudflare Workers AI | Serverless AI inference | Open models at the network edge | Yes |
| DigitalOcean GPU Droplets | GPU virtual machines | Your own model server on a GPU VM | Not stated |
| Elestio | Managed open-source software hosting | Managed LLM stacks on any cloud | Not stated |
| GMI Cloud | AI cloud for compute and inference | Serverless start, dedicated GPUs later | Not stated |
| Hugging Face Inference Endpoints | Managed model endpoints | Deploying Hub models to an endpoint | Not stated |
| Nebius Token Factory | Open model inference platform | Comparing and serving open models | Not stated |
| OVHcloud AI Endpoints | Generative AI API from a European cloud | Open models through a European API | Not stated |
| Prem | Sovereign AI platform | Private and encrypted inference | Not stated |
| Scaleway Managed Inference | Dedicated inference in Europe | Dedicated deployments hosted in Europe | Not stated |
| vLLM | Open-source inference engine | Serving an LLM on your own GPUs | Yes |
Ten LLM hosting options worth a look
Cloudflare Workers AI
Best for: Web teams who want to call open language models on serverless GPUs from Cloudflare's network, with a free plan named on its page.
DigitalOcean GPU Droplets
Best for: Small companies that want AMD or NVIDIA GPU virtual machines they can spin up in seconds to run their own model server.
Elestio
Best for: Founders who want managed open-source AI tools and GPU inference software deployed on the cloud of their choice.
GMI Cloud
Best for: Startups that want to begin with serverless inference and move to dedicated GPU clusters as traffic grows.
Hugging Face Inference Endpoints
Best for: Teams already on the Hugging Face Hub who want to deploy a model from the catalog to a managed endpoint.
Nebius Token Factory
Best for: Product builders who want to compare open text, code and reasoning models, then serve or adapt them with their own data.
OVHcloud AI Endpoints
Best for: European businesses that want to call generative AI models through an API from a cloud provider based in Europe.
Prem
Best for: Regulated firms in healthcare, finance, legal and government that want encrypted inference and models customised on private data.
Scaleway Managed Inference
Best for: EU companies that want AI models on dedicated inference infrastructure, with all hosting kept in Europe.
vLLM
Best for: Engineers who want to self-host an LLM on their own GPUs with one of the most active open-source serving projects.
Which option fits the job
- Open models called from web code: Cloudflare Workers AI.
- Your own model server on a GPU VM: DigitalOcean GPU Droplets.
- Managed open-source AI software on any cloud: Elestio.
- Serverless first, dedicated GPUs later: GMI Cloud.
- Comparing open models before you commit: Nebius Token Factory.
- An API from a European cloud: OVHcloud AI Endpoints.
- Encrypted inference for regulated work: Prem.
The model licence travels with the model
Meta's Llama 3.1 licence says that if you distribute or make available Llama materials, or a product that contains them, you must provide a copy of the agreement and prominently display Built with Llama on a related website, user interface, blogpost, about page or product documentation.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
Hosting an open model isn't the same as owning it. Wherever you run it, on your own GPUs or a managed platform, the model's licence still applies to your product. Read the licence of the exact model and version you deploy, note any attribution or usage limits, and keep a copy with your product documentation so nobody has to guess later.
Self-hosted, managed endpoint or serverless API?
Self-hosting on your own GPUs gives you the most control over the model, the data and the version, and the most work keeping it all running.
A managed endpoint runs the model you pick on dedicated hardware the provider looks after. You choose the model and size, they handle the servers.
A serverless API runs open models on shared infrastructure. It's the fastest way to start, with the least control over hardware and location.
Six questions before you host an LLM
- Which model and version will you run, and under what licence?
- How much GPU memory does it need?
- Which country must the data stay in?
- Who patches and updates the serving software?
- How will you measure response times and errors?
- Can you swap models later without changing your app?
Do you make LLM hosting? Tool makers can ask to be considered for a featured place on this list.
My plain advice on hosting your own LLM
Start with a managed endpoint or serverless API, and only self-host once you've proved the model works for your users. Running GPUs yourself is real operational work, and it only pays back when privacy, control or steady volume demands it.
Whichever route you take, write down the model licence and the data location before launch. My guide to Mistral AI looks at one of the open-weight model makers people host most.
Frequently asked questions
What is LLM hosting?
It's running a large language model on servers or a platform you choose, so your app sends requests to your own deployment.
Why host an LLM instead of using an API?
People host their own model for privacy, to keep data in a particular country, to fix the model version or to customise it.
Can I host an LLM for free?
Open-source serving software is free to use, and some platforms name free plans, but GPUs for larger models usually aren't free.
Do open models have licence rules?
Yes. Meta's Llama 3.1 licence, for example, asks you to display Built with Llama when you make a product containing it available.
Where should I host an LLM for European customers?
Look at providers with data centres in Europe and check their data terms, especially if the model handles personal information.
What do I need to self-host an LLM?
You need a GPU with enough memory for the model, serving software and someone to keep it updated and monitored.
