Beam Cloud
Serverless GPU compute for AI agents, inference, and sandboxes.
Beam offers the fastest path from Python code to production GPU inference, with memory snapshots that slash cold starts and a developer experience that's genuinely pleasant. If you need any GPU beyond RTX 4090, A10G, or A100 – or require HIPAA compliance – you'll want to keep looking.
- AI developers deploying GPU inference APIs quickly
- Teams running sandboxed LLM-generated code execution
- Developers needing serverless GPU task queues for data processing
- Prototyping and deploying custom model fine-tuning workflows
- Enterprise teams requiring HIPAA compliance out of the box
- Users needing bare-metal GPU performance without virtualization overhead
- Teams that want a fully managed multi-region auto-scaling solution without vendor lock-in
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Skip Beam if you need HIPAA-compliant GPU infrastructure out of the box or require deep VPC peering and custom firewall rules without manual configuration.
Going past 5 GPU containers on the Developer tier requires upgrading to Team ($89/mo) — a jump if you need more concurrency.
Beam's Developer tier ($0/mo + $30 credits) is great for solo developers testing GPU workloads. The Team tier ($89/mo) beats alternatives like E2B's Team plan ($99/mo) for GPU concurrency (50 vs 25 containers) and includes $30 credits. Growth (custom) is for high-volume users. For steady-state 24/7 inference, compare hourly dedicated rates (RTX 4090 at $0.42/hr) against serverless per-second billing to decide.
In short
Beam Cloud — Serverless GPU compute for AI agents, inference, and sandboxes. Best for AI developers deploying GPU inference APIs quickly, Teams running sandboxed LLM-generated code execution, Developers needing serverless GPU task queues for data processing. Free to start; paid plans from $89/mo.
What's new in Beam Cloud
Checked 15 days agoAcross the latest 10 updates: 4 feature updates, 2 pricing changes and 4 news mentions.
Batch Inference on Serverless GPU
Tutorial: fan out batch inference across GPUs with one .map() call.
How to Deploy ComfyUI as an API
Tutorial: turn ComfyUI workflow into callable HTTP API on serverless GPUs.
Modal Pricing Explained (2026): Plans, GPU Rates, and Why Serverless Gets Expensive at Scale
Analysis of Modal's 2026 pricing tiers, GPU rates, and cost comparison with Beam.
How to Set Up GLM 5.1 for Coding Agents
Tutorial: install Z.ai's open-weight coding model GLM 5.1 into Cursor and Claude Code.
ChatGPT Enterprise Pricing Guide (2026)
Overview of OpenAI plans including Enterprise per-seat costs and feature comparisons.
Best Sandbox Providers for Reinforcement Learning in 2026
Comparison of sandbox providers for RL: GPU support, parallelism, BYOC, docker-in-docker.
NVIDIA B200 Pricing (June 2026)
Cloud pricing for B200: on-demand rates $3.70–$14/hr, Beam at $3.93/hr.
NVIDIA RTX A6000 Pricing (June 2026)
Cloud pricing for RTX A6000: on-demand rates, vs L40S, Beam at $0.51/hr.
E2B Pricing Explained (2026): Tiers, Limits, and Cheaper Alternatives
E2B pricing analysis: Hobby vs Pro tiers, session limits, and cheaper alternatives with GPU.
How to Self-Host a Code Execution Sandbox for AI Agents (2026)
Guide to self-hosting code execution sandboxes: isolation, GPU, orchestration, Beam vs E2B etc.
Viability Score
How likely is Beam Cloud to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Serverless GPU sandboxes (A10G, A100, H100, H200, B200, RTX 4090, L40S, A6000, RTX PRO 6000, RTX 5090)
- GPU inference endpoints with autoscaling
- Durable task queues with retries and callbacks
- Memory snapshots for sub-second cold starts (up to 35× faster)
- Docker-in-Docker support
- Distributed storage volumes
- One-line hardware switching
- Local debug emulation matching production config
- Multiple workers per container (vertical scaling)
- Deploy from GitHub Actions CI/CD
- Scheduled cloud functions (cron jobs)
- Multi-cloud deployment (AWS, GCP, Azure, Hetzner)
- 30+ regions globally
- Secrets management
- SOC 2 Type II certified
About Beam Cloud
Beam Cloud is a serverless GPU platform that lets developers deploy Python code with GPU access in seconds using a simple SDK and CLI. It specializes in running AI agents, inference endpoints, task queues, and sandboxed code execution without managing cloud infrastructure. Key features include memory snapshots for sub-second cold starts (up to 35× faster than traditional cold boot), Docker-in-Docker support, one-line hardware switching across GPUs (RTX 4090, A10G, A100, H100, H200, B200, L40S, RTX PRO 6000, A6000, RTX 5090), and distributed storage volumes. Beam supports multi-cloud deployment — bring your own AWS, GCP, Azure, or Hetzner account or burst to Beam’s on-demand fleet across 30+ regions. The platform includes durable task queues with retries and callbacks, autoscaling inference endpoints, and scheduled cloud functions (cron jobs). It also offers a local debug emulator that matches production configuration. Beam is SOC 2 Type II certified and trusted by AI startups. Pricing is per-second for serverless GPUs (RTX 4090 at $0.000192/s, A10G at $0.000292/s) or per-hour on-demand (from $0.42/hr for RTX 4090 up to $3.93/hr for B200). Three usage tiers exist: Developer (free with $30 monthly credits, 5 GPU containers), Team ($89/mo, 50 GPU containers, 3 seats), and Growth (custom pricing). Compared to alternatives like E2B and Modal, Beam offers stronger GPU variety, memory snapshots, and the flexibility to run on your own cloud accounts.
Behind the Verdict
We'd reach for Beam when we need to get a GPU-based API into production in minutes, not days. The SDK is clean, the local emulator works, and memory snapshots make cold starts nearly invisible. For prototyping or light production workloads, the free tier ($30/mo credits) is generous enough to stay within for quite a while. Where it bites: the free tier limits you to 5 concurrent GPU containers. If your app needs to burst beyond that consistently, you're looking at the Team tier at $89/mo. Also, while Beam supports a wide range of GPUs, the serverless pricing (per-second) is only available for RTX 4090 and A10G; other GPUs are on-demand only, which may be pricier for spiky workloads. Compared to Modal, Beam is more opinionated about Python (nice if that's your stack) and offers more GPU variety. Modal's free tier is more generous (up to 30 containers), but Beam's memory snapshots give it an edge for latency-sensitive apps. E2B is cheaper for sandbox-only use cases, but lacks the same depth of inference and task queue features. In practice, Beam shines for teams that want to iterate fast on AI features without hiring a DevOps person. The ability to bring your own cloud (AWS, GCP, Azure, Hetzner) reduces lock-in risk, though you'll need to manage those accounts. If you need HIPAA compliance or deep VPC networking, you'll need to self-host or look elsewhere.
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Real-world workflow fit
Concrete scenarios for the personas Beam Cloud actually fits — and what changes day-one when you adopt it.
You need to deploy a vLLM inference endpoint that autoscales with queue depth, supports 300 concurrent containers, and costs nothing when idle.
Outcome: Deploy a single Python script with Beam's SDK, set QueueDepthAutoscaler for 30 tasks per container, and achieve sub-second cold starts via memory snapshots. Pay only for compute seconds used, with zero cost when idle.
You need to run untrusted LLM-generated Python code securely at scale, with GPU support and stateful environments.
Outcome: Use Beam Sandboxes to spin up isolated GPU containers in milliseconds, snapshot memory for reuse, and terminate after each run. No infrastructure to manage, and you can bring your own cloud for data residency.
You have a custom training script and want to run it on a H100 GPU without dealing with cloud setup, then view logs and metrics.
Outcome: Define a Beam task queue with one-line GPU selection (H100), upload your dataset to a distributed storage volume, and run the training job. Logs and metrics are streamed to Beam's dashboard, and you pay only for the compute used (per second).
Use Cases
- Deploy a Hugging Face model as a scalable REST API in minutes
- Run batch image generation using Stable Diffusion on demand
- Set up a streaming LLM endpoint for chat applications
- Execute scheduled GPU training jobs without managing servers
- Create a real-time video processing pipeline with parallel GPU tasks
- Fine-tune a transformer model on custom data with automatic logging
- Run LLM-generated code in secure sandboxed environments
- Host a Streamlit or Gradio UI with GPU backend
Models Under the Hood
as of 2026-07-14
Limitations
- Free tier includes $30 monthly credits, but heavy GPU usage burns credits fast.
- Per-second billing can become expensive for always-on workloads; dedicated GPU instances are more cost-effective for heavy continuous use.
- Cold starts (up to several seconds) can affect latency-sensitive applications, though memory snapshots reduce this.
- SOC2 Type II is listed but may require custom agreement for enterprise compliance.
as of 2026-06-28
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Beam Cloud tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Developer
$0/mo
Ideal for
Solo developers and hobbyists experimenting with GPU workloads; includes $30 free monthly credits and 5 GPU containers.
What this tier adds
Entry-level tier with $0/mo, 5 GPU containers, 30 CPU containers, 1 seat, and 30-day log retention.
Team
$89/mo
Ideal for
Small teams needing higher concurrency (50 GPU containers) and collaboration (3 seats included, $25/additional seat).
What this tier adds
Adds 50 GPU containers (up from 5), 1000 CPU containers, 3 seats, and same $30 monthly credits.
Growth
Contact Us
Ideal for
Scaling teams and enterprises with custom GPU concurrency, unlimited CPU, unlimited seats, and 1-year log retention.
What this tier adds
Custom GPU and CPU concurrency limits, unlimited seats, 1-year log retention, and dedicated support options.
Where the pricing makes sense
The company stage and team size where Beam Cloud's pricing actually pencils out — and where peers do it cheaper.
Beam's Developer tier ($0/mo + $30 credits) is great for solo developers testing GPU workloads. The Team tier ($89/mo) beats alternatives like E2B's Team plan ($99/mo) for GPU concurrency (50 vs 25 containers) and includes $30 credits. Growth (custom) is for high-volume users. For steady-state 24/7 inference, compare hourly dedicated rates (RTX 4090 at $0.42/hr) against serverless per-second billing to decide.
Setup time & first value
How long it actually takes to get something useful out of Beam Cloud — broken out by persona, not the marketing-page minute.
For individual developers, deploying a first app takes under 5 minutes: install the Beam CLI, authenticate, write a Python function with the @app.task_queue decorator, and run `beam deploy`. For teams using CI/CD, integrating with GitHub Actions adds ~10 minutes. If you're bringing your own cloud (AWS/GCP/Azure), initial setup takes ~30 minutes to configure IAM and connect accounts.
Switching to or from Beam Cloud
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From AWS SageMaker: Redeploy your inference scripts using Beam's SDK; switch from SageMaker endpoints to Beam's QueueDepthAutoscaler for autoscaling.
- →From Modal: Reuse Dockerfiles and Python code; adjust decorators from Modal's @app.function to Beam's @task_queue and set GPU type in code.
- →From Replicate: If you have a custom model, containerize it and deploy via Beam's SDK; for public models, use Beam's supported framework examples.
- →From self-hosted Kubernetes (K8s + EC2): Deploy your existing Docker images directly via Beam SDK; no YAML or kubectl needed.
- →From E2B: Port your sandbox logic to Beam's Sandbox SDK; Beam offers wider GPU selection and memory snapshots.
- ↗To AWS Batch: Export your Beam task queue code to batch job definitions and manage EC2 or Fargate compute environments.
- ↗To Modal: Rewrite Beam decorators to Modal's serving/task syntax; both use similar Python-first patterns.
- ↗To RunPod: Move your Docker images to RunPod for serverless GPU if you prefer a different pricing model.
- ↗To self-hosted K8s: Containerize your Beam code and deploy on your own cluster with NVIDIA GPU Operator.
- ↗To E2B: If you only need sandboxes and no inference/task queues, E2B may offer simpler sandbox-only APIs.
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