Effortless AI infrastructure
on your own cloud
Run agents, sandboxes, task queues, and GPU workloads on Beam, or bring your own compute from AWS, GCP, Azure, and Hetzner.
from beam import Sandbox
# Spin up a cloud sandbox in milliseconds
sandbox = Sandbox(gpu="A10G", memory="2Gi").create()
result = sandbox.process.run_code("print('hello from the cloud')")
print(result.result)TRUSTED BY THE BEST AI COMPANIES
Built for speed, at any scale.
Engineered from the ground up for heavy AI workloads. Sub-second cold starts, massive parallelism, and full observability.
Run AI workloads across clouds.
Connect all your cloud accounts to Beam, and run workloads across all of them. Achieve the highest cloud utilization, lowest cost, and maximum scale.
Sub-second cold starts.
Memory snapshots restore GPU containers in seconds — up to 35× faster than a traditional cold boot.
Run near your agents.
Workloads route across clouds and regions in real time, for low-latency execution wherever your users are.
Snapshot, branch, restore.
Snapshot a running sandbox, then restore it into thousands of concurrent isolated runs, each with realtime streaming output.
Build AI Products
Logic and hardware in one place — no YAML, no Dockerfiles, no infra to manage.
Sandboxes for AI Agents
Sandboxes are stateful. You can connect to a running process, attach persistent storage volumes, and snapshot the file system to create reusable templates.
from beam import Image, Sandbox
sb = Sandbox().create()
image_id = sb.create_image_from_filesystem()
sb.terminate()
sb = Sandbox(image=Image.from_id(image_id)).create()Durable Task Queues
Control the full lifecycle of a task with automated retries and event-based callbacks to your application.
from beam import task_queue
@task_queue(gpu="A10G", callback_url="ngrok.io")
def transcribe():
model = whisper.load_model("small")
model.transcribe("./meeting-notes")GPU Inference
We provide distributed storage layer, memory snapshotting, and GPU checkpoint restore, resulting in lightning fast container boot times.
from beam import QueueDepthAutoscaler
# Scale out when queue size > 30 tasks
autoscaling_config = QueueDepthAutoscaler(
tasks_per_container=30,
max_containers=300,
)Deploy across clouds.
Bring your own cloud. No lock-in.
Run your code on any hardware in seconds — just change one line of Python to switch hardware.
Test your code before deploying it, using the exact configuration you'll run in production.
Scale vertically by running multiple workers on the same container.
Run the full Docker daemon in your containers.
Deploy your APIs automatically by adding Beam to your existing CI/CD pipeline.
One platform for sandboxes, inference, and training.
Custom Model Inference
Host any custom model on GPU or CPU. Bring your own image.
Sandboxed Code Execution
Run LLM-generated code in secure execution environments.
Training & Fine-Tuning
Train and fine-tune, from SLMs and LLMs to diffusion models.
Audio Processing Pipelines
Deploy task queues to process large amounts of data.
Streamlit and Gradio UIs
Run frontend apps, from Streamlit and Gradio apps to Notebooks.
Web Scraping
Run Chromium instances — headed or headless — at scale.
Join our community.
From solo builders to teams shipping at scale.
Beam is powering hands-down the best developer experience to run models on GPUs easily at scale. Best decision on the infra side for us this year so far.
@beam_cloud is 🔥. Such a huge workflow improvement over AWS Sagemaker / Google vertex ai
One of the better developer experiences I've had in a while was with @beam_cloud - a serverless GPU and API infra platform. Check them out 👇
Deploy an open source model on hugging face running on GPUs in a few minutes with 6 lines of code.
Keep your eyes on these guys 👀
I can't recommend Beam highly enough. Their developer experience is top notch.
We never could have shipped Happy Accidents as quickly as we did without them. We were able to build the GPU portion of our app in hours instead of weeks.
Not only is the platform great, we loved working with the Beam team. They're extremely responsive, so we had a high level of confidence in the reliability of the platform.
Beam has been a huge time-saver by eliminating the need to monitor and manage my own VM infrastructure.
I no longer worry about unexpected bugs or outages which means less downtime and fewer headaches.
This lets me provide a significantly more reliable service to my users, and it's been surprisingly more cost-efficient than my prior solution.
Time is the biggest thing Beam has helped us with. I went from spending 6 hours developing an API to pressing a button and deploying instantly
Spun up a new app today and realized just how it easy it was. Took me only 15 mins to organize and deploy on Beam.
Realizing that quick python apps on Beam is a cheat code
Beam has been a revelation in terms of making it simple to build an ML application on GPU
Frase is running language models exclusively on Beam and it was surprisingly easy to migrate, less maintenance, and is saving us money because unlike Google and other cloud providers, Beam is able to provide us with an on-demand solution that scales immediately with our traffic, and we don’t need to worry about any of the clunky tooling around GPUs.
Beam is amazing. I tested the CLI and in 5 minutes had something running on the cloud.
And the Slack community is a game changer because when we get stuck we get responses quickly
If you're looking to dip your toes into building something with AI, definitely take a look at http://beam.cloud.
Serverless functions with access to GPUs so you can run jobs on-demand and pay only for what you use.
And it's *much* easier than setting up a VM somewhere!
