Deploy open-source LLMs
Deploy GLM 5.2, DeepSeek, Qwen 3.7, or any open-source LLM as a production API in minutes.
Production LLM serving, without the infrastructure
Everything you need to run open-source LLMs at scale.
“Beam's infrastructure makes productizing our AI straightforward. We can scale up or down when needed, test new GPU options, and keep our users happy.”
Deploy an LLM in three steps
Define your app in code
Use the managed vLLM integration — or wrap any inference code in an endpoint decorator — and declare GPU, memory, and image in Python or TS.
Deploy with one command
beam deploy ships it to serverless GPUs. No Dockerfiles, no Kubernetes, no YAML.
Call it from any OpenAI client
Point your existing SDK at the endpoint URL. Auth, autoscaling, and telemetry are handled for you.
# 1. Deploy the server
$ beam deploy app.py:llama
=> Deployed 🎉
=> https://llama-3-1-8b-abc123.app.beam.cloud
# 2. Call it from any OpenAI client
from openai import OpenAI
client = OpenAI(
base_url="https://llama-3-1-8b-abc123.app.beam.cloud/v1",
api_key="YOUR_BEAM_TOKEN",
)
response = client.chat.completions.create(
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
messages=[{"role": "user", "content": "Hello!"}],
)Frequently asked questions
Which models can I serve?
Any open-source or custom model — GLM 5.2, DeepSeek, Qwen, or your own fine-tuned weights. Use the vLLM integration for an OpenAI-compatible server, or wrap any inference code in an endpoint decorator.
Do I pay while the endpoint is idle?
No. Endpoints scale to zero when idle and billing is per-second, so you only pay while requests are being processed. If you're latency-sensitive, you can keep containers warm and control exactly how long they stay up.
Am I billed for cold starts?
No. Beam only charges for the time it takes to load your application code — never for server spin-up or container image pulls.
Can I keep a server running 24/7?
Yes. By default apps spin down after each request, but you control the idle timeout with keep_warm_seconds — or set min_containers to keep capacity always on. Many teams with latency-sensitive workloads run their LLM servers around the clock on Beam.
Is the API OpenAI-compatible?
Yes, when you use Beam's vLLM integration. Your deployed endpoint speaks the OpenAI API, so existing SDKs and client libraries work by changing only the base URL.
Can I stream tokens back to my users?
Yes. The vLLM integration streams through the standard OpenAI API.
How do I monitor my deployment?
The dashboard shows request volume, latency, container counts, and resource utilization per deployment, with streaming logs for every task. Deployments are versioned, so you can roll back to a previous version as needed.
Can I run this in my own cloud?
Yes. Beam's engine is open source (Beta9), and you can self-host it on your own VPC or connect your own hardware — same SDK, same decorators, your infrastructure.
Related use cases
Deploy any Hugging Face model as an API
From model ID to live, autoscaling endpoint with one Python file.
Training & Fine-tuningFine-tune models on GPUs
LoRA and QLoRA fine-tuning on serverless GPUs.
InferenceServe embedding models for your RAG stack
Host embedding and reranking models on your own endpoints.
Start shipping on infra
you won’t outgrow.
Run sandboxes and GPU workloads on your cloud, and scale out to ours when you need to. No infra to manage.