Run Stable Diffusion and Flux as an API

Host SDXL, Flux, or any diffusion checkpoint behind a cloud API.

Fast inference for image generation

Everything you need to serve generative models at scale.

Serve any checkpoint or LoRA
SDXL, Flux, SD 1.5, or your own fine-tuned weights — load from Hugging Face or a volume, and stack LoRAs at runtime.

GPU Checkpoint Restore
Speed up cold boots by creating snapshots of the GPU state. No need to reload model weights back into vRAM when your container spins up.

Scale with bursts
Image traffic is spiky. Endpoints scale out on demand and back to zero.

Hosted image outputs
Save results with Output and return public URLs straight from your endpoint — no need to push results to S3.

Weights cached in volumes
Keep multi-gigabyte checkpoints on a persistent volume shared across containers. Storage is included, free of charge.

GPUs from 4090s to B300s
Run lighter checkpoints on 4090s and reserve B300s for big models — one line to switch.

“Switching to Beam has been really helpful. It has addressed all the pain points we had with Replicate, enabling us to provide a better, more reliable service to our customers.”
Simon Brami Founder, Gepetto

5xfaster cold boots than their previous provider
$1,000ssaved monthly

99.9%production uptime

How it works

Ship an image API in three steps

01

Load the pipeline once

Load your checkpoint when the container boots — requests reuse the warm pipeline instead of reloading weights.

02

Deploy the endpoint

beam deploy gives you an authenticated HTTPS URL backed by autoscaling GPUs.

03

Generate over HTTP

POST a prompt, get back a hosted image URL. Beam handles queuing, scaling, and auth.

$ beam deploy app.py:generate
=> Deployed 🎉
=> https://sdxl-abc123.app.beam.cloud

$ curl -X POST https://sdxl-abc123.app.beam.cloud \
    -H 'Authorization: Bearer YOUR_TOKEN' \
    -d '{"prompt": "isometric voxel city at dusk"}'

{"url": "https://app.beam.cloud/output/id/8b0d..."}

Frequently asked questions

Which models can I host?+

Anything diffusers can load: SDXL, SD 1.5, Flux, plus your own fine-tuned checkpoints and LoRAs.

How does this compare to per-image APIs like Replicate or fal?+

Per-image platforms charge a markup on every generation, and your model options are limited to their catalog. On Beam you deploy your own checkpoints and LoRAs, pay per-second for the GPU while it's generating, and your code stays private.

How are images returned?+

Save them with Output and return a hosted public URL, or base64-encode them into the JSON response.

What about cold starts with large checkpoints?+

Keep weights on a volume so containers skip the download, and use on_start so each container loads the pipeline exactly once.

Can I keep an endpoint warm?+

Yes — set keep_warm_seconds to hold containers hot for latency-sensitive apps, and let everything else scale to zero.

Do I need to manage queues for burst traffic?+

No. Autoscaling is managed by Beam — set your scaling policy and containers are added as request volume grows.