Beam Cloud
Beam Cloud runs serverless GPU inference, sandboxes, and durable task queues — billed by the millisecond.
If your team already burns AWS or GCP credits and wants serverless GPU inference plus forkable sandboxes without rewriting around a new vendor's IDs, Beam is the least painful option we've looked at. The bare-metal microVM story is real, and sandbox snapshotting makes thousands of parallel RL rollouts a one-liner. Verify compliance and VPC needs before you commit — the sandboxes and secret-proxying alleviate a lot, but regulated workloads deserve a close read.
Verified 18h ago · liveness 79/100 · cite: rightaichoice.com/tools/beam-cloud
- AI developers who want a live GPU inference endpoint without managing Kubernetes or Dockerfiles
- Reinforcement learning teams forking sandbox snapshots into thousands of parallel rollouts
- Agent builders who need persistent, pausable VMs with MCP-driven control and egress rules
- Companies with existing AWS, GCP, or Azure credits that want BYOC orchestration
- Teams expecting a model marketplace or a no-code inference UI rather than a runtime
- Groups that need deep VPC peering and custom firewall rules without extra setup work
- Anyone who wants a fixed monthly compute bill with no per-millisecond usage metering
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Skip Beam if you need HIPAA or SOC2 compliance covered on the entry or Team tier, or a fully self-serve reserved multi-node cluster without a sales conversation.
Serverless GPU tasks bill per second while your code runs, so a warm endpoint left up overnight racks up GPU, CPU, and RAM charges even with no traffic.
Beam's Developer tier is free plus usage, Team is $89/mo plus usage with 3 seats, and Growth is contact-us with volume discounts above $10K/mo. Per-second serverless rates start at $0.000192/sec for an RTX 4090 and $0.000972/sec for an H100; on-demand machines run $0.44/hr to $4.11/hr with NVMe included. It sits alongside Modal, Replicate, and Baseten, and undercuts them mainly when you already hold AWS, GCP, or Azure credits to spend through BYOC.
In short
Beam Cloud — Beam Cloud runs serverless GPU inference, sandboxes, and durable task queues — billed by the millisecond. Best for AI developers who want a live GPU inference endpoint without managing Kubernetes or Dockerfiles, Reinforcement learning teams forking sandbox snapshots into thousands of parallel rollouts, Agent builders who need persistent, pausable VMs with MCP-driven control and egress rules. Plans from $0.
No head-to-head comparison for Beam Cloud yet — the alternatives below are the closest matches we list.
What's new in Beam Cloud
Checked todayAcross the latest 5 updates: 5 news mentions.
Tinker Model Pricing: What Fine-Tuning Costs in 2026
A cost analysis of fine-tuning on Tinker with worked examples and GPU rental comparisons, useful for teams weighing Beam's LoRA and QLoRA fine-tuning against other runtimes.
What Is a Container, Really? Five Years of GPU Infrastructure
Recaps Beam's GPU infrastructure evolution from ECS and Knative cold starts to a custom container runtime with FUSE lazy-loading and trustless binaries.
Hugging Face Inference Endpoints Alternatives
Compares Beam, Modal, Replicate, and Baseten as alternatives, citing cheaper GPUs, per-second billing, and no cold-start 502s.
Serverless GPU for Reinforcement Learning
Walks through fanning out thousands of RL rollouts with one .map() call, snapshotting environments, scaling to zero, and running on your own cloud.
Batch Inference on Serverless GPU
Tutorial on fanning out batch inference across GPUs with a single .map() call on Beam's serverless runtime.
What people actually say about Beam Cloud — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
30 mentions across 4 sources (Hacker News, YouTube, Bluesky, Lemmy) · researched Jul 23, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Memory snapshots deliver sub-second cold starts, 35× faster than traditional boot.
- +Supports a wide range of GPUs from RTX 4090 to H100, B200.
- +One-line hardware switching simplifies scaling between GPU types.
- +Multi-cloud deployment lets you bring your own cloud accounts.
- +Docker-in-Docker enables complex sandboxed environments.
- −Almost no community discussion about actual usage or issues.
- −HN comments treat it as an easily swapped component, not a standout.
- −No user testimonials on performance or reliability in production.
- −Pricing details for Growth tier are opaque (custom).
- −Support responsiveness untested due to lack of feedback.
- • Custom pricing for Growth tier may scale unpredictably
- • Per-second billing adds up for steady-state workloads vs. reserved instances
Viability Score
How well maintained and how widely used is Beam Cloud? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Serverless GPU inference with sub-second cold starts via a Python decorator
- OpenAI-compatible inference API serving open-source LLMs with vLLM
- Persistent, forkable microVMs that pause for free and resume in ~1s
- Memory snapshotting that restores RAM and running processes in under a second
- Sandbox snapshot_memory() to fork a running sandbox into thousands of concurrent runs
- 100+ MCP tools to create, fork, run commands, and drive a desktop
- Hostname egress rules with secret proxying so keys never enter the VM
- Durable task queues with retries, callbacks, and scheduled jobs
- Batch processing and ETL fanned out with a single .map() call
- LoRA and QLoRA fine-tuning on serverless GPUs
- One-line hardware switching across a 29-GPU catalog including H100, H200, B200, B300
- On-demand bare-metal machines at up to 5.7 GHz boost
- Multi-node GPU clusters with InfiniBand, reserved monthly or yearly
- Bring your own cloud on AWS, GCP, Azure, or bare metal
- Expiring share links and per-VM persistent IPv6 addresses
About Beam Cloud
Beam Cloud is a serverless GPU platform for engineers who want to ship AI workloads without managing Kubernetes. The workflow is a decorator: write a Python, TypeScript, or Go function, wrap it, and Beam handles provisioning, autoscaling, and scale-to-zero. It runs on Beam's own cloud or inside your AWS, GCP, Azure, or bare-metal accounts, so existing cloud credits still apply. The platform now leans hard into agent infrastructure. Persistent, forkable microVMs boot in seconds, pause and resume for free, and expose 100+ MCP tools so an agent can create VMs, run commands, and drive a desktop. Memory snapshots restore RAM and running processes in under a second, and egress hostname rules keep real secrets out of the machine by proxying keys in at request time. Three primitives anchor the product: inference endpoints serving open-source LLMs behind an OpenAI-compatible API with vLLM, durable task queues with retries and callbacks, and sandboxes for untrusted code. Sandbox snapshot_memory() forks a running sandbox into thousands of concurrent runs with streaming output — the pattern Beam pitches for RL rollouts and batch inference. Hardware switching is a one-line change across a 29-GPU catalog (RTX 4090, RTX 5090, A6000, L40S, A100, H100, H200, B200, RTX PRO 6000, B300), and batch work fans out with a single .map() call. Beam's positioning against Modal, Replicate, and Baseten rests on hardware breadth and BYOC: connect your cloud accounts, route workloads across 30+ regions, and let Beam manage orchestration while compute bills to you.
Behind the Verdict
Pick Beam when the job is messy: a batch inference fan-out, an RL training loop that needs thousands of isolated environments, or an agent that has to run untrusted code and resume where it left off. The sandbox snapshotting is the differentiator I keep coming back to. Forking a running sandbox into N concurrent runs with streaming output is exactly what reinforcement learning rollouts and batch ETL want, and not every competitor makes that a first-class call. Pass it if you want a model marketplace or a no-code inference UI. Beam is a runtime, not a catalog — you bring the model and the Dockerfile. Teams expecting a fixed monthly bill should also look elsewhere; the metering is per-millisecond and costs move with usage. The closest alternative is Modal, with Replicate and Baseten in the mix. Beam's edge is BYOC plus hardware breadth — 29 GPU types including B200 and B300, routed across 30+ regions. If your compute already lives in AWS or GCP, that's the argument. In practice, the recent agent push is where the product is heading. Persistent VMs that pause for free and wake on a request, share links that expire, and hostname egress rules that proxy secrets means you can hand an agent a machine without handing it your OpenAI key. The bare-metal 5.7 GHz boost numbers matter for latency-sensitive inference. Where it bites: compliance-gated workloads and deep VPC peering need a conversation before you start, and the Team plan's $89/mo plus usage is a floor, not a ceiling. Fine-tuning runs on the same serverless GPUs, which is convenient, but price it against Tinker-style dedicated fine-tuning if that's your main use case.
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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.
Wrap an open-source model in a Beam @endpoint decorator pointing at an H100, run beam deploy, and get an autoscaling OpenAI-compatible API in US East.
Outcome: A live inference endpoint with sub-second cold starts, billed by the millisecond and scaled to zero when idle.
Build an environment in a sandbox, call snapshot_memory(), then fork it into thousands of isolated runs with realtime streaming output.
Outcome: Thousands of parallel rollouts without re-provisioning the environment per run.
Fan out batch inference and ETL across thousands of containers with a single .map() call on a durable task queue with retries and callbacks.
Outcome: Large batch pipelines that retry on failure and scale to zero between runs.
Use Cases
- Deploy an open-source LLM behind an autoscaling OpenAI-compatible endpoint
- Fan out batch inference and ETL across thousands of containers with .map()
- Run thousands of RL rollouts by snapshotting and forking sandbox environments
- Execute LLM-generated code in isolated sandboxes with realtime streaming output
- Start GPU training runs from Python and pay only while they run
- Turn a ComfyUI workflow into an autoscaling API endpoint
- Host SDXL, Flux, and custom checkpoints behind an image generation API
- Run scheduled GPU jobs and long pipelines on durable task queues
Models Under the Hood
as of 2026-10-16
Limitations
- Beam is a serverless GPU platform billed by the millisecond for GPU compute, with usage-based pricing on tasks and sandboxes plus flat per-machine rates on on-demand hardware.
- The live pages surface SDKs for Python, TypeScript, and Go alongside an OpenAI-compatible inference API, but the vendor pages captured here do not enumerate underlying model versions, so specific model names cannot be confirmed from this evidence.
- Capabilities such as multi-node clusters, BYOC, and compliance features are described at a product level here; plan-level limits and entitlements would need confirmation on the pricing page.
as of 2026-09-29
Verification history
We have re-verified Beam Cloud 20 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 20 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Virtual Machines
$0.0306/hr
Sandboxes
$0.319/hr
Serverless GPU Tasks
$0.000192/sec (RTX 4090)
On-Demand Machines
From $0.44/hr
Clusters (Reserved)
Contact us
Storage
$0.021/GB/month over 1 TB
Bring Your Own Cloud
Management fee; compute bills to your cloud
Team
$89/mo plus usage
Ideal for
A small production team running steady GPU workloads that needs more concurrency, more seats, and log export.
What this tier adds
Adds 50 GPU and 1,000 CPU containers of concurrency, 3 included seats at $25 per extra seat, and live chat support.
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 is free plus usage, Team is $89/mo plus usage with 3 seats, and Growth is contact-us with volume discounts above $10K/mo. Per-second serverless rates start at $0.000192/sec for an RTX 4090 and $0.000972/sec for an H100; on-demand machines run $0.44/hr to $4.11/hr with NVMe included. It sits alongside Modal, Replicate, and Baseten, and undercuts them mainly when you already hold AWS, GCP, or Azure credits to spend through BYOC.
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 a developer already writing Python, first endpoint is typically a single decorator and a beam deploy command — minutes rather than days. Sandbox and task queue work starts at the SDK call. On-demand machines and BYOC are near-instant once an account is connected; multi-node InfiniBand clusters and committed spend require a sales call first, which adds days.
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 Hugging Face Inference Endpoints: port the model to a Beam @endpoint with vLLM and serve it behind the OpenAI-compatible API.
- →From a Kubernetes GPU stack: replace YAML and Dockerfiles with decorators, keeping the same cloud account via BYOC.
- →From Modal or Replicate: rewrite the function signature to Beam's decorator; hardware switching becomes a one-line gpu= change.
- ↗To Modal: rewrite decorators and re-map hardware names, losing Beam's snapshot_memory() fork model.
- ↗To self-managed Kubernetes on EC2: take the container runtime in-house and re-implement autoscaling and cold-start handling.
- ↗To a hyperscaler managed endpoint: move weights to AWS, GCP, or Azure managed inference and give up Beam's per-second billing.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Beam Cloud”, and we withheld 5: 5 could not be judged, because “Beam Cloud” is a single word that other videos use for other things. Showing the 1 we can prove is about Beam Cloud.
Official links
Tools that pair well with Beam Cloud
Common stack mates teams adopt alongside Beam Cloud, with the specific reason each pairing earns its keep.
Alternatives to Beam Cloud
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Frequently Asked Questions
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Topics
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