4 Easy Ways to Deploy an LLM (and When to Use Each in 2027)

Getting a model to answer a prompt in a chat is simple: it’s the equivalent of a controlled demo. Getting it to answer real users from a stable endpoint with predictable costs-that’s a real product. You have to match GPU drivers to the right compute software (CUDA for NVIDIA, ROCm for AMD), choose and tune a serving engine like vLLM, and set scaling rules so the endpoint stays up when traffic spikes-all before a request even lands.

The good news? There are easy ways to deploy an LLM that let you skip most of that work-sometimes all of it. The right path will depend on how much control you want and what traffic you expect. Let’s explore four ways to deploy an LLM on DigitalOcean and when to use each.

Key takeaways:

• Deploying an LLM means running model weights behind an inference engine and an API endpoint on infrastructure capable of serving real traffic.
• The easiest ways to deploy an LLM remove the infrastructure work entirely or package it into a ready-to-run image or app, so you launch a working server instead of installing drivers and a serving engine yourself.
• Choose your path based on traffic patterns, model and hardware control requirements, and whether you pay per token or per GPU-hour.
• This LLM deployment guide covers Serverless Inference, 1-Click Models on GPU Droplets, the Ollama and Open WebUI 1-Click App, and Dedicated Inference.

What does it mean to deploy an LLM?

Deploying a large language model (LLM) means making it available to users outside of a development environment.

Deployments typically have four parts:

1. Model weights
2. An inference engine to load the weights and generate responses
3. An API layer to accept requests, and
4. GPU or CPU infrastructure

You can use managed services to handle some or all of these parts for you. You’ll ship faster by managing fewer aspects, but you give up some control over the model and hardware.

Easy ways to deploy an LLM

These four paths range from managing no infrastructure at all to running your own GPU server. Most teams start with a managed API and move to dedicated GPUs as traffic increases or requirements get more specific.

Pricing and availability are based on DigitalOcean documentation as of October 28, 2026

1. Call a model through Serverless Inference

Serverless Inference is the quickest way to put an LLM behind your application. That’s because there’s nothing to deploy. You send requests to an OpenAI-compatible API, and DigitalOcean runs the model on its infrastructure. The Model Catalog includes 85+ open and frontier models, including GPT, Claude, Llama, DeepSeek, Qwen, and Kimi (with text, image, and audio outputs). You pay per input and output token from a prepaid balance.

Prompt caching, now generally available, can cut the cost of context your app resends by up to 90%. Our Serverless Inference deep dive traces a request from the API gateway through Ray and vLLM, including where caching and Inference Router fit in.

To get started:

1. Create a model access key in the control panel, optionally scoped to specific models.
2. Point your existing OpenAI-compatible client at the Serverless Inference endpoint.
3. Choose a model.
4. (Optional) Set the model to the Inference Router (public preview). This sends each request to the best-fit model based on your cost or quality preference.

Best for: Prototypes, spiky or unpredictable traffic, and teams that want open and frontier models behind one API because you pay only for the tokens you use and don’t need to manage a GPU.

Note that you’re limited to models in the catalog, and per-token costs can add up under steady, high-volume traffic.

Need to run your own fine-tuned weights? With Bring Your Own Models (BYOM), you can import a model from Hugging Face or a DigitalOcean Spaces bucket into the Model Catalog and deploy it on Dedicated Inference. Imported weights must use the Safetensors format (a standard file format for model weights).

2. Launch a 1-Click Model on a GPU Droplet

1-Click Models provide the ability to use an open model that runs on your own GPU Droplet without a need to build the serving stack yourself. The hard part of self-hosting is usually the setup, not the model, so DigitalOcean packages that setup into one image. DigitalOcean’s Inference Ready image comes with Docker and vLLM (an open-source inference engine) preinstalled, downloads your chosen model from Hugging Face, and supports speculative decoding and FP8 quantization (two techniques that speed up responses and cut GPU memory use) out of the box. The end result? An OpenAI-compatible endpoint on a GPU you control, billed per Droplet hour.

To get started:

1. Create a GPU Droplet from the 1-Click Inference Ready image.
2. Connect through the Web Console or SSH and run the included setup script.
3. Enter a Hugging Face token and pick a model from the list or any Hugging Face model you have access to.
4. Wait for the API to report ready.

Best for: Teams that want a specific open-weight or fine-tuned model and full control over the server because you choose the model, the GPU, and every serving setting.

Note that the single-GPU image supports H100 and H200 GPUs, with one model per Droplet; you’re responsible for updates, scaling, and uptime.

3. Run Ollama and Open WebUI for private prototypes

The Ollama with Open WebUI 1-Click App sets up a private, ChatGPT-style interface on a single Droplet. Ollama downloads and runs open models with a single command, and Open WebUI adds a browser-based chat window with user accounts on top. It runs on a standard CPU Droplet, so it’s best for small models and internal tools rather than production APIs.

If you’re working with larger models, follow our tutorial to run LLMs with Ollama on H100 GPUs, which moves Ollama onto a GPU Droplet that can handle bigger models than a CPU Droplet.

To get started:

1. Deploy the 1-Click App from the DigitalOcean Marketplace.
2. Open the Droplet’s IP address in a browser and create the first account, which becomes the admin.
3. Retrieve a model through Ollama and start chatting.

Get started with our beginner-friendly walkthrough on how to deploy Phi-3 with Ollama and Open WebUI.

Best for: Prototypes, internal chat tools, and testing models privately because it runs on a single low-cost Droplet and your prompts stay on it.

Note that the CPU Droplets run larger models slowly, and you’ll want to set up HTTPS and user approvals before sharing access.

4. Scale up with Dedicated Inference

Dedicated Inference runs your chosen model on GPUs reserved for you, while DigitalOcean manages the serving layer. You pick an open-source or commercial model (including gated Hugging Face models), a GPU plan, and how many GPU instances to run, then send requests to a private or public OpenAI-compatible endpoint. Billing is per GPU-hour rather than per token, which makes costs predictable for steady traffic.

Read our Dedicated Inference technical deep dive to learn how it runs vLLM with llm-d on Kubernetes and routes requests to reuse cached work.

To get started:

1. In the control panel, open Inference Engine, choose Dedicated Inference, and select Deploy.
2. Pick a region, model, GPU plan, and GPU count.
3. Copy the endpoint access key while the deployment provisions.
4. Send requests to the chat completions endpoint.

Best for: Steady, high-volume production traffic and latency-sensitive applications because reserved GPUs give you consistent performance and a flat per-GPU-hour cost.

Note that it’s currently available in the ATL1, NYC2, and TOR1 regions, and you pay for reserved GPU time whether or not requests arrive.

How to choose an LLM deployment path

The right path for your LLM deployment is less about your model and more about traffic, budgets, and how much control you need.

Here’s how to narrow down your options:

• If you want a working endpoint today and traffic is unpredictable, start with Serverless Inference. Per-token billing means you don’t pay for idle GPUs while you validate demand.
• If you need a specific open-weight or fine-tuned model, use a 1-Click Model on a GPU Droplet or Dedicated Inference. Both let you serve models from Hugging Face rather than choosing from a fixed catalog.
• If you’re testing models for internal use, the Ollama and Open WebUI app gives your team a private chat interface on a single Droplet. It’s a low-cost way to compare models before you commit to one.
• If your traffic is steady and high-volume, compare per-token and per-GPU-hour costs using your real request numbers. Past a certain volume, Dedicated Inference is usually easier to budget.
• If you’re weighing other providers, hyperscalers like AWS make sense when your data and compliance controls already live there, while GPU clouds like RunPod suit teams that mainly need raw GPU capacity. DigitalOcean fits teams that want Serverless Inference and Dedicated Inference alongside the databases, storage, and Kubernetes their app needs, on one platform and one bill.

How to pick your first model

Always start smaller than you think. Small open models handle chat, summarization, and extraction tasks, fit on a single GPU, and cost much less to run than larger models. At publication, options include Ministral 3 14B and Nemotron Nano 12B v2 VL in Serverless Inference, plus small open models in the 1-Click Models menu. Smaller models also load faster, so you can iterate quickly while you’re still learning what your application needs.

Move to a larger model when evaluations show the smaller one falls short on your real tasks. And if you need a place to run your small model, consider the options for hosting your open-source model under 10B. If you only need to call a model rather than host it, Serverless Inference lets you compare sizes from Ministral 3 14B up to gpt-oss-120b through the same API before you commit.

When you’re ready for more complex deployments

Moving past the easy paths doesn’t mean moving off DigitalOcean. Dedicated Inference, DigitalOcean Kubernetes, managed databases, and storage run on the same platform, so you can scale without re-architecting your app or adding vendors.

These signals let you know when it’s time:

• Per-token bills become steady and predictable. That’s usually the point to compare Serverless Inference with Dedicated Inference. Read our guide to serverless vs. dedicated vs. batch inference to understand the trade-offs.
• Latency or throughput targets get stricter. Batching raises throughput, quantization cuts memory use, and speculative decoding lowers latency, often before you need more hardware.
• You need to run multiple models or replicas. Kubernetes-based serving with llm-d (an open-source framework for distributing LLM inference across a cluster) gives more control in exchange for more operations work.
• You can’t tell what’s going wrong. Once real users arrive, LLM observability tools help you track latency, cost, and output quality.

LLM deployment FAQ

What’s the easiest way to deploy an LLM?

The easiest way is to call a hosted model through a serverless inference API, because there’s no GPU or serving engine to set up. On DigitalOcean, Serverless Inference gives you access to 85+ open and frontier models behind one OpenAI-compatible endpoint, billed per token. If you need to run your own open model, a 1-Click Model on a GPU Droplet comes with vLLM preinstalled.

Where can I deploy a Hugging Face model to a production GPU endpoint quickly?

DigitalOcean offers two quick routes: 1-Click Models (which launch a GPU Droplet with vLLM and download your chosen Hugging Face model) and Dedicated Inference (which serves the model on reserved GPUs behind a managed endpoint). Gated models work too once you add a Hugging Face token.

Which inference provider lets me move from a serverless prototype to a dedicated production endpoint without re-architecting?

Look for a provider that offers both serverless and dedicated inference behind the same OpenAI-compatible API format. On DigitalOcean, you can prototype with Serverless Inference and move to Dedicated Inference when traffic steadies, mostly by changing the endpoint and access key in your client. Both run on the same platform as your databases, storage, and Kubernetes clusters.

How do I move from a single GPU VM running Ollama to a managed inference endpoint when traffic grows?

First, check whether your model is available in a managed catalog. If it is, Serverless Inference lets you switch by changing the endpoint your app calls, with no servers to run. If it isn’t, Dedicated Inference serves the same open-weight model on GPUs that DigitalOcean manages. Point your app at the new endpoint and confirm it works before you retire the Ollama Droplet.

Where should I host inference for a small open-source model under 10B parameters?

Models under 10B parameters fit on a single GPU, so you rarely need a multi-GPU setup. If the model is already in DigitalOcean’s Model Catalog, Serverless Inference is the simplest option. If it isn’t, a 1-Click Model on a single-GPU Droplet gives you full control over the model and its configuration.

Deploy your LLM on DigitalOcean

The easiest deployment path is the one you don’t have to replace later. DigitalOcean, the AI-Native Cloud, puts Serverless Inference, GPU Droplets, Dedicated Inference, and the databases and storage your app needs on one platform and one bill. You can start with a single API call and grow into dedicated GPUs without switching providers.

Teams already run production AI this way on DigitalOcean:

• Hippocratic AI, which builds voice agents for healthcare, reported 40% lower latency on DigitalOcean while powering over 20 million patient interactions, as of June 2026.
• Workato’s AI Research Lab reported a 67% reduction in inference cost per million tokens after moving workloads to DigitalOcean GPU Droplets with NVIDIA H200 GPUs, as of November 2026.
• ACE Studio reported reducing inference latency by 40%, from 2,500ms to 1,500ms, on DigitalOcean, as of April 2026.

Results in customer environments may vary depending on configuration, implementation, and usage. Results and/or savings are not guaranteed.

Key features include:

• 85+ open and frontier models behind one OpenAI-compatible endpoint
• 1-Click Models with vLLM preinstalled on GPU Droplets
• Dedicated Inference for steady, high-volume production traffic
• Inference Router to send each request to the best-fit model
• Databases, storage, and Kubernetes on the same network and bill

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